Programming On-Demand

Python Data Analysis with JupyterLab Training On-Demand (PYT252)

9 hours
Python

Learn to analyze and visualize data with JupyterLab, NumPy, pandas, and matplotlib through practical Python exercises using arrays, Series, and DataFrames.

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Enroll in On-Demand Training

Price per student
$49.00
  • On-demand access, 24/7
  • Readings, videos, and exercises
  • Quizzes to check your knowledge
  • Lifetime access

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Course Overview

Turn raw data into clear answers with Python. In this course, you'll get hands-on with the tools you need to analyze data using Python. You'll learn NumPy for fast numerical calculations, pandas for loading, filtering, and reshaping data from CSV files and databases, and matplotlib for building clear, annotated charts. Interactive exercises with real-world data give you practice with the skills you'll use to work with datasets of any size.

NOTE ABOUT PYTHON EDITORS / IDEs: 

  • Instructor-led classes: Students learn about Jupyter notebooks in class but are welcome to use their preferred Python editor or IDE throughout the training. 
  • Self-paced / OnDemand training: This course uses JupyterLab throughout, and setup instructions are included in the first lesson.

Course Benefits

  • Calculate Efficiently with NumPy: Create and manipulate one- and two-dimensional arrays, and see why NumPy outperforms standard Python lists.
  • Load and Explore Data with pandas: Build Series and DataFrames from lists, CSV files, and SQL queries, then inspect and summarize them.
  • Select, Filter, and Reshape Data: Use indexing, boolean selection, and pivoting to focus on the information you need, and handle missing values along the way.
  • Visualize Results: Create line and bar charts with matplotlib, customize labels and legends, and add annotations that highlight key points.
  • Work in Jupyter Notebooks: Run code, document your work with Markdown, and use magic commands to time and test your code.

Course Outline

  1. JupyterLab
    1. Exercise: Creating a Virtual Environment
    2. Exercise: Getting Started with JupyterLab
    3. Jupyter Notebook Modes
    4. Exercise: More Experimenting with Jupyter Notebooks
    5. Markdown
    6. Exercise: Playing with Markdown
    7. Magic Commands
    8. Exercise: Playing with Magic Commands
    9. Getting Help
  2. NumPy
    1. Exercise: Demonstrating Efficiency of NumPy
    2. NumPy Arrays
    3. Exercise: Multiplying Array Elements
    4. Multi-dimensional Arrays
    5. Exercise: Retrieving Data from an Array
    6. More on Arrays
    7. Using Boolean Arrays to Get New Arrays
    8. Random Number Generation
    9. Exploring NumPy Further
  3. pandas
    1. Getting Started with pandas
    2. Introduction to Series
    3. np.nan
    4. Accessing Elements in a Series
    5. Exercise: Retrieving Data from a Series
    6. Series Alignment
    7. Exercise: Using Boolean Series to Get New Series
    8. Comparing One Series with Another
    9. Element-wise Operations and the apply() Method
    10. Series: A More Practical Example
    11. Introduction to DataFrames
    12. Creating a DataFrame using Existing Series as Rows
    13. Creating a DataFrame using Existing Series as Columns
    14. Creating a DataFrame from a CSV
    15. Exploring a DataFrame
    16. Exercise: Practice Exploring a DataFrame
    17. Changing Values
    18. Getting Rows
    19. Combining Row and Column Selection
    20. Boolean Selection
    21. Pivoting DataFrames
    22. Be careful using properties!
    23. Exercise: Series and DataFrames
    24. Plotting with matplotlib
    25. Exercise: Plotting a DataFrame
    26. Other Kinds of Plots

Course Prerequisites

Required experience

  • Basic Python programming experience. In particular, you should be very comfortable with:
    1. Working with strings.
    2. Working with lists, tuples and dictionaries.
    3. Loops and conditionals.
    4. Writing your own functions.

Prerequisite Courses

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