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

Data Science with Python

This track covers the core data science workflow in Python: data manipulation with NumPy and pandas, exploratory analysis, and visualization with Matplotlib and Seaborn. It also introduces reproducible notebooks and best practices for sharing results. Ideal for analysts, researchers, and developers seeking to turn raw data into actionable insights using Python.

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Lesson outline

  1. 13
    Datetime Data in pandas

    Work with datetime data in pandas — Data Science with Python.

  2. 14
    Aggregate Data with Pivot Tables

    Learn to aggregate data with pivot tables in Python. Hands-on lesson covering core concepts, step-by-step instructions, troubleshooting, and next steps.

  3. 15
    Combine Multiple CSV Files with pandas

    Learn to combine multiple CSV files with pandas: merge, concatenate, and handle edge cases in this hands-on Data Science with Python tutorial.

  4. 16
    Export Cleaned Data to CSV or Excel

    Learn how to export cleaned data to CSV or Excel with pandas. Step-by-step instructions, common pitfalls, and next steps in the Data Science with Python track.

  5. 17
    Build Your First NumPy Array

    Create your first NumPy array in this step-by-step Python tutorial. Learn the core concept, see hands-on examples, troubleshoot common issues, and know what to study next in the Data Science with Python track.

  6. 18
    Index and Slice NumPy Arrays

    Master indexing and slicing in NumPy with this hands-on tutorial. Learn to access and modify array elements using basic and advanced techniques, including boolean masks. Includes practical examples, troubleshooting tips, and next steps for your data science journey.

  7. 19
    NumPy Universal Functions

    Use NumPy universal functions to perform element-wise operations on arrays efficiently. This lesson covers what ufuncs are, how to apply them, and practical examples to enhance your data science workflow in Python.

  8. 20
    Compute Statistics with NumPy

    Compute statistics with NumPy — lesson 23 in the Data Science with Python track. Learn to calculate mean, median, standard deviation, and more, with hands-on steps and troubleshooting.

  9. 21
    Reshape and Transpose NumPy Arrays

    Learn how to reshape and transpose NumPy arrays in Python for efficient data manipulation. This step-by-step tutorial covers core concepts, hands-on exercises, common pitfalls, and what to study next in the Data Science with Python track.

  10. 22
    NumPy Broadcasting Rules

    Learn NumPy broadcasting rules in this Data Science with Python tutorial. Understand how arrays of different shapes work together, practice hands-on examples, and avoid common pitfalls.

  11. 23
    Create Line Charts with Matplotlib

    Learn to create line charts with Matplotlib in Python. Step-by-step tutorial covering data preparation, plotting, customization, and troubleshooting. Practical exercises included.

  12. 24
    Customize Chart Colors

    Learn to customize chart colors and styles in Python for clearer data visualization. This lesson covers practical techniques, hands-on steps, and troubleshooting tips.

    Data Science with Python — step-by-step tutorials

    What you will find here

    This track walks through data science 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.