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. 49
    Aggregate with Named and Custom Functions

    Learn to use pandas groupby with named aggregations and custom functions to compute multiple statistics efficiently. Perfect for data analysis with Python.

  2. 50
    Export Data to Parquet & Feather

    Export Results to Parquet and Feather — Data Analysis with Python.

  3. 51
    Build an Interactive Notebook Report

    Learn to build an interactive notebook report in Python: structure, key components, and a hands-on exercise for reproducible data analysis.

  4. 52
    Profile DataFrames with Pandas Profiling

    Profile DataFrames with Pandas Profiling — Data Analysis with Python tutorial, lesson 52.

  5. 53
    Scikit-Learn Pipelines

    Learn to simplify data workflows with scikit-learn pipelines. This lesson shows how to chain preprocessing and modeling steps into a single reusable object, with hands-on examples and troubleshooting tips.

  6. 54
    Handle Missing Values with Imputation Strategies

    Learn how to handle missing values with imputation strategies in pandas — mean, median, mode, and more. Practical examples, troubleshooting, and next steps included.

  7. 55
    Detect Outliers Statistically

    Learn how to detect outliers with statistical methods in Python. This step-by-step lesson covers Z-scores, IQR, and visualization, with hands-on coding examples and troubleshooting tips.

  8. 56
    Fuzzy Match Merging in Python

    Merge multiple sources with fuzzy matching in Python. This tutorial covers the core concept, step-by-step implementation, and practical exercises for combining datasets with imperfect keys.

  9. 57
    GroupBy Aggregations

    Create database-style aggregations with pandas GroupBy in this hands-on tutorial. Learn to group, aggregate, and troubleshoot.

  10. 58
    Histograms and KDE in Python

    Visualize Distributions with Histograms and KDE — 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.