Data Science & Analytics with Python · Data Science & Analytics with Python

Data Analysis with Python

This track covers practical data analysis using Python's core libraries: NumPy for numerical computing, pandas for data manipulation, and visualization tools like Matplotlib and Seaborn. Designed for beginners and professionals alike, you will learn to clean, explore, and communicate data insights effectively. By the end, you will confidently perform end-to-end analyses and present findings in reproducible notebooks.

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Lesson outline

  1. 1
    Set Up Python for Data Analysis

    Step 1 in the Data Analysis with Python track: install Python, set up Jupyter Notebook, and prepare your environment for pandas, NumPy, and visualization libraries. Hands-on exercise included.

  2. 2
    Load Your First CSV with pandas

    Load Your First CSV with pandas — Data Analysis with Python.

  3. 3
    DataFrames with Head and Info

    Explore DataFrames with Head and Info — Data Analysis with Python lesson 3. Learn to inspect data quickly using .head() and .info().

  4. 4
    Series & DataFrame Basics

    Understand Series and DataFrame Basics — Data Analysis with Python.

  5. 5
    Select Data with loc and iloc

    Learn to select columns and rows with loc and iloc in pandas. Master label-based and position-based indexing with hands-on examples, troubleshooting tips, and next steps in the Data Analysis with Python track.

  6. 6
    Filter Data with Boolean Conditions

    Filter Data with Boolean Conditions — Data Analysis with Python.

  7. 7
    dropna & fillna for Missing Values

    Learn how to handle missing values with dropna and fillna in pandas. This tutorial covers removing or filling NaN values, comparing methods, and practical examples to keep your data analysis clean and accurate.

  8. 8
    Rename and Add Columns in pandas

    Learn how to rename and add columns in pandas with clear, hands-on steps. Perfect for data analysis with Python learners.

  9. 9
    Sort and Rank Your Data

    Sort and Rank Your Data Easily — Data Analysis with Python.

  10. 10
    Group Data with groupby

    Master pandas groupby() to slice, aggregate, and derive insights from your data — with hands-on examples, common pitfalls, and next steps.

  11. 11
    Aggregate with Sum, Mean, Count

    Learn to aggregate data with sum, mean, and count in Python using pandas. This lesson covers core concepts, practical examples, troubleshooting, and next steps.

  12. 12
    Merge and Join DataFrames

    Merge and Join DataFrames like a Pro — Data Analysis with Python.

    Data Analysis with Python — step-by-step tutorials

    What you will find here

    This track walks through data analysis with python in order — each lesson is server-rendered HTML you can read without JavaScript. Follow the outline, then practice in the browser IDE when a lesson links to runnable code.

    Tutorials vs quizzes and code samples

    Tutorials teach in sequence. For quick checks use quizzes. For copy-paste snippets see code samples. For deeper reading browse articles.