TripleTen Academic Catalog

• Institutional Mission and Objectives
• Instructional Location
• Language of Instruction & Required Proficiency
• Accreditation Status, Academic Credit, & Federal or State Financial Aid
• Student Conduct
• Nondiscrimination Policy
• Academic Freedom & Integrity
• Sexual Harassment
• Evaluation & Grading Policy
• Attendance Policy – All Programs
• Academic Probation and Dismissal Policies
• **Leaves of Absence **
• Student Grievance Procedures
• Career** Services**
• Money-Back Guarantee
• Student Records and Transcripts
• Student’s Right to Cancellation, Withdrawal, & Refunds
• Cancellation Policy & 100% Refund Policy
• Withdrawal & Refund Policy
• Third-party financing, loans, and payment processing
• Charges: Tuition & Fees
• Other Fees
• Description of Programs
• AI Automation | 4 Modules | 280 Hours | 14 Weeks
• Module 1: AI Tools and Application Development — 4 weeks (80 hours)
• Module 2: AI-Driven Automation and Advanced Applications — 4 weeks (80 hours)
• Module 3: Technical Integration and Solution Optimization — 4 weeks (80 hours)
• Module 4: Capstone Project — 2 weeks (40 hours)
• Module 3: Neural Networks and Advanced Techniques — 11 weeks (220 hours)
• AI and Machine Learning Program | 3 Modules | 720 Hours | 36 Weeks
• Module 1: Python for Data Analysis and Statistics — 14 weeks (280 hours)
• Module 2: Machine Learning — 8 weeks (160 hours)
• Module 3: Neural Networks, Cloud, and Production Systems — 14 weeks (280 hours)
• AI Software Engineering Program | 3 Modules | 560 Hours | 28 weeks
• Module 1: Building Websites with HTML, CSS, and JS — 12 weeks (240 hours)
• Module 2: MERN Stack Software Engineering — 10 weeks (200 hours)
• Module 3: AI-Assisted Software Engineering — 6 weeks (120 hours)
• AI Systems Engineering Program | 3 Modules | 800 Hours | 40 weeks
• Module 1: Build the AI Capability — Integration, Retrieval & Agents — 14 weeks (280 hours)
• Module 2: Evaluate & Operate — Evaluation, Reliability & Observability — 7 weeks (140 hours)
• Module 3: Ship & Secure — Deployment & Security — 9 weeks (180 hours)
• Module 4: Design, Defend & Capstone — Architecture & Communication — 10 weeks (200 hours)
• AI Product Management Program | 5 Modules | 400 Hours | 20 Weeks
• Module 1: Foundations & Discovery — 4 weeks (80 hours)
• Module 2: Strategy, Roadmapping & Rapid Prototyping — 4 weeks (80 hours)
• Module 3: AI Fundamentals, Metrics & Agile Execution — 4 weeks (80 hours)
• Module 4: Financial Modeling, Ethics & Go-to-Market — 4 weeks (80 hours)
• Module 5: Capstone Project — 4 weeks (80 hours)
• Cybersecurity Program ** | 5 Modules | 600 Hours | 30 Weeks **
• Module 1. Fundamentals — 6 weeks (120 hours)
• Module 2. Incident Response — 4 weeks (80 hours)
• Module 3. Vulnerability Management — 8 weeks (160 hours)
• Module 4. Investigating Incidents — 8 weeks (160 hours)
• Module 5. Emerging Threats — 2 weeks (40 hours)
• Module 6. CompTIA Security+ — 1 week (20 hours)
• Data Analytics Program | 4 Modules | 320 Hours | 16 weeks
• Module 1. Spreadsheets — 2 weeks (40 hours)
• Module 2. SQL and Python for Data Analysis — 6 weeks (120 hours)
• Module 3. Data Visualization and Predictive Analytics — 6 weeks (120 hours)
• Module 4. Capstone Project — 2 weeks (40 hours)
• Quality Assurance Program | 4 Modules | 400 Hours | 20 Weeks
• Module 1. QA as a Profession — 4 weeks (80 hours)
• Module 2. Testing Across Platforms — 8 weeks (160 hours)
• Module 3. Scripting and Automation — 6 weeks (120 hours)
• Module 4. Applied Testing: Final Project — 2 weeks (40 hours)
• UX/UI Design Program** | 6 Modules | 420 Hours | 21 Weeks **
• Module 1: Design Fundamentals — 2 weeks (40 hours)
• **Module 2: UX Research — 6 weeks (120 hours) **
• **Module 3: UI Design — 6 weeks (120 hours) **
• Module 4: Advanced UX/UI Integration —** 3 weeks (60 hours) **
• Module 5: Final Capstone Project — 2 weeks (40 hours)
• Module 6. Building a Portfolio — 2 weeks (40 hours)
• Business Intelligence Analytics Program | 4 Modules | 320 Hours | 16 Weeks
• Module 1. Spreadsheets — 4 weeks (80 hours)
• Module 2. Business Analytics — 4 weeks (80 hours)
• Module 3: Data Visualization and Storytelling — 6 weeks (120 hours)
• Module 4: Applying Analytics for the Final Project — 2 weeks (40 hours)
• Optional module: Basic Python — 2 weeks (40 hours)
• AI Software Engineering Program (Full-time Format) | 3 Modules | 480 Hours | 14 weeks
• Module 1: Building Websites with HTML, CSS, and JS — 4 weeks (160 hours)
• Module 2: MERN Stack Software Engineering — 5 weeks (200 hours)
• Module 3: AI-Assisted Software Engineering — 3 weeks (120 hours)
• Software Engineering Program (Part-time Format) | 6 Modules | 760 Hours | 38 Weeks
• Module 1: Advanced HTML and CSS — 9 weeks (180 hours)
• Module 2: Basic JavaScript and Working with the Browser — 6 weeks (120 hours)
• Module 3. Applied JavaScript — 6 weeks (120 hours)
• Module 4: Creating an Interface with React — 4 weeks (80 hours)
• Module 5: Back-End Basics for Software Engineers — 9 weeks (180 hours)
• Module 6. Final project. Up to 4 weeks (80 hours)
• Software Engineering Program (Full-time Format) | 6 Modules | 880 Hours | 22 Weeks
• Module 1: Advanced HTML and CSS — 4 weeks (160 hours)
• Module 2: Basic JavaScript and Working with the Browser — 3 weeks (120 hours)
• Module 3. Applied JavaScript — 3 weeks (120 hours)
• Module 4: Creating an Interface with React — 2 weeks (80 hours)
• Module 5: Back-End Basics for Software Engineers — 4 weeks (160 hours)
• Module 6. Final project — up to 4 weeks (160 hours)
• Cybersecurity (Full-time Format) | 5 modules | 600 hours | 17 weeks
• Module 1. Fundamentals — 3 weeks (120 hours)
• Learning Outcomes:
• Module 2. Incident Response — 2 weeks (80 hours)
• Learning Outcomes:
• Module 3. Vulnerability Management — 4 weeks (160 hours)
• Learning Outcomes:
• Module 4. Investigating Incidents — 4 weeks (160 hours)
• Learning Outcomes:
• Module 5. Emerging Threats — 1 week (40 hours)
• Module 6. CompTIA Security+ — 1 week (40 hours)
• Breaks — up to 2 weeks
• Data Science Program | 3 Modules | 660 Hours | 33 Weeks
• Module 1: Python and Software Engineering for Data Science — 14 weeks (280 hours)
• Module 2: Machine Learning — 8 weeks (160 hours)
• Required Disclosures applicable by state of student residence
• WYOMING
• CALIFORNIA
• UTAH
• Student Tuition Recovery Fund Disclosures

Date of publication: June 10, 2026

Institutional Mission and Objectives

Our mission is to help people build lasting careers in a world reshaped by AI. From complete beginners entering tech to working professionals advancing to senior roles, we give our students the tech and AI skills, hands-on projects, and career coaching to move into higher-paying roles and keep growing as the economy shifts. We adapt our curriculums to how our students learn, so they feel fully supported when making their big leaps.

Our objectives are to:

• Focus on employment from day one. Career and job-search readiness runs through every TripleTen program from the start, so our students graduate ready to present themselves as tech professionals.
• Provide more support to drive better outcomes. We deliver personalized support. Every student receives the guidance, tutoring, and career coaching needed to reach their goals.
• Meet our students where they are. We don’t ask our students to put their lives on hold, offering programs that take 20 hours instead of 60+. We also offer flexible terms and timing to make our programs work with our students’ lives.

Instructional Location

Instruction is provided online through asynchronous instruction. Students can log in via https://tripleten.com/sso/auth. Students are expected to have access to a computer meeting the following minimum requirements:

Processor (CPU):

Intel Core i5 or i7 / AMD Ryzen 5 or 7 / Apple M1 or M2

Memory (RAM):

8GB minimum, 16GB recommended

Storage:

256GB SSD and 20GB of free space minimum

Operating system (OS):

Windows 10 or newer, macOS 11 (Big Sur) or newer, or a Linux distribution

A computer with the above parameters met will be able to access and interact with webpages required for instruction, with a sufficiently up-to-date browser such as Firefox, Google Chrome, Safari, or Opera.

Language of Instruction & Required Proficiency

All programs are taught in English. Students must be proficient in spoken and written English. English proficiency will be determined based on a third-party assessment and may be requested as a result of interactions with TripleTen student experience or Career Team. We define English proficiency as B2 level based on Common European Framework of Reference (CEFR).

Accreditation Status, Academic Credit, & Federal or State Financial Aid

This institution is not accredited by an accrediting agency recognized by the United States Department of Education. This institution does not participate in any federal or state financial aid programs, and a student enrolled in an unaccredited institution is not eligible for federal financial aid. TripleTen does not confer formal degrees; a Certificate is not a formal degree. Further, you will not receive academic credit for your completion of a program; certificate will not entitle you to academic credit recognized by any other institution.

Student Conduct

We value the integrity of TripleTen community. The TripleTen administration sets standards at its discretion to protect the community. Students agree to comply with the Code of Conduct. In addition, students agree not to use the TripleTen Service to:

• Violate any local, state, national or international law or regulation;
• Transmit any material that is abusive, harassing, tortious, defamatory, vulgar, pornographic, obscene, libelous, invasive of another’s privacy, hateful or racially, ethnically, or otherwise objectionable;
• Stalk, harass, bully, or harm another individual;
• Transmit any unsolicited or unauthorized advertising, promotional materials, junk mail, spam, chain letters, pyramid schemes, or any other form of solicitation;
• Knowingly transmit any material that contains adware, malware, spyware, software viruses or any other computer code, files or programs designed to interrupt, destroy or limit the functionality of any computer software or hardware or telecommunications equipment;
• Defeat or interfere with any security feature of the TripleTen Service, or attempt to do so;
• Impersonate any person or entity, or otherwise misrepresent your affiliation with a person or entity;
• Interfere with or disrupt the TripleTen Service or servers or networks connected to the TripleTen Service, or disobey any requirements, procedures, policies, or regulations of networks connected to the TripleTen Service; or
• Alter or modify any content or component of the TripleTen Service, other than your User Content.

Students also agree not to use or launch any automated system, including, without limitation, “robots,” “spiders,” “offline readers” or the like, that access the TripleTen Service.

Students who have violated the Code of Conduct or the above mentioned agreements my be suspended from the Program and/or you may receive a warning from TripleTen describing the nature of a given concern according to the provisions of the Terms of Use.

Nondiscrimination Policy

This institution is committed to providing equal opportunities to all applicants to programs and to all applicants for employment. Therefore, no discrimination shall occur in any program or activity of this institution, including activities related to the solicitation of students or employees on the basis of race, color, religion, religious beliefs, national origin, sex, sexual orientation, marital status, pregnancy, age, disability, veteran’s status, or any other classification that precludes a person from consideration as an individual.

Academic Freedom & Integrity

TripleTen is committed to assuring full academic freedom to all faculty and students. TripleTen encourages coaches, tutors, and students to engage in discussion and dialog. Students and faculty members alike are encouraged to freely express views, however controversial, as long as they believe it would advance understanding in their specialized discipline or sub-disciplines.

Furthermore, academic integrity is valued at this institution. Students must ensure that all work performed and submitted (e.g., homework assignments, quizzes, exams, and projects) is entirely their own. Students will not share their work with anyone else or perform work for others, except when students are explicitly permitted to share or work with others. Cheating in any form will not be tolerated. Students must not engage in any dishonest or improper conduct to improve their results or affect the results of other students.

Sexual Harassment

This institution is committed to providing a work environment that is free of discrimination, intimidation, and harassment. In keeping with this commitment, TripleTen believes that it is necessary to affirmatively confront this subject and express strong disapproval of sexual harassment. No one associated with this institution may engage in verbal abuse of a sexual nature; use sexually degrading or graphic words to describe an individual or an individual’s body; or display sexually suggestive objects or pictures within the TripleTen service or other locations associated with this institution. Students are responsible for conducting themselves in a manner consistent with the spirit and intent of this policy.

Evaluation & Grading Policy

TripleTen uses a pass/fail grading system. Evaluation of student achievement will be based on meeting the objectives for each program. Students must achieve a “pass” rating on all projects in the program to achieve completion.

Attendance Policy – All Programs

This institution’s policy on attendance tracking for all Programs, except for programs with Full-time Format, in an asynchronous setting is based on the premise that regular interaction with the content by the student, as well as regular and substantive interaction with coaches, tutors, and fellow students, has significant value in the learning process.

The student must demonstrate regular and substantive interaction, defined as demonstrating engagement with the materials for approximately 20 hours per week, and:

• Posting to the program discussion board substantive comments relevant to the subject;
• Applying effort on the sprint content, concepts, practice materials, and project;
• Attending live career coaching sessions to successfully complete the career program portion (applicable to Money-Back Guarantee-eligible students);
• Submission of sprint projects including revisions based on reviewer feedback.

The student enrolled in program with Full-time Format, which is based on a synchronous learning model, must maintain regular and substantive interaction by engaging with the program materials for approximately 40 hours per week.

Each program is comprised of modules of instruction, and each module contains “sprints” – 1, 2 or 3-week long topics of instruction consisting of theory, practice, and final sprint project or set of projects. Each sprint is unlocked according to the predefined schedule of the program as well as student progress. Students will have access to the next sprint’s theory and practice material as soon as they have submitted their project or set of projects for the preceding sprint, however students will not be able to begin work on that sprint’s project until the previously submitted project or set of projects have been accepted with passing marks.

If students have not submitted their project for the sprint by the time that sprint is scheduled to complete, a student can take advantage of an extension, which students can use to prolong the sprint length if they need more time.

Each program has a limited number of such extension weeks, and this number varies based on the program length and difficulty.

Students may request up to 1 hour of individualized tutoring per week. Requests for 1-on-1 tutoring beyond 1 hour in a week may be denied. If a student does not cancel their scheduled tutoring at least 24 hours in advance, the session will be counted toward their weekly cap. Group events and meetings with individual Learning Coaches do not count towards this cap.

Academic Probation and Dismissal Policies

TripleTen may suspend a student’s participation in the programs under one or more of the following circumstances:

(a) if a student fails to pay their tuition fees or TripleTen is unable to process the payment;

(b) if a student breaches TripleTen Terms of Use or violates any policy in this catalog and/or the Code of Conduct;

(c) if a student fails to meet the program deadlines for submitting projects.

Continued failure to achieve satisfactory academic progress may result in dismissal from the program. The suspension will last until complete elimination of the reason or circumstances causing such suspension. If the student failed to fix the circumstances leading to its suspension within three business days following TripleTen’s notice of suspension, TripleTen may permanently terminate the student’s access to the program.

If the student wishes to appeal their suspension, the student is to submit a written request for an administrative academic review to the school administration:

TripleTen Inc.

299 South Main, Suite 1300, Salt Lake City, UT 84101

Email: complaints@tripleten.com

Violations of the Harassment or Discrimination Policy of this institution will become part of the student’s record. Depending on the severity and/or frequency of the violation(s), the institution may take disciplinary action, including administrative withdrawal from the institution.

**Leaves of Absence **

You may also request an extension of your studies. In this case, please be aware of the responsibility to meet all Program deadlines and complete the Program on time. To remain eligible for the Money-Back Guarantee, you must complete your studies within 1.5 times the standard Enrollment period. In case of your failure to return to the studies following three months of break, the access to the program may be canceled, and your return to the studies will require new enrollment into the program. Regardless of the reason for your extension, you will continue to be charged during the time of your extension until the cost of the program is fully paid. It does not affect your enrollment period for the purposes of refund calculation. To request an extension, please contact your Learning Coach.

Student Grievance Procedures

Most problems or complaints that students may have with the institution or its administrators can be resolved through a personal meeting with the student’s Learning Coach. If, however, this action does not resolve the matter to the satisfaction of the student, he/she may submit a written complaint.

To initiate a complaint, please follow these steps:

• Compose a written complaint outlining the specific nature of the issue you encountered.
• Provide a detailed description of the problem, including any relevant circumstances.
• If applicable, attach any supporting documentation that may assist in our investigation.
• Please forward your complaint to the following address:

TripleTen Inc.

299 South Main, Suite 1300

Salt Lake City, UT 84101

Email: complaints@tripleten.com

The student can expect to receive a written response within ten business days. The administration will verify that the student has made an attempt to resolve the incident or complaint. If the student has followed the above steps, administration will call a grievance session and include all of the concerned parties. Each party involved may be asked to present their version of the incident prior to all parties being present. The person against whom the complaint is filed shall receive written notice which shall include the initial report, the factual allegations, a list of witnesses and evidence. Each party involved may be asked to present their version of the incident prior to all parties being present. Administration will then issue a statement to all parties within 10 business days of the grievance meeting conclusion.

Career** Services**

Our mission is to prepare you for your job search using the highest standards and best practices in career coaching:

1. You’ll learn how to write a compelling resume and cover letter.
2. You’ll graduate with a portfolio of projects that demonstrate practical work experience.
3. We’ll help you present your best self in tech and behavioral interviews and networking events.
4. We’ll build your soft skills and make sure you feel confident in your new position.
5. We’ll guide your job search:
   • help identify relevant opportunities
   • provide feedback regarding your job search strategy
   • refine your applications
   • connect you with job leads and networking resources.

We offer our alumni post-offer support through career services, designed to help them succeed in the tech industry. Alumni can utilize three career coaching sessions per year, depending on their needs:

• If they’ve recently transitioned into a new role.
• If, at any point, they decide to return to the job search, we’ll be more than happy to set them up for success by providing career advice and reviewing their career materials.

TripleTen does offer career services and externships opportunities to its graduates, however completion of a program is not a guarantee of future employment or advancement.

While we believe that your completion of a TripleTen program will enhance your knowledge base and skill set and make you a more well-rounded employee or employment candidate, we do not and cannot make any representations regarding your future employment or advancement.

Money-Back Guarantee

If you enroll in a Program under a money-back guarantee of employment or promotion, you may receive your money back if you fail to find a new job or obtain a promotion within your current organization, subject to the conditions outlined in the Terms of Use, Money Back Guarantee Obligatory Conditions, and applicable program documents.

Please carefully read all the conditions to determine your eligibility for Money-Back Guarantee.

Please Note: Unless otherwise agreed, the Money-Back Guarantee can only be applied towards one program enrollment per individual student. In other words, you will not be eligible for the Money-Back Guarantee if you have already enrolled in another TripleTen program in the past or present.

Student Records and Transcripts

Student records for all students are kept for five years. Transcripts are kept permanently. Students may inspect and review their educational records. To do so, a student should submit a written request identifying the specific information to be reviewed. Should a student find, upon review, that records that are inaccurate or misleading, the student may request that errors be corrected. In the event that a difference of opinion exists regarding the existence of errors, a student may ask that a meeting be held to resolve the matter. Each student’s file will contain student’s records including a copy of the signed enrollment agreement, Certificate of Completion, transcript of grades earned, copies of all documents signed by the student including contract, financial ledger, refund information as applicable, complaints received from the student or student advisories related to academic progress. Transcripts will only be released to the student upon receipt of a written request. No transcript will be issued until all tuition and other fees due the institution are paid current.

Student’s Right to Cancellation, Withdrawal, & Refunds

Cancellation Policy & 100% Refund Policy

You are entitled to cancel your enrollment and receive a full (100%) refund of the Tuition Fees made by you or on your behalf, provided you cancel within 14 calendar days of the following events, whichever is most recent:

• The date you make your initial payment,
• The date you first log in to the Program (as recorded in the Platform activity logs) or first log in to any communication channel between TripleTen and you, or
• The official Program Start Date.

To cancel your enrollment or withdraw from the Program, you must complete and submit the TripleTen Withdrawal Request Form available at: TripleTen Withdrawal Request.

This form is your official notice to cancel or withdraw, and also a quick exit interview. If there’s ever any question about whether you submitted it on time, it’ll be up to you to show that you did.

Withdrawal & Refund Policy

Determining Your Withdrawal Date

Your official Withdrawal Date is the earlier of:

• The date TripleTen receives your completed Withdrawal Request Form, or
• The date you are notified by TripleTen of termination due to a violation of school policy.

How to Withdraw

To withdraw and begin the refund process, you must complete and submit the TripleTen Withdrawal Request Form found here: https://docs.tripleten.com/support/withdrawal.html.

Refund Calculation

Tuition fees, excluding non-refundable registration costs (if any), are 100% refundable for students who withdraw during the initial 14 days cancellation period detailed above. Beyond this 14-day cancellation period, TripleTen retains all Tuition Fees. You will not be eligible for any refund, and any outstanding balance remains due and owed by you in accordance with your enrollment documents.

Refund Timeline

TripleTen will issue any refund you are owed within 30 days of your Cancellation or Withdrawal Date.

Important Note:

TripleTen does not refund any additional fees and/or interest charged by the third-party financial providers.

Taking a leave of absence, being suspended, receiving an extension, or changing your Program will not pause or** **extend your enrollment period for the purpose of calculating refunds. This means that tuition payments will continue as scheduled even if any of these situations occur.

Third-party financing, loans, and payment processing

If you finance TripleTen Service through a third-party loan or financial services provider, the contracts with those providers may prevail over these Terms on payment, refund, and cancellation provisions. You should independently and voluntarily decide whether you wish to finance TripleTen Service and contract with such providers. TripleTen has no control over your agreements with those providers.

Charges: Tuition & Fees

All fees are subject to change without notice.

Please note that all numerical values presented in the Catalog are subject to rounding. This rounding process is applied consistently throughout the Catalog to ensure clarity and simplicity. As a result, some totals or calculations may reflect these rounded figures rather than exact values. If you require more precise data, please contact support@tripleten.com for additional information.

Other Fees

California Residents Only STRF Fee $0 per $1,000 of rounded institutional charges.

Payment: Billing Schedule, Processing, & Other Options

Students are responsible for paying the then-current tuition fees for the programs taken. Students pay the applicable tuition fees in advance using payment mechanisms the institution may make available. All charges and payments shall be in U.S. Dollars unless otherwise agreed by TripleTen. You may pay your tuition fee fully in advance or opt for recurring monthly payments. 3rd party loan options are also available to students to select if they wish. If you finance TripleTen Service through a third-party loan or financial services provider, the contracts with those providers may prevail over these Terms on payment, refund, and cancellation provisions. You should independently and voluntarily decide whether you wish to finance TripleTen Service and contract with such providers. TripleTen has no control over your agreements with those providers. Students are encouraged to explore all options and select the plan most appropriate to their needs.

Students who choose a payment option other than Standard Tuition, or who chose to obtain a loan or enter into an Income Share Agreement with a 3rd party lender, understand and acknowledge that additional fees and interest may be charged depending on their selection, which will increase the total amount paid for the program. If a student obtains a loan to pay for an educational program, the student will have the responsibility to repay the full amount of the loan, plus interest, less the amount of any available refund, if any. By selecting recurring monthly payments, you expressly authorize TripleTen to automatically charge the applicable recurring monthly fee to your payment method. Your first monthly payment will be charged to your payment method upon your purchase date. Your second monthly payment will be charged to your payment method in 26 days from the Program start date. For each subsequent month, the monthly fee will automatically be charged in 30 calendar days following the date of your previous payment.For the avoidance of doubt, regardless of your payment method, payment schedule, advancement in the program, suspension, or your consumption of TripleTen Service, you are responsible for the payment of your tuition fee.

Description of Programs

Enrollment period (Calendar Days)

AI Automation | 4 Modules | 280 Hours | 14 Weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1: AI Tools and Application Development — 4 weeks (80 hours)

This module lays the foundation for understanding AI’s capabilities and business impact. Participants will compare different AI tools, learn the fundamentals of prompt engineering, and apply best practices to optimize AI-generated results. They will also explore the potential and limitations of Large Language Models (LLMs) and develop AI-powered applications using no-code platforms.

Learning Outcomes:

• Understand and evaluate AI tools for business applications.
• Apply prompt engineering techniques to optimize AI outputs.
• Utilize AI tools like ChatGPT, Claude, Gemini, and Nebius AI Studio.
• Solve business cases using AI-generated responses.
• Understand the structure and limitations of LLMs.
• Implement fine-tuning techniques to improve AI-generated content.
• Develop AI-powered applications using Nebius AI Studio and no-code tools.
• Design AI-driven solutions for business applications.

Module 2: AI-Driven Automation and Advanced Applications — 4 weeks (80 hours)

This module focuses on increasing operational efficiency through AI-driven automation. Participants will analyze case studies, develop hands-on automation solutions, and explore AI bots, agents, and multimedia AI applications. Ethical considerations in AI usage will also be addressed.

Learning Outcomes:

• Identify business processes that can be automated with AI.
• Utilize tools like Zapier and HubSpot for AI-powered workflow automation.
• Implement AI-driven automation solutions to optimize business operations.
• Develop AI bots for business decision-making and task automation.
• Implement AI-generated multimedia content.
• Understand ethical concerns and responsible AI usage.

Module 3: Technical Integration and Solution Optimization — 4 weeks (80 hours)

This module covers AI solution integration into existing business systems using data pipelines, WebHooks, APIs, and Robotic Process Automation (RPA). Participants will learn how to apply AI solutions in technical environments, focusing on efficiency, error handling, and performance monitoring.

Learning Outcomes:

• Integrate AI solutions into existing business systems using APIs, data pipelines, and WebHooks
• Automate data workflows and enable event-triggered actions for seamless system communication
• Design and implement end-to-end AI integrations with real-time operational impact
• Understand the fundamentals and architecture of Robotic Process Automation (RPA)
• Identify and evaluate business processes for automation opportunities
• Build and deploy RPA bots using tools like UiPath to streamline repetitive tasks.

Module 4: Capstone Project — 2 weeks (40 hours)

In the final module, participants will apply everything they’ve learned to develop a complete AI solution to solve a real-world business problem.

Learning Outcomes:

• Identify business challenges and design AI-driven solutions.
• Develop, test, and optimize an AI prototype.
• Present findings and demonstrate the final AI application.

Module 3: Neural Networks and Advanced Techniques — 11 weeks (220 hours)

In this module students dive into advanced areas of Data Science, including Time Series, Unsupervised Learning, Natural Language Processing (NLP), and Neural Networks for applications like Computer Vision. This module equips students with the skills to handle complex data challenges and explore cutting-edge techniques.

Learning Outcomes:

• Apply linear algebra concepts like vectors, matrices, and scalar products to data science tasks.
• Build foundational algorithms, including the nearest neighbor and gradient descent, for machine learning models.
• Train regression models using time series data, leveraging trends, seasonality, and custom features.
• Analyze text with NLP techniques, including TF-IDF, BERT embeddings, and text classification models.
• Describe and implement neural networks, including fully connected and convolutional architectures (e.g., ResNet).
• Explore unsupervised learning, mastering clustering (k-means) and anomaly detection (e.g., isolation forests).

AI and Machine Learning Program | 3 Modules | 720 Hours | 36 Weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1: Python for Data Analysis and Statistics — 14 weeks (280 hours)

This module lays the foundation for a career in data by teaching Python, a versatile programming language widely used in data applications, alongside essential concepts in statistics and software engineering. The focus is on practical applications of Python for data analysis, visualization, and statistical exploration.

Learning Outcomes:

• Code in python syntax including built-in data structures (Strings, Lists, Tuples, and Dictionaries).
• Use pandas library to import and clean data.
• Generate visualizations to interpret data and present findings effectively.
• Conduct appropriate statistical data analysis such as descriptive statistics and hypothesis testing
• Use software development tools such as command line and git
• Write SQL queries, including subqueries and regular expressions, to retrieve and manipulate data.

Module 2: Machine Learning — 8 weeks (160 hours)

Machine Learning is one of the most transformative aspects of Data Science, enabling computers to predict and make inferences about the real world. This module builds on your knowledge of statistics, Python, and software engineering to create intelligent systems. You’ll explore supervised learning techniques, learn to evaluate models, and apply machine learning in real-world business contexts.

Learning Outcomes:

• Identify key machine learning concepts, including classification, regression, and the role of models and algorithms.
• Use scikit-learn to build, train, and evaluate machine learning models.
• Compare models, improve performance through parameter tuning, and select the best approach for a task.
• Apply machine learning to projects like real estate pricing and mobile plan recommendation.
• Demonstrate mastery of evaluation metrics for classification (precision, recall, F1 score, AUC-ROC) and regression (MAE, R²).
• Relate machine learning metrics to business outcomes, calculating profit, margins, and conversion rates.
• Analyze A/B test results using bootstrapping techniques.
• Explain data labeling processes and the role of assessors in supervised learning.

Module 3: Neural Networks, Cloud, and Production Systems — 14 weeks (280 hours)

In this module students dive into advanced areas of Data Science, including Time Series, Unsupervised Learning, Natural Language Processing (NLP), and Neural Networks for applications like Computer Vision. This module equips students with the skills to handle complex data challenges and explore cutting-edge techniques.

Learning Outcomes:

• Apply linear algebra concepts like vectors, matrices, and scalar products to data science tasks.
• Build foundational algorithms, including the nearest neighbor and gradient descent, for machine learning models.
• Train regression models using time series data, leveraging trends, seasonality, and custom features.
• Analyze text with NLP techniques, including TF-IDF, BERT embeddings, and text classification models.
• Describe and implement neural networks, including fully connected and convolutional architectures (e.g., ResNet).
• Explore unsupervised learning, mastering clustering (k-means) and anomaly detection (e.g., isolation forests).

AI Software Engineering Program | 3 Modules | 560 Hours | 28 weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1: Building Websites with HTML, CSS, and JS — 12 weeks (240 hours)

In this module, students build a strong foundation in frontend software engineering. They learn to create accessible, responsive, and interactive web interfaces from design specifications. Along the way, students practice professional workflows — including Git-based collaboration, documentation, and code quality standards — and begin using AI-assisted tools to support real-world frontend development.

Learning Outcomes:

• Build accessible, responsive, and functional web interfaces using HTML, CSS, JavaScript, or React (Vite), based on design specifications from Figma
• Ensure web interfaces comply with W3C standards and accessibility (A11y) guidelines
• Add interactivity to web applications through DOM manipulation and event handling
• Integrate remote APIs into frontend applications using the Fetch API and API documentation
• Read and apply technical documentation to implement third-party libraries and tools
• Write modern JavaScript (with or without TypeScript) and document code using JSDoc
• Apply modern development best practices, including formatting, linting, and structured code organization
• Use the command line to navigate the file system and run development tooling
• Use AI-assisted tools to support frontend development tasks while validating generated output
• Collaborate with other developers using Git and GitHub
• Communicate technical decisions clearly and take ownership of assigned work

Module 2: MERN Stack Software Engineering — 10 weeks (200 hours)

In this module, students move beyond the frontend to build and deploy production-ready full-stack applications. They design secure backend APIs with Express and MongoDB, integrate them with React frontends, and apply best practices for validation, error handling, testing, and security. Students also learn how to containerize applications and deploy them to cloud environments, while using AI-assisted tools and collaborative workflows to support real-world, end-to-end software development.

Learning Outcomes:

• Design and implement RESTful JSON APIs using Express.js and MongoDB
• Model, persist, and manage data using MongoDB and Mongoose
• Validate incoming requests and enforce backend constraints
• Implement centralized error handling, logging, and appropriate HTTP status codes
• Inspect and manage MongoDB databases using MongoDB tooling
• Test API endpoints using tools such as Postman
• Build React-based frontends that integrate with backend APIs
• Manage state, routing, forms, and conditional rendering in frontend applications
• Apply client-side accessibility and security best practices
• Ensure code quality through automated testing and continuous integration using GitHub Actions
• Containerize full-stack applications using Docker and Docker Compose
• Deploy applications to production environments, including AWS EC2
• Implement continuous deployment pipelines
• Use AI-assisted coding tools to support development, testing, and documentation
• Collaborate using Git branching, merging, and pull requests
• Adapt to evolving tools, frameworks, and project requirements

Module 3: AI-Assisted Software Engineering — 6 weeks (120 hours)

In this module, students bring everything together to build secure, production-ready full-stack applications using TypeScript, with AI integrated throughout the development workflow. They apply AI-assisted approaches to planning, coding, testing, debugging, and documentation, while maintaining strong engineering judgment and security practices. The module also focuses on career readiness — helping students strengthen problem-solving skills, prepare for technical interviews, and present their work through a professional portfolio as they transition into engineering roles.

Learning Outcomes:

• Apply AI-assisted workflows across the software development lifecycle, while maintaining engineering judgment and accountability
• Build type-safe full-stack applications using TypeScript across frontend, backend, and data models
• Design and implement secure applications and APIs, including authentication, authorization, and secure external API integrations
• Conduct security testing and structured debugging of full-stack applications
• Deploy, document, and polish production-ready applications
• Design and deploy a professional portfolio website showcasing technical projects
• Practice data structures and algorithms to strengthen problem-solving and technical interview readiness
• Communicate technical work effectively to both technical and non-technical audiences
• Demonstrate ownership, continuous improvement, and readiness to work in professional engineering teams

AI Systems Engineering Program | 3 Modules | 800 Hours | 40 weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1: Build the AI Capability — Integration, Retrieval & Agents — 14 weeks (280 hours)

This module covers the core of building AI behavior: standing up a working toolchain, integrating large language models into applications, building retrieval-augmented generation (RAG) over real data, and constructing agentic, tool-using workflows. Students build to a reference architecture and finish with an agent that can answer from a real corpus and take actions behind a human-approval gate.

Learning Outcomes:

• Stand up a working AI toolchain (Git/GitHub, containers, an AI coding assistant) and ship a containerized service that talks to a model
• Translate a business problem into an initial AI system design, choosing deliberately between an LLM, a retrieval layer, an agent, or plain code (full treatment in Module 4)
• Design LLM integration architecture: gateway/routing, multi-provider fallback, token/latency/cost control, streaming, retries, and structured outputs
• Treat prompt engineering as system design, and reason about when not to use AI
• Design production-grade RAG: chunking, embeddings, hybrid retrieval, reranking, grounding, freshness, and cost control
• Select and operate a vector store based on access patterns
• Evaluate retrieval quality as a precursor to system-level evaluation
• Design multi-step, tool-using agent workflows: tool/function calling, state, orchestration, error handling, and human-in-the-loop checkpoints
• Decide when an agent is the right tool and when it is unnecessary complexity
• Scaffold integration, RAG, and agent code with AI coding assistants, then review and correct their output — spotting hallucinated APIs, logic bugs, and inefficiencies before they reach the system

Module 2: Evaluate & Operate — Evaluation, Reliability & Observability — 7 weeks (140 hours)

This module covers proving the system works and keeping watch over it in production — the skill that most separates a real engineer from someone who only built a demo. Students build repeatable evaluation, instrument observability, and practice incident response.

Learning Outcomes:

• Build evaluation pipelines: define metrics, construct eval sets, run offline and online evaluations, and improve quality deliberately
• Design reliability patterns: guardrails, shadow mode, canary releases, fallbacks, and latency/cost budgets
• Instrument AI-specific observability and run incident response for failure modes such as hallucination, regression, and drift
• Use AI coding assistants to draft eval cases, instrumentation, and postmortems, and correct their output — catching misleading metrics and wrong root-cause claims

Module 3: Ship & Secure — Deployment & Security — 9 weeks (180 hours)

This module covers deploying AI systems as production services and securing them. Students expose capabilities through APIs, containerize and deploy with Docker and Kubernetes, provision with Terraform, build CI/CD on AWS, and harden the AI layer — including prompt injection and data exfiltration — with responsible-AI and compliance practices relevant to finance, healthcare, and legal.

Learning Outcomes:

• Expose AI capabilities through clean APIs, choosing synchronous or streaming/asynchronous patterns by tradeoff
• Containerize and deploy with Docker and Kubernetes, provision with Terraform, and ship through CI/CD
• Architect a cloud topology sized for AI workloads (compute, networking, scaling, environments)
• Reason about serving, caching, and cost-aware scaling for AI
• Apply identity and access management (OAuth 2.0, OIDC) and secrets management at a working level (managed providers)
• Defend the AI layer against prompt injection, data exfiltration, and unsafe output handling, applying the OWASP LLM Top 10 and guardrails
• Apply responsible-AI practices (bias/safety checks, audit logging) with awareness of GDPR, SOC 2, and HIPAA
• Generate IaC, deployment manifests, and API code with AI coding assistants, then review their output for security holes and misconfigurations — the highest-stakes correction

Module 4: Design, Defend & Capstone — Architecture & Communication — 10 weeks (200 hours)

This module covers designing an AI system from scratch, defending the tradeoffs, and the cumulative capstone. Students translate requirements into architecture, document decisions with the C4 model and Architecture Decision Records, and present and defend a complete system.

Learning Outcomes:

• Translate requirements into a system architecture and defend the tradeoffs
• Decide between monolithic and service-based architectures deliberately, recognizing when an AI system does not need the complexity
• Reason quantitatively about scalability, latency, throughput, and bottlenecks
• Apply a build-vs-adopt framework to architecture and tooling decisions, including recommending a team AI-tooling setup weighed on cost, data governance, and fit
• Document and communicate decisions using the C4 model and Architecture Decision Records, and run a structured architecture review
• Drive AI coding assistants to draft ADRs and architecture proposals, and correct their reasoning — then design, build, and present a cumulative capstone system (live, evaluated, secured, documented) to both technical and non-technical audiences

AI Product Management Program | 5 Modules | 400 Hours | 20 Weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1: Foundations & Discovery — 4 weeks (80 hours)

This module introduces core product management responsibilities, the AI industry landscape, foundational prompting skills, and user research methods for identifying product opportunities.

Learning Outcomes:

• Describe the role of a product manager and understand business models
• Set up an AI toolkit and apply core prompting techniques
• Explain the product development lifecycle, key stakeholders, and agile fundamentals
• Define key product metrics and goals as a foundation for data-driven decision making
• Conduct AI-assisted user research and create personas and journey maps
• Apply product discovery frameworks to evaluate product opportunities

Module 2: Strategy, Roadmapping & Rapid Prototyping — 4 weeks (80 hours)

This module covers strategic product planning, competitive analysis, roadmap communication, and hands-on prototyping using AI-powered tools.

Learning Outcomes:

• Conduct competitive analysis and market sizing using structured frameworks
• Use AI tools for strategic analysis, competitive intelligence, and data synthesis
• Build and prioritize product roadmaps and communicate strategy to stakeholders
• Build working prototypes using AI-powered coding tools

Module 3: AI Fundamentals, Metrics & Agile Execution — 4 weeks (80 hours)

This module covers how AI works at a conceptual level, data-driven decision making, and product metrics within an agile execution framework.

Learning Outcomes:

• Understand data foundations for AI, including training data, data quality, and bias
• Define and track product metrics to measure product health and growth
• Apply AI-native UX principle,s including trust, transparency, and graceful degradation
• Write product briefs, user stories, and product requirements

Module 4: Financial Modeling, Ethics & Go-to-Market — 4 weeks (80 hours)

This module explores financial modeling, AI ethics, product lifecycle management, monetization strategies, and go-to-market planning.

Learning Outcomes:

• Build a product financial model and understand unit economics
• Manage product lifecycle stages, including defining kill criteria
• Develop monetization and go-to-market strategies
• Use AI agents for PM workflows and build an AI tool stack
• Communicate strategy and negotiate scope with cross-functional stakeholders

Module 5: Capstone Project — 4 weeks (80 hours)

In this final module, students apply everything they have learned to build a comprehensive case study, develop a professional portfolio, and prepare for the PM job market.

Learning Outcomes:

• Structure and finalize an end-to-end case study for portfolio and interviews
• Deliver a product strategy presentation with a demo

Cybersecurity Program ** | 5 Modules | 600 Hours | 30 Weeks **

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1. Fundamentals — 6 weeks (120 hours)

This module provides a comprehensive introduction to cybersecurity fundamentals, focusing on network configuration, threat detection, and the application of cybersecurity frameworks. Students will develop essential skills in network security, threat analysis, and risk assessment, ensuring a solid foundation in protecting organizational assets.

Learning Outcomes:

• Recognize and evaluate critical networking components, including topologies, IP addresses, and protocols, to understand how they contribute to cybersecurity.
• Analyze network traffic using monitoring tools to distinguish between anomalies caused by misconfigurations or malicious activities, and apply appropriate security measures.
• Conduct scans and enumerate device clusters within a company to identify assets and implement defense mechanisms that protect device identities from attackers.
• Apply cybersecurity frameworks, such as NIST and ISO to assess cybersecurity risk profiles and improve business continuity.
• Develop and implement effective incident response strategies to detect, contain, and recover from cybersecurity incidents, ensuring continuous improvement and risk reduction.

Module 2. Incident Response — 4 weeks (80 hours)

This module focuses on building expertise in identifying, responding to, and mitigating cybersecurity incidents. Students will learn how to model organizational threats, develop playbooks for common incident scenarios, and apply best practices to enhance incident response capabilities.

Learning Outcomes:

• Threat model an organization and identify common threats and attack vectors.
• Prioritize mitigations to reduce attack surface and minimize risk.
• Develop playbooks for standard incident response scenarios: phishing, malware, data breach, and DDoS.
• Implement incident response procedures that effectively detect, respond to, and recover from security incidents.

Module 3. Vulnerability Management — 8 weeks (160 hours)

This module focuses on building expertise in identifying, assessing, and remediating vulnerabilities to protect organizational assets. Students will develop skills in network hardening, vulnerability assessment, exploitation, and remediation, preparing them for real-world cybersecurity challenges.

Learning Outcomes:

• Harden a network, establish a Security Network Operations Center (SNOC), and automate repetitive tasks to improve operational efficiency.
• Compare and test network architecture choices and evaluate network changes without disrupting production services.
• Strengthen authentication practices and identify defensive gaps according to threat models.
• Gain foundational knowledge of CompTIA Security+ exam domains and strategies, utilizing Anki flashcards for long-term retention.
• Conduct vulnerability assessments by inventorying assets, scanning, and interpreting results to assess and remediate high-risk vulnerabilities.
• Perform penetration tests to exploit vulnerabilities on servers and verify findings.
• Present findings using industry-standard report formats, plan remediation strategies, and execute the necessary fixes.

Module 4. Investigating Incidents — 8 weeks (160 hours)

This module focuses on strengthening the ability to identify, investigate, and respond to cybersecurity incidents. Students will develop expertise in securing networks, detecting complex attacks, and applying effective incident response strategies to mitigate risks.

Learning Outcomes:

• Secure physical, virtual, and cloud networks, safely extending corporate networks while aligning security practices with compliance goals.
• Validate, aggregate, and maintain critical data sources to enable continuous monitoring and improve incident detection capabilities.
• Investigate anomalous file changes, analyze unusual network traffic, and focus on suspicious login activity to identify security threats.
• Detect common attacks using evidence sources available to security analysts and create precise search queries to pinpoint attack indicators.
• Distinguish between normal and malicious activity to enhance threat detection accuracy.
• Ask the right questions and pivot between evidence sources to guide investigation efforts.
• Tailor mitigation and remediation strategies to reduce attack vectors uncovered during incidents.

Module 5. Emerging Threats — 2 weeks (40 hours)

This module prepares students to use the reference as a guiding structure for their learning and practice. It helps them understand how the frameworks, domains, and exam-style logic of Security+ translate into real-world AI security contexts. By aligning theory with practical application, learners build the ability to evaluate AI systems through a structured, standards-based lens.

Learning Outcomes:

By the end of this sprint, learners will be able to:

• Apply the reference framework as a structured approach to analyze and secure AI systems.
• Identify and evaluate key vulnerabilities in AI-integrated environments, including prompt injection and LLM-based attacks.
• Explain how emerging protocols (such as MCP and A2A) contribute to secure AI operations.
• Assess real-world AI deployments within an organization using risk and resilience frameworks.
• Design and propose mitigation strategies to defend against AI-driven threats.
• Integrate concepts from traditional cybersecurity (e.g., Security+ domains) into modern AI security contexts.
• Demonstrate readiness to apply structured, standards-based thinking to new and evolving AI security challenges.

Module 6. CompTIA Security+ — 1 week (20 hours)

This module prepares students for the CompTIA Security+ certification exam, focusing on advanced exam topics and hands-on practice to ensure readiness.

Learning Outcomes:

• Focus on advanced CompTIA Security+ exam topics and key domains.
• Complete hands-on exercises to reinforce understanding of cybersecurity concepts.
• Take practice exams to assess knowledge and readiness.
• Confidently prepare for and pass the CompTIA Security+ certification exam.

Data Analytics Program | 4 Modules | 320 Hours | 16 weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1. Spreadsheets — 2 weeks (40 hours)

This module introduces you to the foundations of business data analysis using spreadsheets. You’ll learn how to clean, structure, and analyze datasets, apply descriptive statistics, and calculate core business metrics. The focus is on building analytical thinking and understanding how business questions translate into measurable definitions.

Learning Outcomes:

• Clean and prepare raw datasets using spreadsheet tools.
• Apply formulas and functions to manipulate and transform data.
• Use pivot tables to aggregate and summarize business metrics.
• Calculate key performance indicators such as revenue, retention, churn, and conversion rates.
• Create simple visualizations to communicate analytical findings.
• Document metric definitions and analytical assumptions clearly.

Module 2. SQL and Python for Data Analysis — 6 weeks (120 hours)

This module develops your ability to work with structured data in relational databases and automate analysis using Python. You’ll learn how to retrieve, transform, and analyze data efficiently using SQL, and how to use Python for data cleaning, exploratory analysis, and workflow automation. The module emphasizes practical problem-solving using real datasets.

Learning Outcomes:

• Write SQL queries to retrieve, filter, and sort data.
• Use joins to combine multiple tables and analyze relational datasets.
• Apply aggregate functions and grouping techniques to summarize data.
• Design and interpret basic relational data models.
• Use Python and pandas to clean and transform datasets.
• Perform exploratory data analysis (EDA) to identify patterns and anomalies.
• Connect Python to SQL databases for integrated workflows.
• Manage analytical projects using Git and GitHub.

Module 3. Data Visualization and Predictive Analytics — 6 weeks (120 hours)

This module focuses on building interactive dashboards and introducing predictive analytics concepts. You’ll learn how to model data for reporting, create stakeholder-ready visualizations, and apply basic machine learning techniques to support business decision-making. The emphasis is on combining technical analysis with clear communication.

Learning Outcomes:

• Build interactive dashboards using Power BI.
• Design data models to support scalable reporting.
• Create calculated measures and performance metrics.
• Apply best practices in data visualization and storytelling.
• Understand supervised machine learning concepts.
• Build and evaluate basic regression and classification models in Python.
• Interpret model outputs using business context.
• Present analytical findings clearly to executive audiences.

Module 4. Capstone Project — 2 weeks (40 hours)

This module is dedicated to completing an independent, end-to-end analytics project. You’ll define a business problem, collect and integrate data from multiple sources, perform analysis, and deliver stakeholder-ready insights supported by a documented workflow.

Quality Assurance Program | 4 Modules | 400 Hours | 20 Weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1. QA as a Profession — 4 weeks (80 hours)

This module introduces the foundational concepts of QA, provides an overview of the software development lifecycle (SDLC), and covers essential techniques for designing and documenting tests. Building on these core skills, students will develop a deeper understanding of QA processes throughout subsequent sprints.

Learning Outcomes:

• Describe the Software Development Life Cycle (SDLC) and the role of QA engineers within it.
• Identify different types of testing (e.g., functional, performance, regression).
• Perform exploratory testing to identify defects and verify software functionality.
• Analyze and decompose product requirements to ensure coverage in test design.
• Design and implement effective test cases and scenarios.
• Read, create, and maintain detailed test documentation for tracking and reporting purposes.

Module 2. Testing Across Platforms — 8 weeks (160 hours)

In this module, students will build on foundational QA knowledge and gain hands-on experience testing web apps, mobile apps, web APIs, and SQL databases. Each sprint focuses on a specific platform, providing the necessary tools and techniques to understand the architecture and testing requirements of that platform.

Learning Outcomes:

• Illustrate web application architecture and client-server interactions.
• Utilize DevTools to inspect and analyze web pages.
• Use Charles to monitor and analyze HTTP requests.
• Demonstrate core Postman functionality for API testing.
• Summarize API architecture, protocols, and technologies.
• Validate and test APIs effectively using Postman.
• Identify database testing principles.
• Write and execute SQL queries to extract data.
• Apply various types of joins to work with databases.
• Explore mobile testing techniques using Android Studio.
• Identify and resolve bugs in mobile applications.
• Set up and remove logs for effective troubleshooting.

Module 3. Scripting and Automation — 6 weeks (120 hours)

In this module, students will gain hands-on experience with automation frameworks like Selenium WebDriver. You’ll also develop foundational programming skills in Python, which are essential for building and maintaining automated tests efficiently.

Learning Outcomes:

• Interpret fundamental programming logic (e.g., statements, loops, conditions).
• Illustrate essential data structures (lists, dictionaries, tuples).
• Write test scripts to automate repetitive QA tasks.
• Define the concepts of automation and the testing pyramid.
• Apply Selenium WebDriver to automate and test web applications effectively.

Module 4. Applied Testing: Final Project — 2 weeks (40 hours)

The final project gives students the opportunity to put all of learning into practice as they test a mobile app, a web app, and an API. Students will design and perform tests, then submit bug reports with their findings.

UX/UI Design Program** | 6 Modules | 420 Hours | 21 Weeks **

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1: Design Fundamentals — 2 weeks (40 hours)

This immersive sprint will introduce you to the UX/UI design process and fundamental principles of design thinking while you explore Figma’s foundational tools. You’ll establish a solid understanding of how each phase of the design process contributes to a complete, user-centered project. Additionally, you’ll explore how to create inclusive experiences that prioritize user needs using principles of accessibility, ethical design, and inclusivity.

Learning Outcomes:

• Identify the phases of the design process and their role in achieving successful design solutions.
• Demonstrate foundational Figma skills by focusing on key tools and interface navigation.
• Evaluate the importance of accessibility, ethical design, and inclusivity for user-centered design.

**Module 2: UX Research — 6 weeks (120 hours) **

This module provides students with the foundational skills to conduct effective user research and apply insights to design user-centered solutions. Students will explore methods such as interviews, usability testing, and surveys, and use findings to create user personas, journey maps, user flows, and information architectures that address user needs and goals.

Learning Outcomes:

• Conduct user research using methods such as interviews, usability testing, and surveys.
• Develop user personas and journey maps to represent user goals, behaviors, and pain points.
• Design user flows and organize content into intuitive information architectures.
• Apply research findings to support and inform design decisions.

**Module 3: UI Design — 6 weeks (120 hours) **

This module focuses on the core principles and techniques of user interface design, from wireframing and prototyping to creating design systems. Students will gain hands-on experience with industry-standard tools like Figma, mastering the skills needed to craft visually engaging, user-friendly, and responsive digital interfaces.

Learning Outcomes:

• Create detailed wireframes, prototypes, and interactive interfaces for usability testing.
• Apply foundational UI principles, including grids, layouts, typography, and color theory, to develop professional designs.
• Design responsive layouts and interactive components adaptable across various screen sizes.
• Develop and document design systems to ensure consistency and scalability in digital products.

Module 4: Advanced UX/UI Integration —** 3 weeks (60 hours) **

This module focuses on integrating advanced branding, motion design, and cross-functional collaboration techniques into UX/UI workflows. Students will learn to create cohesive brand identities, enhance user engagement through motion design, and navigate real-world design handoff and team collaboration processes.

Learning Outcomes:

• Develop a brand language guide and explore motion design principles to create engaging user experiences.
• Apply responsive design principles to adapt designs for various platforms and devices.
• Collaborate effectively with cross-functional teams using common workflows and methodologies.
• Organize design files and document decisions for seamless design handoff.

Module 5: Final Capstone Project — 2 weeks (40 hours)

In this culminating sprint, students apply the knowledge and skills acquired throughout the program to a comprehensive capstone project. Students engage in an end-to-end design process, starting with user research and progressing through ideation, prototyping, and final design presentations. This hands-on experience will simulate real-world design challenges and provide you with invaluable practice in tackling complex design problems.

Learning Outcomes:

• Conduct user research and derive insightsIdeate and prototype design solutionsIterate designs based on feedback and usability testing.
• Present and defend final designs to peers and professionals.
• Develop a portfolio-ready design case study.

Module 6. Building a Portfolio — 2 weeks (40 hours)

In this final sprint, you’ll design, build, and publish your professional portfolio. You’ll define your personal brand and set goals for your site. Then, you’ll plan your site structure, craft case studies, and showcase your work with storytelling and strong visuals. Finally, you’ll publish your portfolio using platforms like Squarespace or Webflow. This sprint brings together creativity, technical skills, and real-world publishing experience to help you confidently launch your portfolio and take the next step in your UX/UI career.

Learning Outcomes:

• Define a personal brand identity and organize a portfolio site structure that aligns with career goals.
• Develop clear and compelling UX/UI case studies that showcase design thinking, problem-solving, and outcomes.
• Build and style a functional, responsive portfolio website using a professional website builder.

Business Intelligence Analytics Program | 4 Modules | 320 Hours | 16 Weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1. Spreadsheets — 4 weeks (80 hours)

This module introduces students to the fundamentals of business analytics using spreadsheets, focusing on industry best practices for metric selection, data analysis, and efficient use of spreadsheet tools.

Learning Outcomes:

• Define key terms of the BACCM framework and apply them to business analysis practices.
• Plan and effectively communicate business analysis information, ensuring clarity and relevance to stakeholders.
• Perform advanced data cleaning techniques to enhance data integrity and accuracy, establishing a strong foundation for high-quality analysis.
• Use pivot tables in Google Sheets to efficiently summarize, analyze, and interpret complex datasets.
• Apply best practices in spreadsheet design, structure, and management to create clear, organized, and highly effective spreadsheets.
• Create various types of graphs and charts to visualize data, conduct in-depth exploratory analysis, and effectively communicate actionable insights derived from data.

Module 2. Business Analytics — 4 weeks (80 hours)

This module introduces students to the fundamentals of business analytics using SQL databases, emphasizing the practical application of data retrieval, filtering, and advanced querying techniques.

Learning Outcomes:

• Write SQL queries of varying complexities.
• Apply regular expressions for data filtering and pattern matching.
• Use subqueries to extract specific information from large datasets.
• Navigate SQL documentation for applying database structures.
• Utilize aggregate functions to summarize and analyze data.
• Merge tables to combine datasets and create comprehensive analyses.
• Identify and analyze financial metrics (revenue, sales, costs, profit margin, ROI) to uncover improvement opportunities.
• Build financial models in spreadsheets to calculate profitability across scenarios.
• Construct marketing and product funnels in spreadsheets to track performance.
• Perform cohort analyses in spreadsheets to measure retention and churn rates.
• Calculate user activity metrics (user sessions, LTV, CAC) to prioritize resources and uncover improvement opportunities.

Module 3: Data Visualization and Storytelling — 6 weeks (120 hours)

In this module, students will focus on mastering data visualization techniques using Tableau and Power BI to create interactive, compelling visualizations that effectively communicate insights to stakeholders. Additionally, students will explore best practices in storytelling to construct persuasive narratives that translate complex data into actionable insights.

Learning Outcomes:

• Describe Tableau’s ability to integrate with various data formats such as Excel, text files, JSON, and geospatial files.
• Select and generate appropriate chart types based on the data and analysis needs.
• Select/Identify when Tableau is the preferred tool over Excel for data analysis and visualization.
• Create relationships between datasets in Tableau to support more in-depth data exploration and analysis.
• Apply data type inference and adjust data types in Tableau for accurate and effective data representation.
• Navigate Tableau workbooks efficiently, including data source screens, worksheets, dashboards, and stories.
• Differentiate between measures and dimensions and understand their role in Tableau for data analysis and visualization.
• Design multiple worksheets in Tableau to explore and analyze different aspects of the data, organizing them within a workbook.
• Build interactive dashboards in Tableau, combining various visualizations to provide a holistic view of data.
• Develop a structured framework for storytelling, focusing on clear purpose, logical structure, audience tailoring, and summarizing key points.
• Recognize when Power BI is preferred over Tableau and other tools.

Module 4: Applying Analytics for the Final Project — 2 weeks (40 hours)

In this module, students will apply their data analytics skills to tackle real-world project. The focus is on integrating various techniques and tools to solve complex problems. The final project serves as the culmination of the program, allowing students to demonstrate their mastery of analytics and showcase their ability to apply key concepts effectively.

Optional module: Basic Python — 2 weeks (40 hours)

This sprint offers free additional material for students interested in exploring fundamental Python concepts.

Learning Outcomes:

• Store data in variables and apply it for later use.
• Perform numeric calculations using Python.
• Work with text data and manipulate it effectively.
• Create and utilize data structures like lists and tuples.
• Write code with decision-making capabilities (e.g., conditional statements).

AI Software Engineering Program (Full-time Format) | 3 Modules | 480 Hours | 14 weeks

Students are expected to engage with the material at least 40 hours per week to maintain satisfactory progress through the program.

Module 1: Building Websites with HTML, CSS, and JS — 4 weeks (160 hours)

In this module, students build a strong foundation in frontend software engineering. They learn to create accessible, responsive, and interactive web interfaces from design specifications. Along the way, students practice professional workflows — including Git-based collaboration, documentation, and code quality standards — and begin using AI-assisted tools to support real-world frontend development.

Learning Outcomes:

• Build accessible, responsive, and functional web interfaces using HTML, CSS, JavaScript, or React (Vite), based on design specifications from Figma
• Ensure web interfaces comply with W3C standards and accessibility (A11y) guidelines
• Add interactivity to web applications through DOM manipulation and event handling
• Integrate remote APIs into frontend applications using the Fetch API and API documentation
• Read and apply technical documentation to implement third-party libraries and tools
• Write modern JavaScript (with or without TypeScript) and document code using JSDoc
• Apply modern development best practices, including formatting, linting, and structured code organization
• Use the command line to navigate the file system and run development tooling
• Use AI-assisted tools to support frontend development tasks while validating generated output
• Collaborate with other developers using Git and GitHub
• Communicate technical decisions clearly and take ownership of assigned work

Module 2: MERN Stack Software Engineering — 5 weeks (200 hours)

In this module, students move beyond the frontend to build and deploy production-ready full-stack applications. They design secure backend APIs with Express and MongoDB, integrate them with React frontends, and apply best practices for validation, error handling, testing, and security. Students also learn how to containerize applications and deploy them to cloud environments, while using AI-assisted tools and collaborative workflows to support real-world, end-to-end software development.

Learning Outcomes:

• Design and implement RESTful JSON APIs using Express.js and MongoDB
• Model, persist, and manage data using MongoDB and Mongoose
• Validate incoming requests and enforce backend constraints
• Implement centralized error handling, logging, and appropriate HTTP status codes
• Inspect and manage MongoDB databases using MongoDB tooling
• Test API endpoints using tools such as Postman
• Build React-based frontends that integrate with backend APIs
• Manage state, routing, forms, and conditional rendering in frontend applications
• Apply client-side accessibility and security best practices
• Ensure code quality through automated testing and continuous integration using GitHub Actions
• Containerize full-stack applications using Docker and Docker Compose
• Deploy applications to production environments, including AWS EC2
• Implement continuous deployment pipelines
• Use AI-assisted coding tools to support development, testing, and documentation
• Collaborate using Git branching, merging, and pull requests
• Adapt to evolving tools, frameworks, and project requirements

Module 3: AI-Assisted Software Engineering — 3 weeks (120 hours)

In this module, students bring everything together to build secure, production-ready full-stack applications using TypeScript, with AI integrated throughout the development workflow. They apply AI-assisted approaches to planning, coding, testing, debugging, and documentation, while maintaining strong engineering judgment and security practices. The module also focuses on career readiness — helping students strengthen problem-solving skills and present their work through a professional portfolio as they transition into engineering roles.

Learning Outcomes:

• Apply AI-assisted workflows across the software development lifecycle, while maintaining engineering judgment and accountability
• Build type-safe full-stack applications using TypeScript across frontend, backend, and data models
• Design and implement secure applications and APIs, including authentication, authorization, and secure external API integrations
• Conduct security testing and structured debugging of full-stack applications
• Deploy, document, and polish production-ready applications
• Design and deploy a professional portfolio website showcasing technical projects
• Practice data structures and algorithms to strengthen problem-solving and technical interview readiness
• Communicate technical work effectively to both technical and non-technical audiences
• Demonstrate ownership, continuous improvement, and readiness to work in professional engineering teams

Breaks — up to 2 weeks

Up to 2 weeks of breaks to catch up with the program deadlines.

Software Engineering Program (Part-time Format) | 6 Modules | 760 Hours | 38 Weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1: Advanced HTML and CSS — 9 weeks (180 hours)

This module provides an in-depth exploration of HTML and CSS, focusing on advanced techniques for creating responsive and well-structured web pages. Students will learn to manage complex layouts, maintain an organized codebase, and develop adaptable designs that deliver seamless user experiences across various devices.

Learning Outcomes:

• Control a webpage layout with Flexbox.
• Display and position elements using position properties.
• Structure the code using the BEM methodology.
• Navigate code changes using the version control system Git.
• Manipulate elements visually using CSS transforms and keyframes.
• Create markup for user input forms.
• Organize file project structure according to BEM rules.
• Navigate and use the command line for Git.
• Write custom CSS for different screen sizes using media queries.
• Build the layout of a webpage using grid columns and rows.
• Collaborate with other developers using Git branches and pull requests.

Module 2: Basic JavaScript and Working with the Browser — 6 weeks (120 hours)

This module marks the start of hands-on programming. Students will dive into JavaScript fundamentals and apply their knowledge to develop a fully functional, interactive website.

Learning Outcomes:

• Utilize basic programming concepts in JavaScript.
• Manipulate HTML and CSS using JavaScript and the DOM.
• Debug the JavaScript code using the developer tools and console.
• Resolve Git merge conflicts with commands for managing code history.
• Manipulate different data structures in JavaScript using ES6 capabilities.
• Create HTML content with JavaScript and the DOM methods.
• Create event listeners that handle browser events.
• Manipulate objects in JavaScript.
• Control keyboard and mouse events.
• Access form elements and validate user input.

Module 3. Applied JavaScript — 6 weeks (120 hours)

This module expands on foundational JavaScript knowledge, introducing advanced programming concepts and techniques essential for a career in software engineering. Students will deepen their understanding of JavaScript to develop more complex and efficient applications.

Learning Outcomes:

• Implement object-oriented features in JavaScript using OOP principles.
• Modify markup content inside classes.
• Split JavaScript code into usable modules.
• Manage objects and arrays using destructuring syntax.
• Organize code according to the object-oriented design.
• Configure webpack for automating build tasks.
• Write asynchronous JavaScript code with promises.
• Make HTTP requests to a server using the Fetch API.
• Update markup using fetched data.

Module 4: Creating an Interface with React — 4 weeks (80 hours)

This module introduces students to the React library and its ecosystem, focusing on component-based design. Students will learn to create reusable components, streamline the development of complex interfaces, and write maintainable, scalable code efficiently.

Learning Outcomes:

• Create user interface elements using JSX.
• Create reusable React components.
• Manage the state and lifecycle of components using React Hooks.
• Add and access routes to a React application using React Router.
• Pass data between React components using context.
• Use controlled components to control form elements.

Module 5: Back-End Basics for Software Engineers — 9 weeks (180 hours)

Modern web development extends beyond the front end, relying on back-end systems to store, retrieve, and manage user data while enabling advanced functionality. In this module, students will explore core back-end development concepts and gain exposure to essential engineering principles relevant to technical interviews.

Learning Outcomes:

• Build a server and use it to serve data and responses to client requests.
• Store, manipulate, and retrieve data on a server using MongoDB and Mongoose.
• Determine different error types by name and class of the errors.
• Summarize and implement back-end authentication and authorization.
• Use regular expressions to validate and extract string data.
• Identify the complexity of an algorithm.
• Implement front-end authorization and authentication.
• Protect routes in a React application using protected routes and user tokens.
• Describe approaches to preventing web application security vulnerabilities.
• Write basic automated tests with Jest.
• Deploy a web application to Google Cloud.

Module 6. Final project. Up to 4 weeks (80 hours)

In this culminating module, students will apply the skills and knowledge acquired throughout the program to complete a comprehensive, real-world project. No additional lessons or assignments will be required; instead, students will work independently, utilizing their learning, problem-solving abilities, and research skills to meet project objectives. This final project serves as a capstone experience, demonstrating students’ readiness to apply their expertise in a professional setting.

Software Engineering Program (Full-time Format) | 6 Modules | 880 Hours | 22 Weeks

The program implies synchronous format with fixed schedule and deadlines. Additionally, students are expected to engage with the material at least 40 hours per week to maintain satisfactory progress through the program.

Module 1: Advanced HTML and CSS — 4 weeks (160 hours)

This module provides an in-depth exploration of HTML and CSS, focusing on advanced techniques for creating responsive and well-structured web pages. Students will learn to manage complex layouts, maintain an organized codebase, and develop adaptable designs that deliver seamless user experiences across various devices.

Learning Outcomes:

• Control a webpage layout with Flexbox.
• Display and position elements using position properties.
• Structure the code using the BEM methodology.
• Navigate code changes using the version control system Git.
• Manipulate elements visually using CSS transforms and keyframes.
• Create markup for user input forms.
• Organize file project structure according to BEM rules.
• Navigate and use the command line for Git.
• Write custom CSS for different screen sizes using media queries.
• Build the layout of a webpage using grid columns and rows.
• Collaborate with other developers using Git branches and pull requests.

Module 2: Basic JavaScript and Working with the Browser — 3 weeks (120 hours)

This module marks the start of hands-on programming. Students will dive into JavaScript fundamentals and apply their knowledge to develop a fully functional, interactive website.

Learning Outcomes:

• Utilize basic programming concepts in JavaScript.
• Manipulate HTML and CSS using JavaScript and the DOM.
• Debug the JavaScript code using the developer tools and console.
• Resolve Git merge conflicts with commands for managing code history.
• Manipulate different data structures in JavaScript using ES6 capabilities.
• Create HTML content with JavaScript and the DOM methods.
• Create event listeners that handle browser events.
• Manipulate objects in JavaScript.
• Control keyboard and mouse events.
• Access form elements and validate user input.

Module 3. Applied JavaScript — 3 weeks (120 hours)

This module expands on foundational JavaScript knowledge, introducing advanced programming concepts and techniques essential for a career in software engineering. Students will deepen their understanding of JavaScript to develop more complex and efficient applications.

Learning Outcomes:

• Implement object-oriented features in JavaScript using OOP principles.
• Modify markup content inside classes.
• Split JavaScript code into usable modules.
• Manage objects and arrays using destructuring syntax.
• Organize code according to the object-oriented design.
• Configure webpack for automating build tasks.
• Write asynchronous JavaScript code with promises.
• Make HTTP requests to a server using the Fetch API.
• Update markup using fetched data.

Module 4: Creating an Interface with React — 2 weeks (80 hours)

This module introduces students to the React library and its ecosystem, focusing on component-based design. Students will learn to create reusable components, streamline the development of complex interfaces, and write maintainable, scalable code efficiently.

Learning Outcomes:

• Create user interface elements using JSX.
• Create reusable React components.
• Manage the state and lifecycle of components using React Hooks.
• Add and access routes to a React application using React Router.
• Pass data between React components using context.
• Use controlled components to control form elements.

Module 5: Back-End Basics for Software Engineers — 4 weeks (160 hours)

Modern web development extends beyond the front end, relying on back-end systems to store, retrieve, and manage user data while enabling advanced functionality. In this module, students will explore core back-end development concepts and gain exposure to essential engineering principles relevant to technical interviews.

Learning Outcomes:

• Build a server and use it to serve data and responses to client requests.
• Store, manipulate, and retrieve data on a server using MongoDB and Mongoose.
• Determine different error types by name and class of the errors.
• Summarize and implement back-end authentication and authorization.
• Use regular expressions to validate and extract string data.
• Identify the complexity of an algorithm.
• Implement front-end authorization and authentication.
• Protect routes in a React application using protected routes and user tokens.
• Describe approaches to preventing web application security vulnerabilities.
• Write basic automated tests with Jest.
• Deploy a web application to Google Cloud.

Module 6. Final project — up to 4 weeks (160 hours)

In this culminating module, students will apply the skills and knowledge acquired throughout the program to complete a comprehensive, real-world project. No additional lessons or assignments will be required; instead, students will work independently, utilizing their learning, problem-solving abilities, and research skills to meet project objectives. This final project serves as a capstone experience, demonstrating students’ readiness to apply their expertise in a professional setting.

Breaks — up to 2 weeks

Up to 2 weeks of breaks to catch up with the program deadlines.

Cybersecurity (Full-time Format) | 5 modules | 600 hours | 17 weeks

Students are expected to engage with the material at least 40 hours per week to maintain satisfactory progress through the program.

Module 1. Fundamentals — 3 weeks (120 hours)

This module provides a comprehensive introduction to cybersecurity fundamentals, focusing on network configuration, threat detection, and the application of cybersecurity frameworks. Students will develop essential skills in network security, threat analysis, and risk assessment, ensuring a solid foundation in protecting organizational assets.

Learning Outcomes:

• Recognize and evaluate critical networking components, including topologies, IP addresses, and protocols, to understand how they contribute to cybersecurity.
• Analyze network traffic using monitoring tools to distinguish between anomalies caused by misconfigurations or malicious activities, and apply appropriate security measures.
• Conduct scans and enumerate device clusters within a company to identify assets and implement defense mechanisms that protect device identities from attackers.
• Apply cybersecurity frameworks, such as NIST and ISO to assess cybersecurity risk profiles and improve business continuity.
• Develop and implement effective incident response strategies to detect, contain, and recover from cybersecurity incidents, ensuring continuous improvement and risk reduction.

Module 2. Incident Response — 2 weeks (80 hours)

This module focuses on building expertise in identifying, responding to, and mitigating cybersecurity incidents. Students will learn how to model organizational threats, develop playbooks for common incident scenarios, and apply best practices to enhance incident response capabilities. Starting from this module students students prepare for the CompTIA Security+ certification exam, focusing on advanced exam topics and hands-on practice to ensure readiness.

Learning Outcomes:

• Threat model an organization and identify common threats and attack vectors.
• Prioritize mitigations to reduce attack surface and minimize risk.
• Develop playbooks for standard incident response scenarios: phishing, malware, data breach, and DDoS.
• Implement incident response procedures that effectively detect, respond to, and recover from security incidents.
• Focus on advanced CompTIA Security+ exam topics and key domains.
• Complete hands-on exercises to reinforce understanding of cybersecurity concepts.
• Take practice exams to assess knowledge and readiness.

Module 3. Vulnerability Management — 4 weeks (160 hours)

This module focuses on building expertise in identifying, assessing, and remediating vulnerabilities to protect organizational assets. Students will develop skills in network hardening, vulnerability assessment, exploitation, and remediation, preparing them for real-world cybersecurity challenges.

Learning Outcomes:

• Harden a network, establish a Security Network Operations Center (SNOC), and automate repetitive tasks to improve operational efficiency.
• Compare and test network architecture choices and evaluate network changes without disrupting production services.
• Strengthen authentication practices and identify defensive gaps according to threat models.
• Gain foundational knowledge of CompTIA Security+ exam domains and strategies, utilizing Anki flashcards for long-term retention.
• Conduct vulnerability assessments by inventorying assets, scanning, and interpreting results to assess and remediate high-risk vulnerabilities.
• Perform penetration tests to exploit vulnerabilities on servers and verify findings.
• Present findings using industry-standard report formats, plan remediation strategies, and execute the necessary fixes.
• Take practice exams to assess knowledge and readiness.

Module 4. Investigating Incidents — 4 weeks (160 hours)

This module focuses on strengthening the ability to identify, investigate, and respond to cybersecurity incidents. Students will develop expertise in securing networks, detecting complex attacks, and applying effective incident response strategies to mitigate risks.

Learning Outcomes:

• Secure physical, virtual, and cloud networks, safely extending corporate networks while aligning security practices with compliance goals.
• Validate, aggregate, and maintain critical data sources to enable continuous monitoring and improve incident detection capabilities.
• Investigate anomalous file changes, analyze unusual network traffic, and focus on suspicious login activity to identify security threats.
• Detect common attacks using evidence sources available to security analysts and create precise search queries to pinpoint attack indicators.
• Distinguish between normal and malicious activity to enhance threat detection accuracy.
• Ask the right questions and pivot between evidence sources to guide investigation efforts.
• Tailor mitigation and remediation strategies to reduce attack vectors uncovered during incidents.
• Take practice exams to assess knowledge and readiness.
• Confidently prepare for and pass the CompTIA Security+ certification exam.

Module 5. Emerging Threats — 1 week (40 hours)

This module prepares students to use the reference as a guiding structure for their learning and practice. It helps them understand how the frameworks, domains, and exam-style logic of Security+ translate into real-world AI security contexts. By aligning theory with practical application, learners build the ability to evaluate AI systems through a structured, standards-based lens.

Learning Outcomes

By the end of this sprint, learners will be able to:

• Apply the reference framework as a structured approach to analyze and secure AI systems.
• Identify and evaluate key vulnerabilities in AI-integrated environments, including prompt injection and LLM-based attacks.
• Explain how emerging protocols (such as MCP and A2A) contribute to secure AI operations.
• Assess real-world AI deployments within an organization using risk and resilience frameworks.
• Design and propose mitigation strategies to defend against AI-driven threats.
• Integrate concepts from traditional cybersecurity (e.g., Security+ domains) into modern AI security contexts.
• Demonstrate readiness to apply structured, standards-based thinking to new and evolving AI security challenges.

Module 6. CompTIA Security+ — 1 week (40 hours)

This module prepares students for the CompTIA Security+ certification exam, focusing on advanced exam topics and hands-on practice to ensure readiness.

Learning Outcomes:

• Focus on advanced CompTIA Security+ exam topics and key domains.
• Take practice exams to assess knowledge and readiness.
• Confidently prepare for and pass the CompTIA Security+ certification exam.

Breaks — up to 2 weeks

Up to 2 weeks of breaks to catch up with the program deadlines.

Data Science Program | 3 Modules | 660 Hours | 33 Weeks

Students are expected to engage with the material at least 20 hours per week to maintain satisfactory progress through the program.

Module 1: Python and Software Engineering for Data Science — 14 weeks (280 hours)

This module lays the foundation for a career in data by teaching Python, a versatile programming language widely used in data applications, alongside essential concepts in statistics and software engineering. The focus is on practical applications of Python for data analysis, visualization, and statistical exploration.

Learning Outcomes:

• Code in python syntax including built-in data structures (Strings, Lists, Tuples, and Dictionaries).
• Use pandas library to import and clean data.
• Generate visualizations to interpret data and present findings effectively.
• Conduct appropriate statistical data analysis such as descriptive statistics and hypothesis testing
• Use software development tools such as command line and git
• Write SQL queries, including subqueries and regular expressions, to retrieve and manipulate data.

Module 2: Machine Learning — 8 weeks (160 hours)

Machine Learning is one of the most transformative aspects of Data Science, enabling computers to predict and make inferences about the real world. This module builds on your knowledge of statistics, Python, and software engineering to create intelligent systems. You’ll explore supervised learning techniques, learn to evaluate models, and apply machine learning in real-world business contexts.

Learning Outcomes:

• Identify key machine learning concepts, including classification, regression, and the role of models and algorithms.
• Use scikit-learn to build, train, and evaluate machine learning models.
• Compare models, improve performance through parameter tuning, and select the best approach for a task.
• Apply machine learning to projects like real estate pricing and mobile plan recommendation.
• Demonstrate mastery of evaluation metrics for classification (precision, recall, F1 score, AUC-ROC) and regression (MAE, R²).
• Relate machine learning metrics to business outcomes, calculating profit, margins, and conversion rates.
• Analyze A/B test results using bootstrapping techniques.
• Explain data labeling processes and the role of assessors in supervised learning.

Required Disclosures applicable by state of student residence

WYOMING

If the student does not feel that the school has adequately addressed a complaint or concern, the student may consider contacting the Wyoming Department of Education at: 2300 Capitol Avenue, Hathaway Building, 2nd Floor, Cheyenne, WY 82002-0050; (307) 777-7690; http://edu.wyoming.gov/ContactUs.aspx.

If the student does not feel that the school has adequately addressed a complaint or concern, the student may consider contacting the Wyoming Attorney General at Attorney General’s Office, Consumer Protection Unit, 123 Capitol Building, 200 W. 24th Street, Cheyenne, WY 82002; (307) 777-7841; TDD: (307) 777-5351; http://attorneygeneral.state.wy.us.

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CALIFORNIA

Any questions a student may have regarding this catalog that have not been satisfactorily answered by the institution may be directed to the Bureau for Private Postsecondary Education at 1747 North Market, Suite 225 Sacramento, CA 95834, P.O. Box 980818, West Sacramento, CA 95798, www.bppe.ca.gov, toll free telephone number (888) 370-7589 Fax (916) 263-1897.A student or any member of the public may file a complaint about this institution with the Bureau for Private Postsecondary Education by calling (888) 370-7589 or by completing a complaint form, which can be obtained on the bureau’s Internet Web site www.bppe.ca.gov.

UTAH

TripleTen is registered with the State of Utah Department of Commerce - Division of Consumer Protection as Non-Degree Granting Postsecondary School. Complaints against TripleTen may be filed with the Utah Department of Commerce – Division of Consumer Protection at: 60 East 300 South, Salt Lake City, UT 84111, 800.721.7233. Consumer Protection Website: https://dcp.utah.gov.

Student Tuition Recovery Fund Disclosures

The State of California established the Student Tuition Recovery Fund (STRF) to relieve or mitigate economic loss suffered by a student in an educational program at a qualifying institution, who is or was a California resident while enrolled, or was enrolled in a residency program, if the student enrolled in the institution, prepaid tuition, and suffered an economic loss. Unless relieved of the obligation to do so, you must pay the state-imposed assessment for the STRF, or it must be paid on your behalf, if you are a student in an educational program, who is a California resident, or are enrolled in a residency program, and prepay all or part of your tuition.

You are not eligible for protection from the STRF and you are not required to pay the STRF assessment, if you are not a California resident, or are not enrolled in a residency program.

It is important that you keep copies of your enrollment agreement, financial aid documents, receipts, or any other information that documents the amount paid to the school. Questions regarding the STRF may be directed to the Bureau for Private Postsecondary Education, 1747 N. Market Blvd., Suite 225, Sacramento, CA 95834, (916) 574-8900 or (888) 370-7589.To be eligible for STRF, you must be a California resident or are enrolled in a residency program, prepaid tuition, paid or deemed to have paid the STRF assessment, and suffered an economic loss as a result of any of the following:

1. The institution, a location of the institution, or an educational program offered by the institution was closed or discontinued, and you did not choose to participate in a teach-out plan approved by the Bureau or did not complete a chosen teach-out plan approved by the Bureau.
2. You were enrolled at an institution or a location of the institution within the 120 day period before the closure of the institution or location of the institution, or were enrolled in an educational program within the 120 day period before the program was discontinued.
3. You were enrolled at an institution or a location of the institution more than 120 days before the closure of the institution or location of the institution, in an educational program offered by the institution as to which the Bureau determined there was a significant decline in the quality or value of the program more than 120 days before closure.
4. The institution has been ordered to pay a refund by the Bureau but has failed to do so.
5. The institution has failed to pay or reimburse loan proceeds under a federal student loan program as required by law, or has failed to pay or reimburse proceeds received by the institution in excess of tuition and other costs.
6. You have been awarded restitution, a refund, or other monetary award by an arbitrator or court, based on a violation of this chapter by an institution or representative of an institution, but have been unable to collect the award from the institution.
7. You sought legal counsel that resulted in the cancellation of one or more of your student loans and have an invoice for services rendered and evidence of the cancellation of the student loan or loans.

To qualify for STRF reimbursement, the application must be received within four (4) years from the date of the action or event that made the student eligible for recovery from STRF.

A student whose loan is revived by a loan holder or debt collector after a period of noncollection may, at any time, file a written application for recovery from STRF for the debt that would have otherwise been eligible for recovery. If it has been more than four (4) years since the action or event that made the student eligible, the student must have filed a written application for recovery within the original four (4) year period, unless the period has been extended by another act of law. However, no claim can be paid to any student without a social security number or a taxpayer identification number.