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JustCode IT Academy

9

Data Analytics and Product Analytics with AI

400 000 ₸
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Allocated 48 Quotas

Data Analytics and Product Analytics with AI is a 27-week program of 7 modules (M0–M6) designed to train a junior analyst with a complete stack: Excel/Google Sheets, SQL/PostgreSQL, Python (pandas, NumPy, plotly), product analytics (DAU/MAU, retention, funnels, RFM, unit economics, A/B testing), and Power BI (Power Query, DAX, data modeling). AI tools (Claude, ChatGPT, GitHub Copilot) are integrated into every module as the analyst's everyday working instrument, not as a separate topic. The program is built from scratch — no technical background required: newcomers to IT, marketers, product managers, junior analysts, and entrepreneurs can fully master the profession. Training is entirely online with live classes twice a week (2 academic hours each), homework assignments, and project defense at the end of every core module (M1–M5). The final M6 is a capstone project — an end-to-end analysis of a fintech service based on real data — which becomes part of the graduate's professional portfolio. Upon completion, students receive 2 certificates — from JustCode and Astana Hub Tech Orda.

Special condition

Financial conditions: - Refundable security deposit of 200,000 ₸ before the course begins. Fully refunded upon successful completion of the program. Non-financial conditions: - Internal assessment at the selection stage — evaluation of the candidate's starting level and motivation. - Passing the external Astana Hub Tech Orda test with a minimum score of 50% — a mandatory requirement for successful course completion. - To receive the certificate, the student must achieve: attendance ≥75%, academic performance ≥75%, and successfully defend all 5 module projects and the final capstone project.

Course details

level

For beginner

Study format

Online

Entrance exams

No

Duration, in weeks

27

Education language

Russian

Qualifications

junior

Skills


Upon completing the course, the student will: 1) Work with data using Excel/Google Sheets, SQL/PostgreSQL, and Python (pandas) — the analyst's full stack 2) Integrate AI assistants (Claude, ChatGPT, Copilot) into real analytical tasks — at every step, not as a side topic 3) Identify patterns in data and formulate testable hypotheses 4) Conduct product analytics: evaluate the effectiveness of decisions and product changes 5) Build conversion funnels, calculate unit economics, and run A/B tests 6) Create interactive business dashboards in Power BI (Power Query, DAX, time intelligence) and communicate insights to the business 7) Build a portfolio of 5 module projects and a final capstone project (6 projects total) based on real-world data

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