About course
This course teaches Python the way analysts actually use it. You’ll set up Python with Anaconda or VS Code, learn Jupyter Notebooks, and then dive into pandas for loading, cleaning, transforming, and combining data. You’ll summarize with groupby, create pivot-like tables, handle dates and times, and build clear charts using Matplotlib and Seaborn. You’ll also learn to read/write Excel and CSV files, connect to a small SQLite database, and call simple web APIs. By the end, you’ll have reusable notebooks and a confident workflow for real-world analysis.
Course format: Self-paced recorded sessions
Language: English
Projects included: 3 hands-on projects — Sales Trend & Cohort Analysis, A/B Test Results Deep-Dive, Customer Churn EDA
Duration: ~15 hours of video + practice notebooks & datasets
Level: Beginner to Intermediate (no prior coding required)
Key Skills
- Environment setup with Anaconda/Mini conda or Python + VS Code
- Jupyter Notebook basics (cells, markdown, plotting inline)
- pandas essentials: Data Frame/Series, import/export (CSV, Excel), inspect data
- Data cleaning: fix types, handle missing values, deduplicate, string ops
- Transformations: filtering, sorting, apply/map, reshape (melt/pivot), merge/join/concat
- Aggregations: group by, rolling windows, pivot-like summaries
- Dates & times: parsing, resampling, time-based analysis
- Visualization: Matplotlib & Seaborn (bars, lines, histograms, box/violin, heatmaps)
- Working with files, folders, and paths; reading multiple files
- Intro to SQLite (SELECT, joins) from pandas; simple API requests (requests library)
- Exporting results: Excel reports, images, and clean CSVs
- Performance tips: vectorization basics, memory-aware operations
- Reusable workflows: project folders, notebook structure, reproducibility
