Python Data Analysis with JupyterLab

Course Overview

If you or your team are using or plan to use Python for data science or data analytics, then this is the right Python course for you. The course assumes that you already have had a good amount of Python training and/or experience. Your live instructor will start the class by teaching you how to use Jupyter Notebook, a great tool for writing, testing, and sharing quick Python programs. Even if you do not end up using Jupyter Notebook as your main Python IDE, you will appreciate having it as a tool in your Python toolkit.

You will learn NumPy, which makes working with arrays and matrices (in place of lists and lists of lists) much more efficient, and pandas, which makes manipulating, munging, slicing, and grouping data much easier. You will also learn some simple data visualization techniques with matplotlib.

Course Benefits:

  • JupyterLab.
  • Jupyter notebooks.
  • Markdown.
  • The purpose of NumPy.
  • One-dimensional NumPy arrays.
  • 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.

Course Outline

  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
  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
  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

₦ 20,000

  • Learn at your own pace with 24/7 access
  • Duration: 1 year
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