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. 37
    Sample and Shuffle Data with seed Control

    Learn to sample and shuffle data with seed control in Python. This lesson covers reproducible randomness using random.seed(), numpy.random, and pandas .sample(). Includes hands-on exercises and troubleshooting tips.

  2. 38
    Map Values with Dictionaries

    Learn to map values with dictionary mappings in Python for efficient data transformation. Hands-on example, troubleshooting, and next steps included.

  3. 39
    Categorize Data with Categorical Types

    Learn how to categorize data using pandas categorical types. This lesson explains the concept, benefits, and practical steps to convert data into categoricals, with hands-on examples and troubleshooting tips.

  4. 40
    Split-Apply-Combine in Action

    Learn split-apply-combine workflows in action with this hands-on Data Analysis with Python tutorial — step-by-step guidance, troubleshooting, and next steps.

  5. 41
    Handle Timezones in pandas

    Learn to handle timezones and periods in pandas. This practical lesson covers timezone conversion, period ranges, and common pitfalls, with hands-on examples and next steps.

  6. 42
    Resample Time Series for Aggregated Insights

    Learn to resample time series for aggregated insights in this practical Python tutorial. Step-by-step walkthrough, troubleshooting, and next steps.

  7. 43
    Encode Categorical Data

    Learn to decode and encode categorical data in pandas with practical hands-on steps, troubleshooting tips, and what to study next in this Data Analysis with Python tutorial.

  8. 44
    Use String Accessors for Regex Cleaning

    Learn to use pandas string accessors for regex-based text cleaning in this hands-on Data Analysis with Python tutorial. Step-by-step exercises, troubleshooting, and next steps included.

  9. 45
    Sort DataFrames with Multi-Index Keys

    Learn how to sort pandas DataFrames using multi-index keys. Master stable sorting, level selection, and ascending order control for complex hierarchical data — hands-on tutorial with troubleshooting tips.

  10. 46
    Select Columns with Query and Eval

    Learn to select columns using pandas query and eval for fast, readable data filtering and transformation in Python. Includes practical examples and troubleshooting.

  11. 47
    Round and Clip Values in Python

    Master rounding and clipping numerical values in Python for cleaner data analysis. This lesson covers core methods, practical examples, troubleshooting, and next steps in the Data Analysis with Python track.

  12. 48
    Fill Gaps with Interpolation

    Learn how to fill gaps in your data with interpolation techniques in Python. This lesson covers core concepts, step-by-step implementation, practical examples, and troubleshooting tips to help you confidently handle missing values in your data analysis projects.

    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.