TripleTen is a professional education platform with eight programs in 2026, grouped by your starting point:
- Career growth programs for business professionals: AI Transformation Management, AI Business Analytics, AI Agents & Automation.
- Advanced programs for tech professionals: AI Systems Engineering, AI & Machine Learning.
- Tech programs for complete beginners: AI Software Engineering, Cybersecurity, Quality Assurance.
Below, you'll find an in-depth look at all eight programs: who they're for, the full tech stack from the syllabi, every project, the roles you can target with typical salaries, and the skills that carry over from your current job.
TripleTen programs at a glance
TripleTen offers online part-time programs in three categories for people with various backgrounds. Career growth programs for business professionals take specialists with 2+ years in a business function into AI roles in 4–5 months. Advanced programs for tech professionals help developers with 2+ years of production experience move into AI systems and ML roles in about 20–22 weeks. Tech programs for complete beginners help career switchers transition into quality assurance, cybersecurity, or AI software engineering in 5 or 7 months.
Every program has a 14-day full-refund window.
How TripleTen programs work
Every TripleTen program combines project-based learning, reviews by working practitioners, and career support, but each group starts from a different point.
- Admission. Each group starts with a step that fits its audience.
- Programs for business professionals start with Week 0: an assessment against job postings for your target role that shows which requirements your experience already covers and what the program adds.
- Programs for tech professionals start with a 3-minute skills audit and a call that turns the results into a personal study plan.
- Programs for complete beginners have no admission tests or extra requirements, so you start from the first lesson whatever your background.
- Projects. You learn through projects: 4 in the career growth programs, 5 in the advanced programs, and 4–13 in the beginner programs. Each one goes into your portfolio.
- Reviews. Practitioners review your projects. Career growth programs end with a 30-minute capstone project review in front of a practitioner and a manager who hires for similar roles. In the advanced programs, working engineers review all five projects live.
- Career support. In career growth programs, every project becomes résumé material, and career support lasts 6 months after graduation. Advanced programs start career coaching during the program and continue for up to 3 months after it. Beginner programs include a 6-week Career Sprint after graduation and an Active Job Search Period of up to 10 months.
- Updates. The curriculum is reviewed every two months against job posting requirements.
Career growth programs for business professionals
AI Transformation Management, AI Business Analytics, and AI Agents & Automation are career growth programs: you keep your background and industry and grow into an AI role. In weeks 1–9, all three programs share the same seven modules on how AI works, how to build with it, and how to judge what it produces. In weeks 10–18, each program adds three modules for its own role, and weeks 19–20 are the capstone project review.
You work in a virtual company modeled on business practice: 18 months of operational records, process documents, system sandboxes, and stakeholders to convince. You pick the department closest to your experience (operations, customer support, sales, marketing, or finance) and the process you'll work on.
Projects 1 and 2 are the same in all three programs:
- Project 1. Where the team loses time. You analyze 18 months of your department's data, map where the time goes, and compare an AI agent, a simple automation, a ready-made tool, or removing the step. You finish with a business case and a baseline.
- Project 2. The first AI agent. You build an agent that automates one process (for example, it reads a request, sorts it, and drafts a reply), run 200 past records through it, calculate cost and hours saved, and present the plan to the department head, played by a mentor.
AI Transformation Management
AI Transformation Management prepares you for AI Transformation Manager roles: you find where AI pays off, carry a solution through security, legal, and IT, and make sure teams use it. It runs 21 weeks at 10–12 hours a week and costs $9,900 upfront.
Who it's for
The program fits professionals with 2+ years in one function who have owned a process or a project and worked with other departments: operations and project managers, HR, training and L&D, customer service, and sales operations leads. Ask yourself: were you accountable for a result delivered through a team, or did you carry a solution through the company until teams used it?
What you'll learn and the tech stack
After the shared core, three modules cover what the role is hired for:
- A1. AI rollout and adoption. Pilot and rollout planning, role-based training, playbooks and guides, change management, a network of AI champions, and usage tracking. Tools: Teams, SharePoint, Microsoft Forms, Microsoft 365 Copilot, the ADKAR change model.
- A2. Taking the solution through the company. Sign-offs from security, legal, compliance, IT, procurement, and leadership; security and architecture review; vendor choice; ownership of the process, data, and solution; a sponsor and budget. Tools: Word, Excel, PowerPoint, Microsoft 365 Copilot, SIG Lite and CAIQ questionnaire templates, a DPA template.
- A3. The AI portfolio and company rules. A cross-department diagnostic, an AI opportunity register ranked by effect, feasibility, and risk, effect and usage metrics, rules for AI use, responsible AI, and a portfolio readout for leadership. Tools: Excel, SharePoint, Power BI, PowerPoint, NIST AI RMF.
You'll build things by hand a few times to understand how they work, but you won't write production code.
Projects
- Project 1. Where the team loses time and Project 2. The first AI agent, shared with the other career growth programs.
- Project 3. Pilot and sign-off: a 4-week pilot with part of the team, a playbook, and sign-off from security, legal, and IT, measured against the project 1 baseline.
- Project 4. A second team and a plan for the company: AI rolled out to a second team, an opportunity register with usage rules, and a plan for leadership.
- Capstone: the AI transformation of the company, presented in 30 minutes to a practitioner and a hiring manager who ask the questions a CFO and a security team would ask: where the saving comes from, what happens when the solution misroutes an urgent case, and why build instead of buy.
Roles and salaries
Over five years, the AI Transformation Manager role can grow into transformation director, where typical posted pay averages $196,000 in TripleTen's posting analysis.
Transition skills
Coordinating with other departments is the most requested skill in AI Transformation Manager postings (60%), and two years in operations or projects already gives you that. That's why the program has no modules on coordination, project management, or presenting. Your planning, advising, and analysis skills get amplified by AI, and the program adds the new tasks: finding where AI saves time (59%), rolling out AI tools to teams (59%), and setting rules for AI use (55%).
AI Business Analytics
AI Business Analytics prepares you for AI Business Analyst roles: you take a business question to a dashboard with AI in the loop, pull and check the data yourself, and bring a decision to the manager who asked. It runs 21 weeks at 10–12 hours a week and costs $9,900 upfront.
Who it's for
The program is for professionals with 2+ years who already work with numbers and turn them into conclusions: financial analysts, accountants, AR and AP specialists, and owners of recurring reports. Producing reports alone doesn't qualify: when AI is used for reporting tasks, it completes them on its own in 73% of cases, a recent TripleTen study shows. What counts is being accountable for what the numbers meant.
What you'll learn and the tech stack
- B1. Requirements and data in SQL. Interviews and workshops, as-is and to-be processes, user stories, acceptance criteria, a backlog, SQL written with AI, data quality checks, and reconciliation against a control calculation. Tools: Word, Microsoft 365 Copilot, Databricks, Azure DevOps Boards.
- B2. BI dashboards and the effect of the solution. Dashboards for a manager's question, data models, AI inside BI tools and chat assistants, reconciling numbers against SQL, automatic refresh, and acceptance testing. Tools: Power BI, Tableau.
- B3. Python, pipelines, and data for AI. Source to warehouse to report, analysis in Python with an AI assistant, telling a code error from a data error, API data pulls, a scheduled pipeline with quality checks, and field and metric descriptions so AI reads data correctly. Tools: Azure AI Foundry, Python, pandas, VS Code, Jupyter, GitHub, GitHub Copilot, Databricks.
If your entry assessment shows a gap in statistics, a bridge module (descriptive statistics, period and segment comparisons, hypotheses, A/B tests, correlation and cause) fits inside the same 10–12 hours a week.
Projects
- Project 1. Where the team loses time and Project 2. The first AI agent, shared with the other career growth programs.
- Project 3. Data and a dashboard: you define what the department head needs weekly, pull the history with SQL, set up automatic refresh, and add checks that catch errors, including AI's, before anyone sees the numbers.
- Project 4. An AI analyst for another team: a metric dictionary and an agent that answers questions such as "Why did my unit go over budget this month?", tested on a set of user questions.
- Capstone: a new way the company works with its key numbers, presented to a panel that asks where each number comes from, what happens when AI writes a wrong explanation that reaches leadership, and how the model handles seasonality.
Roles and salaries
AI skills carry a measurable premium here: in TripleTen's posting analysis, the median lower bound of the pay range is $102,000 for AI Business Analysts and $83,200 for Business Analysts without AI, a 23% difference.
Transition skills
Data analysis is the top requirement in AI Business Analyst postings (69%), followed by explaining and advising (50%) and planning projects (46%). Financial analysts and accountants already use these skills daily, and AI makes them faster. The program adds what finance teams rarely have yet: working with AI tools (47%), reporting automations (45%), SQL queries and dashboards (43%), coding basics (41%), and BI tools (38%).
AI Agents & Automation
AI Agents & Automation prepares you for AI Agents & Automation Specialist roles: you build the automation yourself, connect it across a company's systems, and keep it running. It runs 21 weeks at 10–12 hours a week and costs $9,900 upfront.
Who it's for
The program fits professionals with 2+ years in one business function who have run a recurring process and the systems behind it (CRM, ERP, ticketing, spreadsheets): set them up, fixed what broke, and explained them to others. Typical backgrounds are revenue and sales operations, business and process analysts, SaaS implementation and onboarding, and support and service operations. Choose it over AI Transformation Management if you want to build and run the automation of your area yourself.
What you'll learn and the tech stack
- TC1. Code for automations. Python scripts written with an AI agent, owning what an AI coding assistant wrote, reading and editing JavaScript and TypeScript, Git branches, pull requests, review and rollback, automated tests and evals before every change, SQL, and debugging from logs. Tools: Azure AI Foundry, Python, VS Code, GitHub, GitHub Actions, GitHub Copilot, Promptfoo.
- TC2. Agents in code and production. Agents through a model API with tools and function calls, prompts and context in code, RAG over a knowledge base, agent limits and action logs, environments, retries, API limits, monitoring, alerts, secrets, least privilege, and a support guide. Tools: Azure AI Foundry, Azure Functions, Key Vault, Application Insights, LangChain, LangGraph, Langfuse, GitHub Actions.
- TC3. System integrations and no-code agents. REST APIs, webhooks, OAuth, integration platforms, CRM automation, linking CRM, ticketing, and ERP into one process, agents on a corporate no-code platform, and an MCP connector to a company system. Tools: Power Automate, Copilot Studio, Teams, Dataverse, HubSpot, Salesforce, ServiceNow, Workato, n8n.
Projects
- Project 1. Where the team loses time and Project 2. The first AI agent, shared with the other career growth programs.
- Project 3. The agent in code, at work: you rebuild the project 2 agent in code with an AI coding assistant, connect it to your department's working system, write checks that run before every change, and track its cost and errors.
- Project 4. A process between teams: a no-code automation that moves work across systems, for example deal closed → CRM updated → client email → invoice data for accounting.
- Capstone: an automation system for the company, presented to a panel that asks how you know the agent is reliable, how a customer reaches a human when it fails, and what it costs if volume doubles.
Roles and salaries
Transition skills
What stays with you is coordination across departments (39% of AI Automation Specialist postings) and handling rules, quality, and risk (29%). Explaining and advising (45%), analyzing data (40%), and designing processes (29%) get amplified by AI. On top of that, the program adds the technical layer: code, integrations, agents, and production support. The tools in these postings match the syllabus closely: Python, Git, Power Automate, Salesforce, n8n, HubSpot, ServiceNow, Workato, Copilot Studio, LangChain, and MCP.
Advanced programs for tech professionals
TripleTen's two programs for tech professionals take engineers who already ship to production and teach them to build AI systems themselves. Neither program teaches coding from scratch, and entry starts with a 3-minute skills audit.
- Flexible pace. Study 10 to 20 hours a week, and your program length changes with your pace. AI Systems Engineering takes 22 weeks at 15–20 hours a week and about 8 months at 10 hours a week. AI & Machine Learning takes 20 weeks at 20 hours a week. Project hours stay fixed; theory time flexes with what you already know.
- One system across five projects. Each project extends the last, so you design, build, deploy, and present one working system.
- Code in a production-style repository. You start from an existing codebase and submit pull requests. Automated tests, a reviewer, and the instructor all check your work before a live review.
- Live project reviews. Each project gets a pre-review and a live review with working engineers. Retakes are unlimited.
- Weekly 1:1s. An optional 30-minute weekly session with an instructor, plus group sessions on evenings and weekends.
- Skip what you know. Show you understand a topic at the start of a project and move straight to building.
- Career support during the program. 1:1 coaching, résumé and LinkedIn repositioning, up to three behavioral and recruiter mocks, up to three technical mocks with working engineers, and debriefs after employer interviews, for up to 3 months after graduation.
- Lifelong access to the curriculum.
Instructors are working engineers whose profiles you can check, including Dr. Gönen Eren (20+ years of fault-tolerant distributed systems on AWS and Azure, author of the AI Systems Engineering curriculum), Mohamed Cherif (20+ years at AMD, Microsoft, Amazon, and Skype), Ahmed Abouzeid (10 years at Amazon in distributed systems and generative AI), Gagandeep Singh (leads AI engineering teams at Capital One), and Tejashree Khot (software development manager on Alexa at Amazon).
AI Systems Engineering
AI Systems Engineering is an advanced program for engineers who run production systems and want to put AI on top of them. You join a simulated platform team and take one production-shaped system through diagnostics, architecture, operations, security, and finally an autonomous AI system you build and present. It includes 330 hours of project work, takes 22 weeks at 15–20 hours a week, and costs $12,600 upfront.
Who it's for
The program fits backend and full-stack engineers, DevOps engineers, SREs, and cloud and platform engineers with 2+ years of recent hands-on production experience. If AI work in your company goes to another team or a vendor while you get the tickets, this program closes that gap: cloud and DevOps, service architecture, observability, security and compliance, scaling, and production LLM systems.
Projects and the tech stack
The sample project, Autonomous Incident Defense, is on TripleTen's GitHub.
Roles and salaries
Transition skills
Backend and infrastructure engineers already bring the foundation AI systems run on: services, databases, deployments, and on-call reliability. The program builds on that base and adds what AI system roles add to the job: production LLM integration, retrieval, evaluation pipelines, prompt-injection defense, and cost and latency control. In system-design rounds, you'll walk interviewers through a working system you built and reviewed with senior engineers.
AI & Machine Learning
AI & Machine Learning is an advanced program for engineers and data professionals who want to build, evaluate, and deploy models. You work through one storyline: models, services, a fine-tuned LLM, and an agent that all ship as features of one deployed SaaS product, with Claude Code as your engineering assistant. It includes 279 hours of project work, takes 20 weeks at 20 hours a week, and costs $12,600 upfront.
Who it's for
The program fits software engineers, data engineers, QA engineers, DevOps engineers, and quantitative professionals who ship production code or work with data. It also fits analysts whose models stay in the notebook and developers who got handed an AI project at work. The gap it closes: classical ML, choosing what to build with AI and what without, LLM applications and agents, and MLOps.
Projects and the tech stack
The sample project, BriefBoard, is on TripleTen's GitHub.
Roles and salaries
Transition skills
Engineers bring the hard part already: reading an unfamiliar codebase, shipping to production, and debugging under pressure. Data analysts bring data intuition and modeling basics. The program adds the ML and LLM layer on top: model training and evaluation, serving and MLOps, fine-tuning, RAG, agents, and LLM security. The career team treats your existing experience as a qualification, so you move into higher-leverage work without starting over.
Tech programs for complete beginners
TripleTen’s three tech programs for complete beginners take career switchers with no tech background into QA, cybersecurity, or software engineering. Each program runs 5–7 months at 15–20 hours a week, so you can keep your current job while you study.
When we interview our students about how they decided to switch, the same worries come up again and again. A recent TripleTen study puts numbers on them: financial risk (45%), uncertainty about job prospects (44%), and fear of not being technical enough (38%) nearly stopped career changers from moving into tech, and more than half (54%) made the decision without knowing anyone in the industry. Here’s how the beginner programs address each of these worries:
- “I’m not technical enough.” The programs start from zero and move in two-week sprints. Each sprint ends with a project reviewed by working professionals, so you get feedback on your work every two weeks and build a portfolio as you go.
- “I don’t know if I’ll find a job.” A career coach guides your search. You also join at least two live career activities a month, such as workshops and group sessions, where you meet working professionals and other career switchers.
- “I don’t know anyone in tech.” A career coach guides your search, and you join live group sessions with career coaches and at least two live career activities a month, where you meet working professionals and other career switchers.
- “I can’t afford to lose the money.” Every program has a 14-day full-refund window, and the beginner programs include a money-back guarantee: get your tuition back if you’re not hired within 10 months. Conditions: complete the program, follow the job-search roadmap with your coach, meet the weekly application and networking minimums, and meet eligibility requirements such as US residency, work authorization, and age.
Quality Assurance
Quality Assurance prepares you for QA Analyst and QA Engineer roles: you test web apps, APIs, and mobile apps in a structured way and automate the repetitive checks with Python and Selenium. It runs 5 months at 15–20 hours a week and costs $4,935 upfront.
Who it's for
The program is designed for career switchers with little or no knowledge of software testing. It suits detail-oriented professionals from service, admin, and operations roles who like finding what's broken and explaining it clearly.
What you'll learn and the tech stack
Projects
- Urban Routes: exploratory testing of a web app, then test design and documentation, then web app testing with DevTools.
- Urban Grocers API: validating and testing an API with Postman.
- Database testing: SQL queries to check data in a relational database.
- Testing an Android app: finding bugs with the Android Studio emulator and logs.
- Python foundations and automated testing with Python and Selenium: your first test scripts and browser automation.
- Final project: testing a mobile app, a web app, and an API, with bug reports for your findings.
Roles and salaries
These figures cover all experience levels. Entry-level QA roles, such as QA analyst or manual tester, usually start below them, and automation skills move you toward the higher end.
Transition skills
QA rewards skills career switchers often bring from service, admin, and operations jobs: attention to detail, following and documenting a process, and explaining a problem so a developer can fix it. The program adds the technical side: how web apps and APIs work, SQL, mobile testing, Python, and test automation.
From the TripleTen alumni community: Michael Allen worked as an indoor air quality manager before TripleTen and is now a QA Specialist at Tamman.
Networking and using the tools and recommendations my tutors and career coach gave me to better market myself. Michael Allen, TripleTen Student
Cybersecurity
Cybersecurity prepares you for security analyst roles: you secure networks, respond to incidents, find and fix vulnerabilities, and investigate attacks in a SIEM. It runs 7 months at 15–20 hours a week, ends with the CompTIA Security+ exam, and costs $9,800 upfront.
Who it's for
The program is built for career switchers, and it suits IT support specialists, system administrators, and professionals from risk, compliance, and operations roles who want to move into security.
What you'll learn and the tech stack
Projects
You complete 13 projects that follow the work of a security team at a managed security service provider. They include analyzing a network for security flaws, building an asset inventory (CMDB) from scan results, assessing a business against the NIST CSF, threat modeling a company, rewriting a ransomware victim's incident response plan, modernizing an outdated network with virtualization and presenting the plan to executives, a vulnerability assessment, a penetration test, remediation, setting up continuous monitoring, triaging alerts in Splunk, and two major incident investigations mapped to MITRE ATT&CK.
Roles and salaries
These figures cover all experience levels. First security roles are usually SOC analyst, IT security, or incident response positions; security engineering and penetration testing typically come with experience.
Transition skills
IT support and admin experience carries over directly: you already know networks, users, and systems. From any background, the program builds on risk thinking, prioritization, and communication, since security analysts explain technical issues to executives and legal teams. The program adds networking security, incident response, vulnerability management, SIEM investigations, and AI security.
From the TripleTen alumni community: Ora Hall worked as a psychometrician before TripleTen and is now a Cybersecurity Specialist at ThreatLocker.
Networking with the company on LinkedIn was definitely the biggest thing that landed me the job. Ora Hall, TripleTen student
AI Software Engineering
AI Software Engineering prepares you for software engineer and full-stack developer roles: you build web apps on the MERN stack (MongoDB, Express, React, Node.js), deploy them to AWS, and work with AI coding assistants on an existing TypeScript codebase. It runs 7 months at 20 hours a week, with the option to extend deadlines by up to 12 weeks in total, and costs $9,800 upfront.
Who it's for
The program is for career switchers from any background. No prior tech experience is required.
What you'll learn and the tech stack
Projects
- Project 1. Flashcard app: a web app built in nine parts, from layout and interactivity to a GitHub repository, a Figma-based responsive layout, form validation, and API requests.
- Project 2. A full-stack MERN app: an Express server with MongoDB, a React interface, authentication and authorization, and deployment to AWS in Docker.
- Project 3. A brownfield TypeScript app with AI assistance: extending an existing full-stack application with secure APIs, forms, testing, and deployment while working with AI coding tools.
- Project 4. Portfolio website: a TypeScript portfolio site that presents your work to employers.
Roles and salaries
These figures cover all experience levels. Junior developer roles usually start below them.
Transition skills
Problem-solving, persistence, and the ability to learn from documentation matter more in this program than a technical background. Teachers bring structured explanation, service workers bring speed under pressure, and admin roles bring organization. The program adds the full stack: frontend, backend, databases, deployment, and working with AI coding assistants.
From the TripleTen alumni community: Anna Eckman worked as a math teacher before TripleTen and is now a Software Engineer II at The Contingent.
How to choose the right TripleTen program
Pick the program that matches the experience you already have and the work you want to do next.
- You lead processes and teams, and want to own AI adoption: AI Transformation Management.
- You're accountable for numbers and want to pull, check, and explain data with AI: AI Business Analytics.
- You run processes and systems hands-on and want to build the automation yourself: AI Agents & Automation.
- You run backend, cloud, or platform systems and want to build AI systems on top: AI Systems Engineering.
- You ship code or work with data and want to build, fine-tune, and deploy models and agents: AI & Machine Learning.
- You're new to tech and like finding what's broken: Quality Assurance.
- You're new to tech or come from IT support and want to protect systems: Cybersecurity.
- You're new to tech and want to build software: AI Software Engineering.
FAQ
Do I need to know how to code?
Not for the beginner programs, AI Transformation Management, or AI Business Analytics. AI Agents & Automation adds Python and JavaScript in its second half. AI Systems Engineering and AI & Machine Learning require solid programming in any modern language.
Is TripleTen accredited?
TripleTen isn't an accredited college and doesn't grant degrees. It's licensed by the Wyoming Department of Education and registered in California as an out-of-state institution. Graduates receive a certificate of completion. Cybersecurity graduates also earn the CompTIA Security+ certification.
Can I study while working full-time?
Yes. All eight programs are online and part-time. Programs for business professionals take 10–12 hours a week, programs for tech professionals 10–20 hours with no fixed class times, and programs for complete beginners 15–20 hours.
Conclusion
The right TripleTen program depends on where you start: your experience picks the group, and the work you want next picks the program. Each path ends with projects you can walk an interviewer through.
