TripleTen Academic Catalog

Date of publication: November 9, 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 is 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.

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;

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.

1603 Capitol Ave, Suite #512A, Cheyenne, WY 82001

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.

1603 Capitol Ave, Suite #512A, Cheyenne, WY 82001

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:

You’ll learn how to write a compelling resume and cover letter.
You’ll graduate with a portfolio of projects that demonstrate practical work experience.
We’ll help you present your best self in tech and behavioral interviews and networking events.
We’ll build your soft skills and make sure you feel confident in your new position.
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 industry experience 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.

To submit your Money-Back Guarantee request, email career.usa@tripleten.com.

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 submit your request, email us at support@tripleten.com from the email address you used to register. Please include your full name, program, and reason for your request so we can review it and help.

Withdrawal & Refund Policy

Determining Your Withdrawal Date

Your official Withdrawal Date is the earlier of:

The date TripleTen sends you written confirmation that your notification of an intention to withdraw has been received; or
The date TripleTen notifies you of termination due to a violation of school policy.

How to Withdraw

To submit your withdrawal request, email us at support@tripleten.com from the email address you used to register. Please include your full name, program, and reason for your request so we can review it and help.

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.

Payment options:	AI Agents & Automation	AI Automation	AI & Machine Learning	AI Business Analytics	AI Software Engineering	AI Systems Engineering	AI Transformation Management	Cybersecurity	Data Analytics	Quality Assurance
TripleTen Upfront / Split payment	Total: $9,900	Total: $5,950	Total: $12,600	Total: $9,900	Total: $9,800	Total: $12,600	Total: $9,900	Total: $9,800	Total: $5,950	Total: $4,935
Payment upon Enrollment	A single payment of $9,900	A single payment of $5,950	A single payment of $12,600	A single payment of $9,900	A single payment of $9,800	A single payment of $12,600	A single payment of $9,900	A single payment of $9,800	A single payment of $5,950	A single payment of $4,935
2 month installment plan	$4,950 Upfront deposit and x1 $4,950	$2,975 Upfront deposit and x1 $2,975	$6,300 Upfront deposit and x1 $6,300	$4,950 Upfront deposit and x1 $4,950	$4,900 Upfront deposit and x1 $4,900	$6,300 Upfront deposit and x1 $6,300	$4,950 Upfront deposit and x1 $4,950	$4,900 Upfront deposit and x1 $4,900	$2,975 Upfront deposit and x1 $2,975	$2,468 Upfront deposit and x1 $2,468
4 month installment plan	$2,475 Upfront deposit and x3 $2,475	$1,488 Upfront deposit and x3 $1,488	$3,150 Upfront deposit and x3 $3,150	$2,475 Upfront deposit and x3 $2,475	$2,450 Upfront deposit and x3 $2,450	$3,150 Upfront deposit and x3 $3,150	$2,475 Upfront deposit and x3 $2,475	$2,450 Upfront deposit and x3 $2,450	$1,488 Upfront deposit and x3 $1,488	$1,234 Upfront deposit and x3 $1,234
TripleTen Installments (from 6 months)	Total $15,000	Total $10,900	Total $20,000	Total $15,000	Total $17,950	Total $20,000	Total $15,000	Total $17,950	Total $10,900	Total $9,040
6 month installment plan	—	$700 Upfront deposit and x6 $1,700	—	—	$700 Upfront deposit and x6 $2,875	—	—	$700 Upfront deposit and x6 $2,875	$700 Upfront deposit and x6 $1,700	$700 Upfront deposit and x6 $1,390
12 month installment plan	$500 Upfront deposit and x12 $1,208	$700 Upfront deposit and x12 $850	—	$500 Upfront deposit and x12 $1,208	$700 Upfront deposit and x12 $1,438	—	$500 Upfront deposit and x12 $1,208	$700 Upfront deposit and x12 $1,438	$700 Upfront deposit and x12 $850	$700 Upfront deposit and x12 $695
16 month installment plan	—	—	$700 Upfront deposit and x16 $1,206	—	—	$700 Upfront deposit and x16 $1,206	—	—	—	—
18 month installment plan	$750 Upfront deposit and x18 $792	—	—	$750 Upfront deposit and x18 $792	—	—	$750 Upfront deposit and x18 $792	—	—	—
24 month installment plan	—	$700 Upfront deposit and x24 $425	—	—	$700 Upfront deposit and x24 $719	—	—	$700 Upfront deposit and x24 $719	$700 Upfront deposit and x24 $425	$700 Upfront deposit and x24 $348

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

Note: Descriptions below are provided for reference purposes only. While we aim to update this Catalog frequently, changes to curriculum, including but not limited to module content and/or delivery timing, may occur. Enrolled students should refer to their individual account on TripleTen’s learning platform for the most up-to-date information.

Enrollment period (Calendar Days)

AI Agents & Automation	AI Automation	AI & Machine Learning	AI Business Analytics	AI Software Engineering	AI Systems Engineering	AI Transformation Management	Cyber Security	Data Analytics	Quality Assurance
140	98	140	140	196	154	140	210	112	140

AI Agents & Automation Program | 11 Modules | 240 Hours | 20 Weeks

Students are expected to engage with the material at least 10 hours per week to maintain satisfactory progress through the program. The program is designed for professionals with prior experience running recurring business processes. It opens with an entry assessment (week 0) that records the student’s existing skills against current job-posting requirements and selects the case from the student’s own function that runs through the whole program. Modules 1–7 form the common core shared by all three programs (weeks 1–12); modules 8–10 are specific to the program (weeks 13–18); module 11 is the final defense (weeks 19–20). The standard path is 20 weeks, at 10–12 hours a week. Note: this program is distinct from the AI Automation program listed above, which is designed for entry-level students.

Module 1: How Models and Agents Work — 1 week (12 hours)

Students learn how large language models produce answers, how models differ and how to choose one for a task, and what turns a model into an agent: tools, connectors, skills and subagents. The module covers where models fail or show bias and which data must never be uploaded. Working environment: M365 Copilot, Azure AI Foundry (OpenAI and Claude models), Copilot Studio; free versions of ChatGPT, Claude and Gemini for comparison; local models at an overview level.

Learning Outcomes:

Explain how an LLM produces an answer and what context and probability mean for its reliability.
Compare models and select one for a given task, including local models at an overview level.
Explain how an agent differs from a chat assistant and what a harness, a connector and a skill are.
Identify data that must not be shared with an AI tool and document the choice of model and tools for the program case.

Module 2: Finding the Opportunity and the Economics: Baseline and Priorities — 2 weeks (24 hours)

Students learn where AI produces a measurable effect in a process, interview a process owner, map the process as-is, fix a baseline (time, money, errors), compare options (remove the work, simplify, plain automation, AI, buy) and rank candidates by effect, difficulty and risk. The module ends with the first version of the business case for the program case. Working environment: Excel, Microsoft Forms, Visio or Whiteboard; Miro (free plan).

Learning Outcomes:

Collect candidate processes for AI and interview a process owner.
Map a process as-is and measure its baseline before any change.
Compare solution options and rank them by effect, difficulty and risk.
Write a first version of a business case.

Module 3: Setting Tasks for AI and Working with Context — 2 weeks (24 hours)

Students turn work tasks into briefs for AI, choose which documents, data and examples to supply, use structured outputs (JSON, tables, templates), build a knowledge base and retrieval without code, and pack a recurring task into an instruction, a skill or an agent for a team. They brief an agent with a goal, steps, allowed tools and stop conditions, and rework the brief until the result is stable across inputs. Working environment: M365 Copilot (agents, Notebooks), SharePoint as a knowledge base, Copilot Studio; Claude projects (optional).

Learning Outcomes:

Write a brief for AI that states the need, the audience, the format and the success criterion.
Select and structure context, including a knowledge base and structured outputs.
Package a recurring task as a reusable instruction, skill or agent for a team.
Produce a first-version assistant, a team agent and a set of test inputs for the program case.

Module 4: Building Automations and Agents Without Code — 2 weeks (24 hours)

Students split a process into steps handled by AI, by a rule and by a person; build an automation with an AI step (trigger, steps, conditions, human check); build an agent with tools and defined data access; decide when robotic process automation is needed instead of an integration; and debug from run logs. Working environment: Power Automate (Premium), Copilot Studio; n8n Community Edition and Zapier Free for comparison; UiPath at an overview level.

Learning Outcomes:

Design a process in which AI, rules and people each have a defined role.
Build a working automation with an AI step and a human check on a no-code platform.
Build an agent with tools, defined permissions and a hand-off to a person.
Read run logs to find what broke and produce a prototype of the solution for the program case.

Module 5: Connecting Systems: APIs, Webhooks, MCP and Scripts — 1 week (12 hours)

Students connect business systems through APIs and webhooks, use HTTP steps and custom connectors in an automation platform, connect an agent to a work system through MCP, and write a simple Python script with an AI assistant. The module ends with the action plan for the solution. Working environment: Power Automate (HTTP, custom connectors), Copilot Studio (MCP), Azure AI Foundry; Postman, Python, VS Code, GitHub Copilot Free.

Learning Outcomes:

Make an API request, read documentation and handle keys.
Use webhooks, HTTP steps and custom connectors in an automation platform.
Connect an agent to a work system through MCP.
Write and run a simple script with an AI assistant and produce the action plan for the program case.

Module 6: Checking AI Output and Evaluating Agents — 2 weeks (24 hours)

Students build a test set of ordinary, edge and critical cases with reference answers, set quality criteria and the cost of an error, identify typical failures (fabricated facts, calculation errors, an agent doing too much), apply LLM-as-a-judge and human review, compare versions after a change to the brief, the model or the tool, and set up guardrails, user acceptance testing and monitoring after launch. Working environment: agent evaluation in Copilot Studio, evaluations in Azure AI Foundry, an evaluation table in Excel; Promptfoo (optional).

Learning Outcomes:

Build a test set with reference answers and run a model or agent against it.
Define quality criteria and the cost of an error for a use case.
Detect regression after a change to the brief, the model or the tool.
Produce an evaluation report on 30–50 cases, a version comparison and a documented failure for the program case.

Module 7: Rules for Working with AI, Risks and the Economics of a Solution — 2 weeks (24 hours)

Students name the risks of a use case (data, compliance, model error, reputation), decide which data may go into which tool and check access in practice, build rules into the solution (agent limits, human check, action log), review the regulatory landscape (GDPR, HIPAA, SOX, EU AI Act, NIST AI RMF), calculate the full cost of a solution, compare build and buy and assess a vendor, and write a plan to measure the effect after launch. The module ends with the defense of the action plan: rules, risks, cost and a go/no-go decision. Working environment: Excel (TCO/ROI model), access settings and logs in Copilot Studio and Power Automate; NIST AI RMF and EU AI Act texts; Microsoft Purview (demo).

Learning Outcomes:

Identify the risks of an AI use case and the controls that address them.
Define data and access rules and verify them in the tools.
Calculate total cost of ownership, including licences, model calls, checking time and support, and compare build versus buy.
Defend an action plan covering rules, risks, cost and the measurement plan.

Module 8: Code for Automations — 2 weeks (24 hours)

Students write Python scripts that link steps (collect, process, pass on), use an AI assistant to read, understand and edit code, read and edit JavaScript and TypeScript with AI, use Git (branches, pull requests, review, rollback), run automated tests and evaluations before each change, and use SQL for automations and logs to find the cause of an error. Working environment: Azure AI Foundry (models for tests and evaluations); Python, VS Code, GitHub and GitHub Actions, GitHub Copilot Free, Promptfoo; Claude Code and Cursor (optional).

Learning Outcomes:

Write a Python script that links process steps and read the code an AI assistant produced.
Use Git for versioning, review and rollback.
Run automated tests and evaluations before a change.
Produce code and tests in a repository for the program case.

Module 9: System Integrations and Agents Without Code — 2 weeks (24 hours)

Students work with REST APIs and webhooks (documentation, keys, OAuth, data formats), compare integration platforms and choose one for a task, automate work in a CRM and link CRM, ticketing and ERP into one end-to-end process, build an agent on a no-code platform with knowledge sources, actions and publication to a work channel, connect MCP to a company system, and decide where a no-code agent is enough and where code is needed. Working environment: Power Automate (Premium), Copilot Studio (publishing to Teams), Dataverse; HubSpot Free CRM, Salesforce Developer Edition, ServiceNow Personal Developer Instance, Workato Developer Sandbox, n8n Community Edition; Agentforce, UiPath and Zapier at an overview level.

Learning Outcomes:

Connect two business systems through a REST API or webhook with authentication.
Build an end-to-end process across CRM, ticketing and ERP on an integration platform.
Build and publish an agent on a no-code platform with defined permissions.
Produce an integration of two systems and an agent connected to a company system for the program case.

Module 10: Agents in Code and Taking Them into Production — 2 weeks (24 hours)

Students build an agent through a model API with tools, steps and function calls, keep prompts and context in code with templates, versions and retrieval over a knowledge base, set agent limits, human checks and action logs, set up environments, scheduling, error handling, retries and API limits, logs, monitoring and alerts, review an incident, and apply service accounts, secrets and least privilege with a support guide. The module ends with the automation in production: monitoring, a support guide and an incident review. Working environment: Azure AI Foundry (OpenAI and Claude models by API), Azure Functions, Key Vault, Application Insights; LangChain / LangGraph, Langfuse, GitHub Actions; direct Anthropic and OpenAI APIs and LangSmith at an overview level.

Learning Outcomes:

Build an agent through a model API with tools and function calls.
Deploy an automation with environments, scheduling, error handling and retries.
Set up logs, monitoring and alerts and conduct an incident review.
Apply service accounts, secrets management and least privilege, and write a support guide.

Module 11: Final Defense — 2 weeks (24 hours)

Students prepare and defend the final project in a 30-minute online session before a practitioner from the track and a manager who hires for similar roles: a 10-minute presentation and demo, 15 minutes of questions from the perspectives of security, finance and a team lead, and 5 minutes of feedback. Graduates receive a certificate listing the role requirements closed and a hiring scorecard.

Learning Outcomes:

Present a working solution with its baseline, measured effect, checks and a documented failure.
Answer questions on access, failure modes, cost and alternatives.
Translate prior experience and the program work into résumé and interview material aligned with job-posting requirements.

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.

AI & Machine Learning | 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. From Product Requirements to a Trained Model — 6 weeks (120 hours)

In this module, students are “hired” as ML engineers at Wayfinder Labs and turn a product requirements document into a working model for Pulse, a commuter app that warns Boston riders when their bus will be more than three minutes late. They pull live data from the MBTA feed into a database, train and evaluate models, and hand off a versioned, registered model with a written defense of the results.

Learning Outcomes:

Turn a business ask into an ML problem with explicit framing, success metrics, and baselines.
Collect and store live, messy production data reliably.
Train and evaluate regression and classification models, choosing the metrics and thresholds that fit the problem.
Explain how neural networks learn, and train PyTorch models with sound mechanics.
Track experiments, version data, and hand off a registered model like a professional engineering team.
Work with an AI coding assistant while verifying and owning what it produces.

Module 2. Deploying and Operating the Model in the Cloud — 4 weeks (80 hours)

Pulse launches to real riders, so the model has to leave the laptop and run as an always-on cloud service. Students serve it behind FastAPI, containerize it, and deploy on AWS with an API-to-queue-to-worker path for async inference. The result is a monitored system that survives crashes and rush-hour spikes and accepts updates without going offline.

Learning Outcomes:

Serve, serialize, version, and load models safely behind a production API.
Containerize and orchestrate services with Docker and Kubernetes: deployments, services, config and secrets, probes, HPA, and zero-downtime rollouts.
Operate AWS fundamentals with least-privilege IAM and cost discipline, and argue SageMaker endpoints against self-managed serving.
Design an async inference path that survives traffic spikes and partial failure.
Automate delivery with gated CI that blocks broken changes before they reach users.
Monitor a live system and prove the alarms fire when something breaks.

Module 3. Fine-Tuning an LLM for Clinical Summaries — 3 weeks (60 hours)

In this module, students move from classical ML to LLM engineering at CareScribe, a healthtech startup whose product drafts after-visit summaries for doctors. A generic chatbot cannot be trusted with clinical documentation, so students fine-tune an open model on the clinic’s specific style: curating a PHI-free instruction dataset, running PEFT/QLoRA training on rented AWS GPUs, and evaluating the output against a clinician rubric rather than a loss curve.

Learning Outcomes:

Explain transformer architecture and attention, and control generation behavior deliberately.
Apply parameter-efficient fine-tuning to make training affordable on rented GPUs.
Curate instruction data under privacy constraints, treating PHI discipline as part of the product.
Evaluate generated clinical text for factual accuracy and rubric quality, not just automated metrics.
Manage cloud GPU cost as an engineering constraint.

Module 4. Building and Hardening a Production AI Agent — 4 weeks (80 hours)

In this module, students build Resolve, an agentic customer-support system for a team drowning in tickets. The agent screens incoming tickets for prompt injection, searches the company knowledge base with RAG, acts in Jira and Confluence through MCP, and proposes a resolution behind output guardrails. Students then red-team their own agent with OWASP LLM Top 10 attacks and prove the defenses hold.

Learning Outcomes:

Engineer prompts systematically for multi-step agent workflows rather than one-shot chatbot responses.
Build retrieval that grounds an agent in a real company knowledge base.
Connect agents to production workplace tools and deploy them on managed enterprise infrastructure.
Trace and evaluate agent behavior with structured evidence instead of subjective impressions.
Attack an AI system, defend it, and verify that the protections work.

Module 5. Capstone: Shipping AI Features in a SaaS Product — 3 weeks (60 hours)

In this module, students join the team behind Atlas, an existing healthcare platform delivered as a ready-to-run container with sample patient data, and get the brief AI engineers actually receive: here is our product and our data, what intelligent features can you add? The platform is already built, so the work is inventing, building, and demoing the AI capabilities — deciding feature by feature where generative AI earns its cost and where deterministic code wins.

Learning Outcomes:

Scope AI feature ideas against an existing product on feasibility, value, and cost.
Judge where generative AI is worth its price and where deterministic code is the better answer.
Integrate new AI services into a running containerized SaaS application and deploy them on AWS.
Handle sensitive data responsibly in a prototype setting.
Scope, design, build, and deliver an AI solution independently, then demo it and defend it to stakeholders.

AI Business Analytics Program | 11 Modules | 240 Hours | 20 Weeks

Students are expected to engage with the material at least 10 hours per week to maintain satisfactory progress through the program. The program is designed for professionals with prior experience working with business data, including finance, accounting and reporting. It opens with an entry assessment (week 0) that records the student’s existing skills against current job-posting requirements and selects the case from the student’s own function that runs through the whole program. Modules 1–7 form the common core shared by all three programs (weeks 1–12); modules 8–10 are specific to the program (weeks 13–18); module 11 is the final defense (weeks 19–20). The standard path is 20 weeks, at 10–12 hours a week. Statistics is offered as a bridge for students without the basics.

Module 1: How Models and Agents Work — 1 week (12 hours)

Learning Outcomes:

Explain how an LLM produces an answer and what context and probability mean for its reliability.
Compare models and select one for a given task, including local models at an overview level.
Explain how an agent differs from a chat assistant and what a harness, a connector and a skill are.
Identify data that must not be shared with an AI tool and document the choice of model and tools for the program case.

Module 2: Finding the Opportunity and the Economics: Baseline and Priorities — 2 weeks (24 hours)

Learning Outcomes:

Collect candidate processes for AI and interview a process owner.
Map a process as-is and measure its baseline before any change.
Compare solution options and rank them by effect, difficulty and risk.
Write a first version of a business case.

Module 3: Setting Tasks for AI and Working with Context — 2 weeks (24 hours)

Learning Outcomes:

Write a brief for AI that states the need, the audience, the format and the success criterion.
Select and structure context, including a knowledge base and structured outputs.
Package a recurring task as a reusable instruction, skill or agent for a team.
Produce a first-version assistant, a team agent and a set of test inputs for the program case.

Module 4: Building Automations and Agents Without Code — 2 weeks (24 hours)

Learning Outcomes:

Design a process in which AI, rules and people each have a defined role.
Build a working automation with an AI step and a human check on a no-code platform.
Build an agent with tools, defined permissions and a hand-off to a person.
Read run logs to find what broke and produce a prototype of the solution for the program case.

Module 5: Connecting Systems: APIs, Webhooks, MCP and Scripts — 1 week (12 hours)

Learning Outcomes:

Make an API request, read documentation and handle keys.
Use webhooks, HTTP steps and custom connectors in an automation platform.
Connect an agent to a work system through MCP.
Write and run a simple script with an AI assistant and produce the action plan for the program case.

Module 6: Checking AI Output and Evaluating Agents — 2 weeks (24 hours)

Learning Outcomes:

Build a test set with reference answers and run a model or agent against it.
Define quality criteria and the cost of an error for a use case.
Detect regression after a change to the brief, the model or the tool.
Produce an evaluation report on 30–50 cases, a version comparison and a documented failure for the program case.

Module 7: Rules for Working with AI, Risks and the Economics of a Solution — 2 weeks (24 hours)

Learning Outcomes:

Identify the risks of an AI use case and the controls that address them.
Define data and access rules and verify them in the tools.
Calculate total cost of ownership, including licences, model calls, checking time and support, and compare build versus buy.
Defend an action plan covering rules, risks, cost and the measurement plan.

Module 8: Requirements and Data in SQL — 2 weeks (24 hours)

Students find out what the business actually needs through interviews and workshops, describe the process as-is and to-be and the place for AI in it, write requirements (user stories, acceptance criteria, what AI does and what a person does) and run a backlog, write SQL with AI (filters, joins, grouping), and check data quality against a control calculation. Working environment: Word, M365 Copilot; Databricks Free Edition (SQL), Azure DevOps Boards; Snowflake, BigQuery and Jira at an overview level.

Learning Outcomes:

Gather requirements through stakeholder interviews and workshops.
Write user stories and acceptance criteria for an AI solution.
Write and verify SQL queries with filters, joins and grouping.
Produce a specification, SQL over the process history and a baseline for the program case.

Module 9: Python, Pipelines and Data for AI — 2 weeks (24 hours)

Students trace the path of data from source to warehouse to transformation to report or AI, find tables and relationships in a cloud warehouse, write Python for analysis with AI and distinguish a code error from a data error, pull data from a system through an API and build a scheduled pipeline, check data quality inside the pipeline, and prepare data for AI with field and metric descriptions and a knowledge base. Working environment: Azure AI Foundry (model APIs); Python, pandas, VS Code or Jupyter, GitHub, GitHub Copilot Free, Databricks Free Edition (scheduled jobs); Snowflake, dbt, Microsoft Fabric and AWS at an overview level.

Learning Outcomes:

Explain the path of data in a company and navigate a cloud warehouse.
Write a Python script with an AI assistant to process data and read the code it produced.
Build a scheduled pipeline with data-quality checks and an API source.
Produce a script and pipeline in a repository and a metrics dictionary for the program case.

Module 10: BI Dashboards and the Effect of the Solution — 2 weeks (24 hours)

Students build a dashboard for a manager’s question, build the data model behind it, use built-in AI in BI tools and dashboards in chat assistants and judge where they replace BI, reconcile the numbers against SQL and set up automatic refresh, and build a dashboard that shows the effect of the AI solution with acceptance criteria and user acceptance testing. The module ends with a working demo and pre-defense. Working environment: Power BI Pro (publishing, automatic refresh); Power BI Desktop, Tableau Public, dashboards in Claude and ChatGPT (optional); Copilot in Power BI and Looker at an overview level.

Learning Outcomes:

Build a dashboard that answers a specific business question with a reconciled data model.
Set up automatic refresh and reconcile dashboard numbers against SQL.
Judge where AI-built dashboards replace a BI tool and where they do not.
Produce a dashboard showing the effect of the solution, accepted against criteria.

Module 11: Final Defense — 2 weeks (24 hours)

Learning Outcomes:

Present a working solution with its baseline, measured effect, checks and a documented failure.
Answer questions on data sources, reconciliation, cost against manual work and alternatives.
Translate prior experience and the program work into résumé and interview material aligned with job-posting requirements.

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 | 5 Modules | 440 Hours | 22 Weeks

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

Module 1: System Diagnostics and Scaling Review — 4 weeks (80 hours)

In this module, students join a platform team and diagnose a production-shaped reference platform that must survive 10x growth. The system already runs locally with an API, worker, PostgreSQL, Redis, dashboards, traces, logs, and a synthetic load generator. Rather than redesigning the product, students trace one request path, repair key observability signals, run a reproducible load test, review an AI-generated capacity analysis, and write a scaling recommendation backed by evidence.

Learning Outcomes:

Explain how a production-shaped platform behaves at runtime, comparing monolith and microservice trade-offs from observed behavior rather than assumptions.
Interpret golden-signal dashboards and traces under load, and repair unsafe or incomplete instrumentation.
Estimate 10x growth impact with named assumptions and defend the first likely bottleneck using telemetry and capacity math.
Reject false scaling explanations with evidence and communicate a practical scaling recommendation to engineering leadership.

Module 2: Service Architecture and the Polyglot Data Tier — 4 weeks (80 hours)

In this module, students evolve the platform into a clearer service architecture and a production-style data tier. The starter repo is opinionated, but the design choices still matter: REST behavior, one gRPC boundary, data access, idempotency, caching, read freshness, and migration safety. Students implement the required path locally, work with Redis and a simulated read-replica workflow, fix focused relational data defects, and explain where GraphQL, NoSQL, partitioning, sharding, or managed databases become relevant.

Learning Outcomes:

Design APIs that remain stable as product requirements change.
Use idempotency keys to protect distributed requests from duplicate side effects.
Define service boundaries that reduce coupling instead of spreading complexity.
Implement a data layer that supports production-style reads, writes, migrations, and performance checks.
Explain replication lag, cache invalidation, and read freshness trade-offs from a local simulation.
Justify when NoSQL, GraphQL, partitioning, sharding, or managed databases are appropriate without building all of them.
Review AI-assisted implementation for hidden data and decomposition defects.

Module 3: Production Operations, Resilience, and the Chaos Lab — 5 weeks (100 hours)

In this module, students take the platform into a production-like local environment: it must run in local Kubernetes, pass CI gates, expose useful telemetry, apply one OpenTofu/LocalStack resource, survive one failure lab, and demonstrate one resilience pattern. The required path is local-first. Managed EKS, full GitOps, broader AWS topology, and deeper Kafka recovery are optional checkpoints or instructor-led comparisons, with budget, quota, region, tagging, and teardown guardrails whenever real AWS appears.

Learning Outcomes:

Deploy a multi-service platform into a local production-like environment and explain how infrastructure, deployment, scaling, and topology choices affect reliability.
Use OpenTofu and LocalStack without creating uncontrolled cloud spend, and position managed EKS and GitOps without full implementation.
Define one SLO alert tied to a user-impacting failure.
Handle partial failure with an implemented reliability pattern and prove resilience claims through a failure-lab evidence packet.
Review AI-generated IaC and CI suggestions for unsafe defaults and missing safeguards.

Module 4: Zero-Trust Security, Compliance, and a Grounded AI Feature — 4 weeks (80 hours)

In this module, students harden one core platform workflow and add a narrow grounded AI feature for a security-sensitive client scenario. The work covers threat modeling, access control, secrets handling, a CI security gate, redaction, audit logging, and simulated control evidence. Students decide which risks to close, which to document, and how to explain EU AI Act and HIPAA-style safeguards as engineering control mapping rather than legal compliance.

Learning Outcomes:

Identify realistic threats across users, services, data stores, pipelines, and AI features.
Implement identity, access, and secrets controls without production identity-cluster operations.
Add one security check that fails unsafe changes early.
Protect paid LLM API calls with redaction, logging, and cost and request limits.
Translate EU AI Act and HIPAA-style requirements into educational control mapping and simulated audit evidence.
Defend privacy, security, and governance trade-offs in a grounded AI feature.
Produce a security and simulated compliance package that an engineering reviewer can inspect.

Module 5: Autonomous AI System, Built and Defended — 5 weeks (100 hours)

In this module, students receive a production AI problem, not a demo brief: build an autonomous AI system that can be evaluated, bounded, observed, and defended. The required path uses paid LLM APIs through a provider adapter, PostgreSQL with pgvector, keyword search, one bounded LangGraph flow, and CI-safe eval fixtures. Students choose and defend an architecture that balances quality, cost, latency, and safety. GPU serving, vLLM, Groq, Bedrock, Ollama, cross-encoder reranking, and larger LLM-as-judge evaluations are discussed as trade-offs rather than required for the core build.

Learning Outcomes:

Build an end-to-end AI system that satisfies production acceptance criteria.
Implement RAG that balances retrieval quality, latency, cost, and safety.
Prevent prompt injection and unsafe tool execution with concrete controls.
Calibrate evals so failures are measurable, reproducible, and useful.
Use telemetry to explain quality, cost, latency, and failure behavior.
Defend the final architecture, rejected alternatives, provider choice, model-serving and safety trade-offs, eval gaps, and remaining risks.

AI Transformation Management Program | 11 Modules | 240 Hours | 20 Weeks

Students are expected to engage with the material at least 10 hours per week to maintain satisfactory progress through the program. The program is designed for professionals with prior experience in a business function. It opens with an entry assessment (week 0) that records the student’s existing skills against current job-posting requirements and selects the case from the student’s own function that runs through the whole program. Modules 1–7 form the common core shared by all three programs (weeks 1–12); modules 8–10 are specific to the program (weeks 13–18); module 11 is the final defense (weeks 19–20). The standard path is 20 weeks, at 10–12 hours a week.

Module 1: How Models and Agents Work — 1 week (12 hours)

Learning Outcomes:

Explain how an LLM produces an answer and what context and probability mean for its reliability.
Compare models and select one for a given task, including local models at an overview level.
Explain how an agent differs from a chat assistant and what a harness, a connector and a skill are.
Identify data that must not be shared with an AI tool and document the choice of model and tools for the program case.

Module 2: Finding the Opportunity and the Economics: Baseline and Priorities — 2 weeks (24 hours)

Learning Outcomes:

Collect candidate processes for AI and interview a process owner.
Map a process as-is and measure its baseline before any change.
Compare solution options and rank them by effect, difficulty and risk.
Write a first version of a business case.

Module 3: Setting Tasks for AI and Working with Context — 2 weeks (24 hours)

Learning Outcomes:

Write a brief for AI that states the need, the audience, the format and the success criterion.
Select and structure context, including a knowledge base and structured outputs.
Package a recurring task as a reusable instruction, skill or agent for a team.
Produce a first-version assistant, a team agent and a set of test inputs for the program case.

Module 4: Building Automations and Agents Without Code — 2 weeks (24 hours)

Learning Outcomes:

Design a process in which AI, rules and people each have a defined role.
Build a working automation with an AI step and a human check on a no-code platform.
Build an agent with tools, defined permissions and a hand-off to a person.
Read run logs to find what broke and produce a prototype of the solution for the program case.

Module 5: Connecting Systems: APIs, Webhooks, MCP and Scripts — 1 week (12 hours)

Learning Outcomes:

Make an API request, read documentation and handle keys.
Use webhooks, HTTP steps and custom connectors in an automation platform.
Connect an agent to a work system through MCP.
Write and run a simple script with an AI assistant and produce the action plan for the program case.

Module 6: Checking AI Output and Evaluating Agents — 2 weeks (24 hours)

Learning Outcomes:

Build a test set with reference answers and run a model or agent against it.
Define quality criteria and the cost of an error for a use case.
Detect regression after a change to the brief, the model or the tool.
Produce an evaluation report on 30–50 cases, a version comparison and a documented failure for the program case.

Module 7: Rules for Working with AI, Risks and the Economics of a Solution — 2 weeks (24 hours)

Learning Outcomes:

Identify the risks of an AI use case and the controls that address them.
Define data and access rules and verify them in the tools.
Calculate total cost of ownership, including licences, model calls, checking time and support, and compare build versus buy.
Defend an action plan covering rules, risks, cost and the measurement plan.

Module 8: AI Adoption and Use — 2 weeks (24 hours)

Students plan a pilot and a rollout to teams with dates and owners, design role-based training and basic AI literacy, write playbooks and guides so that teams work without them, lead change and work with resistance, build a network of AI champions, and track use and feedback to improve the solution and the training. Working environment: Teams, SharePoint, Microsoft Forms, M365 Copilot; the ADKAR methodology; Copilot Dashboard (Viva Insights) and admin analytics in ChatGPT and Claude Enterprise at an overview level.

Learning Outcomes:

Plan a pilot and a rollout with owners, dates and success measures.
Design role-based training and write a playbook.
Apply a change-management approach to resistance and build a champions network.
Track adoption and produce a pilot plan, a playbook and training materials for the program case.

Module 9: Carrying a Solution Through the Company — 2 weeks (24 hours)

Students map the approvals a solution needs (security, legal, compliance, IT, procurement, leadership), prepare for a security and IT architecture review, work with legal and compliance on liability and regulatory requirements, take a vendor through procurement, settle ownership of the process, the data and the solution between departments, and secure a sponsor and a budget. Working environment: Word, Excel, PowerPoint, M365 Copilot; SIG Lite and CAIQ questionnaire templates, a DPA template.

Learning Outcomes:

Build an approvals map for an AI solution in a company.
Prepare answers for a security and IT review: data, storage, access.
Prepare vendor and contract materials for procurement.
Produce an approvals package for the program case: what each party asked and what was answered.

Module 10: Portfolio of AI Initiatives and Company Rules — 2 weeks (24 hours)

Students diagnose processes across departments, build a register of AI opportunities ranked by effect, feasibility and risk, set effect and usage metrics before launch, write rules for working with AI for teams and an approval procedure for tools and use cases, cover industry requirements and responsible AI (bias, transparency), and present the portfolio to leadership. The module ends with a working demo and pre-defense. Working environment: Excel or SharePoint lists for the register, Power BI, PowerPoint; NIST AI RMF.

Learning Outcomes:

Build and prioritize a register of AI opportunities for a department.
Define effect and usage metrics before launch.
Write team-level rules for AI use and an approval procedure.
Present the department’s opportunities, rules and results to leadership.

Module 11: Final Defense — 2 weeks (24 hours)

Learning Outcomes:

Present a working solution with its baseline, measured effect, checks and a documented failure.
Answer questions on cost, risk, data responsibility and alternatives.
Translate prior experience and the program work into résumé and interview material aligned with job-posting requirements.

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 — 7 weeks (140 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 — 3 weeks (60 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.

CompTIA Security+ exam — 1 week (20 hours) (Optional)

This optional 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.

Note: Students wishing to be eligible for the Money-Back Guarantee must complete their certification exam requirements within the prescribed timeline.

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.

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.

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:

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.
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.
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.
The institution has been ordered to pay a refund by the Bureau but has failed to do so.
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.
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.
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.