Python Data Analysis with JupyterLab

Python Data Analysis with JupyterLab

If you are using or plan to use Python for data science or data analytics, then this is the right Python course for you. This course is in-depth and assumes that you already possess a strong understanding of Python from previous training or experience.

You will learn how to use Jupyter Notebook, an essential tool for writing, testing, and sharing quick Python programs. As the course progresses, you will also learn about Python libraries such as NumPy, which makes working with arrays and matrices more efficient, and pandas, a key tool for manipulating, munging, slicing, and grouping data. The course will conclude with an overview of simple data visualization techniques with matplotlib.

3 Months Access / 28 Course Hrs
  • Details
  • Syllabus
  • Requirements


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:
3 Months Access Course Code: T14275
No Instructor, Start Anytime
$119.00 CAD


  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


Hardware Requirements:

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

Software Requirements:

  • PC: Windows 8 or later.
  • Mac: OS 10 or later.
  • Browser: The latest version of Google Chrome or Mozilla Firefox are preferred. Microsoft Edge and Safari are also compatible.
  • Adobe Acrobat Reader.
  • Software must be installed and fully operational before the course begins.
  • Other: Email capabilities and access to a personal email account.


Prior to enrolling in this course, you must have previous Python programming experience. You should be comfortable writing your own functions and working with strings, lists, tuples, dictionaries, loops, and conditionals.

Instructional Material Requirements:

The instructional materials required for this course are included in enrollment and will be available online.