Python Data Analysis with JupyterLab

Details

What you will learn

  • JupyterLab & Jupyter notebooks
  • Markdown
  • The purpose of NumPy
  • One-dimensional & Two-dimensional NumPy arrays
  • Using boolean arrays to create new arrays
  • The purpose of pandas
  • Series objects and one-dimensional data
  • DataFrame objects to two-dimensional data
  • Creating plots with matplotlib

How you will benefit

  • Obtain valuable Python data analysis skills
  • Learn to work with Jupyter Notebook
  • Gain best practices for using matplotlib
  • Discover how to use NumPy to work with arrays and matrices of numbers
  • Develop experience utilizing pandas to analyze data

Enrollment Options:

Instructor-Led
6 Weeks Access Course Code: T14275
Start Dates* Jun 19 | Jul 17 | Aug 14 | Sep 18
*Choose start date in cart

$129.99

USD

Enroll before 31th May and get high quality lamination on your hardcopy certificate absolutely free! Use your certificate to your advantage by showing your certified credentials to prospective employers and recruiters.

Syllabus

  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

Requirements

Hardware Requirements:

  • This course can be taken on either a PC or Mac.

Software Requirements:

  • PC: Windows 8 or newer.
  • Mac: OS X Snow Leopard 10.6 or later.
  • Browser: The latest version of Google Chrome or Mozilla Firefox are preferred. Microsoft Edge and Safari are also compatible.

Other:

  • Email capabilities and access to a personal email account.

Prerequisites

There are no prerequisites to take this course.

Instructor

Self-Study

Reviews

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