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. 25
    Convert Data Types

    Convert Data Types for Accurate Analysis — Data Analysis with Python.

  2. 26
    Combine DataFrames in Python

    Learn to join, merge, and concatenate DataFrames in pandas with hands-on examples, troubleshooting tips, and clear guidance on choosing the right method for your data analysis tasks.

  3. 27
    Pivot Tables in Python

    Learn pivot tables and cross-tabulations in Python for data analysis — concise steps, practical examples, and troubleshooting.

  4. 28
    Group Data with groupby Aggregations

    Learn to group data with groupby aggregations in this step-by-step tutorial — covering the core concept, hands-on exercises, and common edge cases.

  5. 29
    Filter Rows with Boolean Indexing

    Filter Rows with Boolean Indexing — Data Analysis with Python.

  6. 30
    Handle Missing Values with fillna and dropna

    Master fillna() and dropna() to clean missing values in pandas—learn when to fill vs drop with hands-on examples and troubleshooting tips.

  7. 31
    Reshape Data with melt and stack

    Learn to reshape data using pandas melt and stack functions in this hands-on Python tutorial. Understand when to use each method, follow step-by-step examples, and troubleshoot common issues. Perfect for data analysis learners.

  8. 32
    Save and Load CSV & Excel Files

    Learn to save and load data in CSV and Excel formats with pandas in Python. Step-by-step guide with hands-on exercises, troubleshooting, and next steps. Ideal for data analysis beginners.

  9. 33
    Read JSON and HTML Tables

    Read JSON and HTML Tables with pandas — Data Analysis with Python.

  10. 34
    Combine Datasets with SQL-like Joins

    Learn to combine multiple datasets with SQL-like joins in Python. This tutorial covers the core concepts, step-by-step implementation, hands-on exercises, and common pitfalls to help you merge data effectively for analysis.

  11. 35
    Apply Custom Functions with vectorize

    Apply custom functions with vectorize — learn how to use numpy.vectorize to apply Python functions element-wise on arrays. This Data Analysis with Python tutorial explains the concept, provides a hands-on exercise, and covers troubleshooting.

  12. 36
    Rolling Statistics with Rolling Windows

    Compute rolling statistics with rolling windows in Python. Master moving averages, rolling sums, and trends with pandas — hands-on exercises included.

    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.