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Python for Data Science Bootcamp

NumPy, pandas, Matplotlib and your first end-to-end analysis.

Taught by Bibek Sharma · Data Scientist

What you'll learn

  • Set up Python 3.13, a virtual environment and Jupyter so projects stay isolated and reproducible
  • Write Python functions, loops and comprehensions that transform lists and dictionaries of data
  • Use NumPy arrays and vectorised operations instead of slow Python loops
  • Load, inspect, clean and filter tabular data with pandas DataFrames
  • Group, join and reshape data to answer business questions
  • Build clear Matplotlib charts and deliver a complete, documented analysis notebook

About this course

Learn Python for data science from a clean environment setup to a finished analysis you can show an employer. This bootcamp is for beginners and career changers who know basic spreadsheets and want to work with data in Python. You will install Python and Jupyter, learn the language features that matter for data work, use NumPy for fast numerical arrays, and spend most of the course in pandas: loading CSV and Excel files, cleaning missing values, filtering, grouping, joining and reshaping DataFrames. You will plot with Matplotlib and finish with a capstone that takes a raw dataset through cleaning, analysis and charts to a written summary. No prior programming experience is required; if you have used formulas in Excel you are ready. Every lesson includes runnable code and exercises, so by the end you will have a portfolio-ready notebook and the confidence to keep going with statistics or machine learning.

Course content

7 lessons
  1. 1Setting Up Python, Jupyter and a Clean EnvironmentInstall Python, isolate projects with virtual environments and run your first notebook.Preview
  2. 2Python Essentials for Data WorkLists, dictionaries, functions, loops and comprehensions: the language features analysts use daily.
  3. 3NumPy: Arrays and Vectorised MathsReplace slow loops with fast array operations and understand the engine underneath pandas.
  4. 4pandas: Loading, Inspecting and Selecting DataRead CSV and Excel files into DataFrames, inspect them properly and select rows and columns.
  5. 5Cleaning and Transforming DataHandle missing values, duplicates, messy text and derived columns without breaking the data.
  6. 6Grouping, Joining and ReshapingAnswer real questions with groupby aggregations, merges between tables and pivots.
  7. 7Plotting with Matplotlib and the Capstone AnalysisMake clear charts, then bring everything together in a complete, documented end-to-end analysis.