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. 1
    Set Up Python for Data Science

    Set up Python for data science — step-by-step installation and configuration for your data science environment.

  2. 2
    Install Jupyter & pandas

    Learn to install Jupyter Notebook and pandas in this step-by-step Python tutorial. Set up your environment, verify the installation, and get ready for hands-on data analysis in the next lesson.

  3. 3
    Load Data with pandas read_csv

    Learn to load data with pandas read_csv in Python. This step-by-step tutorial covers syntax, practical examples, troubleshooting tips, and what to study next in the Data Science with Python track.

  4. 4
    Inspect DataFrames with head and info

    Learn how to use pandas' head() and info() methods to quickly preview and summarize your DataFrames. This lesson covers the core concepts, step-by-step usage, hands-on practice, and common troubleshooting tips to help you understand your data before diving into analysis.

  5. 5
    Select Columns and Filter Rows

    Learn how to select columns and filter rows in pandas with this concise Python tutorial. Master bracket and .loc indexing, boolean conditions, and combine them for powerful data subsetting.

  6. 6
    Missing Values: dropna & fillna

    Handle missing values with dropna and fillna in pandas. Learn to remove or fill NaNs, compare strategies, and apply them in a hands-on exercise.

  7. 7
    Rename Columns & Fix Data Types

    Rename columns and fix data types in pandas. This lesson covers renaming columns with .rename(), correcting dtypes like strings to numbers, and handling common errors. Practical examples and what to learn next.

  8. 8
    Group Data with Groupby

    Learn to group data with groupby operations in pandas. This hands-on Data Science with Python tutorial covers the core concept, step-by-step application, and troubleshooting.

  9. 9
    Merge and Join DataFrames

    Learn how to merge and join DataFrames in pandas: combining datasets on keys with inner, left, right, and outer joins. This hands-on tutorial covers step-by-step logic, troubleshooting, and when to use each method. Includes a practical exercise and a look at what's next in the Data Science with Python track.

  10. 10
    Pivot and Melt in Python

    Reshape data with pivot and melt in Python. Learn how to transform DataFrames from wide to long format and back, with hands-on examples, comparisons, and troubleshooting tips.

  11. 11
    Apply Functions with apply and map

    Learn apply and map in pandas to transform data efficiently. This lesson covers core concepts, practical walkthroughs, and troubleshooting.

  12. 12
    Create New Columns

    Learn how to create new columns from existing data in pandas — practical steps, options, and pitfalls for data science with Python.

    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.