Programs
Career journey
Events
Skills
Blog
About us
Sign in
Armenia

Data Science for AI Engineering

Ready to turn your strong Python and mathematics skills into industry-relevant data science expertise? Apply for this free program and learn from EPAM professionals.
20_WCA_lady_v1.jpg
20_WCA_lady_v1.jpg
Price
Free
Program start
November
Learning type
Facilitated
Language
English
Duration
38 weeks
Format
Online
Level
Fundamentals
Details

If you have a strong foundation in mathematics and Python and are ready to deepen your expertise in data science, machine learning and AI, this intensive program is for you.

Under the guidance of EPAM experts, you will:

  1. Build a solid foundation in data science, from statistics and data analysis to machine learning
  2. Explore deep learning techniques and their applications in solving real-world challenges
  3. Advance your skills through a specialized track in natural language processing, machine learning engineering or time series analysis
  4. Learn how to design and build modern AI solutions using LLMs, RAG, AI agents and other technologies used in the industry

After successfully completing all program stages, you will strengthen your professional skills and be prepared to apply them in data science and AI-related roles at EPAM or across the tech sphere.

Data Science & AI Engineering

This tech direction brings together mathematics, programming, machine learning and generative AI technologies to transform data into insights, build predictive models and create intelligent AI-powered solutions.

More about this skill
direction
What do we offer?
  1. Industry‑focused learning with EPAM, a leader in AI transformation engineering and consulting
  2. Free access to materials and resources designed and regularly refreshed by practitioners
  3. Intensive, hands-on training through numerous practical assignments
  4. Regular guidance and actionable feedback from professionals
  5. Modern educational ecosystem supported by built‑in AI tools
  6. Certificate of completion upon successfully fulfilling all certification requirements
  7. Opportunity to join the EPAM team after successfully completing all stages
Training process

The program is designed to guide you through two learning stages, helping you progress from fundamentals to AI-focused expertise.


Stage 1: Fundamentals (4 months with ~15 hours per week)

In this stage, you will build a solid foundation in data science through structured learning and hands-on practice.

  1. Weekly learning. Explore video lessons and self-study materials, then practice through tasks and tests — all within set deadlines to ensure steady progress.
  2. Mentor guidance. Submit your weekly practical assignments for feedback and approval from expert mentors.
  3. Interactive support. Join weekly group Q&A sessions with professionals to discuss questions and deepen your understanding.

Successfully completing this stage and passing the technical interview will allow you to continue.


Stage 2: Specialization (4 months with ~20-30 hours per week)

This stage is designed to deepen your expertise through intensive mentoring and hands-on practice. You will begin by joining one of the following mentoring programs:

  1. Natural Language Processing (NLP)
  2. Machine Learning Engineering
  3. Time Series Analysis

After completing your chosen specialization, you will continue with AI Solution Engineering mentoring to learn how to design and build modern AI solutions.

The program concludes with a final technical interview. If you successfully pass it and demonstrate strong skills, we may consider you for open positions at EPAM, subject to available opportunities.

What is required for training:
training-is-for-you

General requirements: 

  1. Citizens of Armenia and permanent residents who are eligible to work in this country
  2. Second-to-last or final year university students and recent graduates 
  3. Individuals aged 18 years and older 

Skills requirements: 

  1. English level B2 (Upper-Intermediate) or higher 
  2. Strong knowledge of Python, including the NumPy and Pandas libraries
  3. Proficiency in mathematical analysis (main topics of derivatives, integrals, extrema of functions in multidimensional real space) 
  4. Skills in linear algebra (vectors, matrices, tensors, linear equations, eigenvalues and eigenvectors, quadratic forms) 
  5. Knowledge of probability theory (definition of probability, conditional probability, Bayes theorem, expectation) 
  6. Command of statistics (basic concepts, hypothesis testing, concept of likelihood, estimation of distribution parameter) 
  7. Understanding of basic optimization concepts and methods (stationary points, Lagrange multipliers, gradient descent) 
  8. Knowledge of data structures and algorithms (sorting algorithms, algorithm complexity) 
How to get started?
  1. Register on this page. Once you have filled in all the required fields and attached your CV, you will find a confirmation with more details in your mailbox and the Notifications tab.
  2. Take the English test available in your account's "My assessments" section. You should complete it within 5 days of registration and achieve a B2-level result or higher to continue.
  3. Take the entry technical test available in your account's "My assessments" section. You should take it within 5 days of registration and reach a minimum score of 60% upon completing two parts of the test (preparation materials are below). 
  4. Have a conversation with our AI assistant in your account's "My assessments" section. This step will be available to shortlisted candidates after the review, with a recommended completion period of 5 days once it is unlocked.
  5. Pass an online final technical assessment on October 27. Closer to this date, we will share additional details if you are selected to participate in this step.
  6. Receive an invitation to the program. We will share enrollment results and further instructions for your learning journey within 1-2 weeks after the technical assessment.

Please check your inbox regularly to ensure you get all important notifications.

What will you learn?
Stage 1: Fundamentals (4 months with ~15 hours/week)
Stage 2: Specialization (4 months with ~20-30 hours/week)
Important notice
  1. Registration, assessment and program start dates are subject to change. Check for updates on this page and via email.
  2. We have designed this course for IT beginners seeking to master skills in data science. If you have some production experience, feel free to explore our career website for job opportunities.
  3. The program has limited seats, which makes enrollment competitive. Completing all tests does not automatically guarantee an invitation to the interview and final assessment. We will prioritize applicants with the highest scores who meet all requirements.
  4. We strive to maintain a fair registration and testing process for everyone. If we detect any cheating, we will have to reject your application.
  5. If you are interested in applying while enrolled in another EPAM Campus program, please discuss it with your training coordinator first.
Useful links
FAQs
Can I apply from a country that is different from the program location?
Will I receive a certificate after completing the program?
What should I do if I experience a technical issue during the assessment?
Can I retake a technical assessment or receive an additional attempt?

How does an AI-assisted interview work, and what topics are covered?

If I successfully complete all selection stages, will I be enrolled in the program?
When will I be informed about the enrollment results?
What happens if I miss Q&A sessions or fall behind?

Recommended programs

EPAM Campus provides training programs for various data specializations. Choose the one that fits you the most and apply today: 

  1. Data science is for math and programming enthusiasts willing to create algorithms that predict future trends and behaviors. It uses advanced analytics, machine learning and generative AI technologies to find patterns and provide insights for business growth. 
  2. Data analytics engineering is for those keen to lift massive amounts of data and transform it into actionable insights. It involves collecting, processing, analyzing, ensuring quality and visualizing data, which results in more facilitated business decision-making.