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. 13
    Pivot Tables for Quick Summaries

    Learn pivot tables for quick summaries in this Data Analysis with Python tutorial. Master this essential pandas feature to aggregate and reshape data efficiently — with hands-on steps, troubleshooting, and what to study next.

  2. 14
    Apply Functions with apply and map

    Apply Functions with apply and map — Data Analysis with Python tutorial.

  3. 15
    Work with Dates and Times in pandas

    Learn to work with dates and times in pandas in this Data Analysis with Python tutorial. Master parsing, resampling, and time-based filtering with hands-on examples and troubleshooting tips.

  4. 16
    Create NumPy Arrays

    Learn to create NumPy arrays from scratch in Python — hands-on tutorial with practical steps, troubleshooting, and next steps for data analysis.

  5. 17
    Understand NumPy Array Shapes

    Understand NumPy Array Shapes — Data Analysis with Python. Learn what shapes and axes mean, how to inspect them, and why they matter for data operations.

  6. 18
    Vectorized Math with NumPy

    Perform Vectorized Math with NumPy — Data Analysis with Python. Learn to speed up calculations with arrays, avoid loops, and apply vectorized operations in hands-on exercises. Ideal for developers progressing step by step in the Data Analysis with Python track.

  7. 19
    Index, Slice, and Reshape NumPy Arrays

    Learn to index, slice, and reshape NumPy arrays in this hands-on data analysis tutorial. Master essential array manipulation skills with step-by-step examples, troubleshooting tips, and what to study next.

  8. 20
    Use Broadcasting for Efficiency

    Use Broadcasting for Efficient Computation — Data Analysis with Python.

  9. 21
    Generate Random Data with NumPy

    Learn to generate random data with NumPy in this hands-on Data Analysis with Python tutorial — build practical skills step by step.

  10. 22
    Descriptive Statistics in pandas

    Learn how to compute descriptive statistics in pandas with this hands-on tutorial from the Data Analysis with Python track. Master mean, median, mode, standard deviation, and more.

  11. 23
    Identify and Remove Duplicate Rows

    Master identifying and removing duplicate rows in Python with pandas. This lesson offers a hands-on tutorial, practical steps, troubleshooting tips, and next steps in the Data Analysis with Python track.

  12. 24
    Clean Text Data in Python

    Learn to clean text data with Python string methods — practical steps for data analysis, troubleshooting, and what to study next.

    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.