Python for Data Analysis: A Working Toolkit is a structured track of 13 lessons that build a complete, interview-ready understanding of the topic. Work through them in order, then use the quiz and flashcards in each lesson to revise.
What this course covers
- Setting Up Python for Analysis: Notebooks, Environments & Libraries - A working setup in under an hour, and why environments prevent later pain.
- Python Essentials an Analyst Actually Uses - The subset of the language analytics work needs, without the software engineering.
- Arrays & Vectorisation: Why Loops Are Slow - Vectorised thinking, and the speed difference on a realistic dataset.
- Pandas Basics: Loading, Selecting & Filtering Data - Reading files, selecting rows and columns, and filtering without chained-index bugs.
- Cleaning Data with Pandas: Types, Missing Values & Duplicates - A cleaning pipeline in pandas, with the checks after each step.
- Grouping, Aggregating & Pivoting in Pandas - Split-apply-combine, multiple aggregations and reshaping results for reporting.
- Merging, Joining & Reshaping Data Frames - Joins in pandas, the row-count check after every merge, and wide-to-long reshaping.
- Working with Dates, Times & Time Series - Parsing dates, resampling, rolling windows and period-on-period comparison.
- Visualisation in Python: Matplotlib, Seaborn & Plotly - Which library to reach for, and the minimum to make a chart presentable.
- Statistical Analysis in Python and Reading the Output - Running tests and fitting models, then interpreting the output table correctly.
- Python versus R versus SQL: Choosing the Right Tool - Where each genuinely wins, what Indian teams actually use, and how to answer without tribalism.
- Writing Analysis Code Others Can Re-Run - Structure, naming, comments and seeds that let someone reproduce your number.
- Case Study: A Complete Analysis from Raw File to Recommendation - One project start to finish: clean, explore, analyse, visualise, write up.