Python for Data Analysis: From Raw Data to Actionable Insights
- Hands-on AI and data science techniques from working practitioners
- Structured program with clear milestones and real-world datasets
- Accessible remotely — join from anywhere in Ukraine
About this program
What this program covers
Data analysis work is mostly unglamorous: cleaning messy CSVs, handling missing values, and writing queries that actually run in reasonable time. This program addresses exactly that.
You will work with real datasets from public sources — financial records, health statistics, logistics data — and learn to ask the right questions before writing a single line of code.
Tools and libraries
The curriculum centers on Python 3, pandas, NumPy, and Matplotlib. We also cover Seaborn for statistical plots and a brief introduction to Plotly for interactive dashboards.
Who fits this program
- Analysts moving from Excel to Python
- Junior developers adding data skills
- Researchers needing reproducible workflows
No prior Python experience is required, but comfort with spreadsheet logic helps. Progress depends on consistent practice — plan for roughly six hours per week outside of sessions.
Assessment and completion
Each module ends with a graded assignment on real data. The final project involves a full analysis report with documented code and a short presentation.
Program structure
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Module 1 — Python Foundations
Weeks 1–2Variables, loops, functions, file I/O. Writing clean, readable scripts.
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Module 2 — Data Wrangling with pandas
Weeks 3–5DataFrames, indexing, merging, groupby operations, handling nulls.
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Module 3 — Exploratory Data Analysis
Weeks 6–7Descriptive statistics, distribution analysis, outlier detection.
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Module 4 — Visualization
Weeks 8–9Matplotlib and Seaborn charts, choosing the right plot for the data type.
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Module 5 — Final Project
Weeks 10–11Independent analysis on a chosen dataset, peer review, and presentation.