Schedule - Introduction to Python

Day 1

Time Topic Instructor
10:00 - 10:15 Workshop Introduction Will
10:15 - 10:45 Introduction to Python and Jupyter Lab Will
10:45 - 10:50 Break
10:50 - 11:25 Variables Noor
11:55 - 12:00 Overview of self-learning materials and homework submission Will

Before the next class:

I. Please study the contents and work through all the code within the following lessons:

  1. Conditional Statements

    This lesson introduces conditional logic in Python, showing how to use if, elif, else, comparison operators and logical operators to evaluate conditions.

    In this lesson, we will:

    • Create conditional statements using if, elif and else
    • Use the in operator to check for membership in a collection
    • Use logical operators to test multiple conditions at the same time
  1. Submit your work:
  • Each lesson above contains exercises; please go through each of them.
  • Submit your answers to the exercises using this Google form on the day before the next class.

Questions?

  • If you get stuck due to an error while running code in the lesson, email us

Day 2

Time Topic Instructor
10:00 - 10:30 Self-learning lessons discussion All
10:30 - 11:00 Data Structures Noor
11:00 - 11:05 Break
11:05 - 11:55 Loops Will
11:55 - 12:00 Overview of self-learning materials and homework submission Will

Before the next class:

I. Please study the contents and work through all the code within the following lessons:

  1. Functions

    This lesson explains how functions work in Python, from calling built-in functions with arguments to defining your own reusable functions to organize and simplify code.

    In this lesson, you will:

    • Describe and utilize functions in Python
    • Modify default behavior of a function using arguments
    • Identify Python-specific sources of obtaining more information about functions
    • Demonstrate how to create user-defined functions in Python

  2. Loading and Installing Libraries

    This lesson introduces Python libraries, showing how to install packages, import them into your environment and explore library functions to extend Python functionality for data analysis.

    In this lesson, you will:

    • Explain different ways to install external Python libraries
    • Demonstrate how to load a library and how to find functions specific to a library
  1. Submit your work:
  • Each lesson above contains exercises; please go through each of them.
  • Submit your answers to the exercises using this Google form on the day before the next class.

Questions?

  • If you get stuck due to an error while running code in the lesson, email us

Day 3

Time Topic Instructor
10:00 - 10:30 Self-learning lessons discussion All
10:30 - 11:00 NumPy Arrays Will
11:00 - 11:05 Break
11:05 - 11:55 Pandas DataFrames Noor
11:55 - 12:00 Overview of self-learning materials and homework submission Will

Before the next class:

I. Please study the contents and work through all the code within the following lessons:

  1. Data Wrangling

    This lesson focuses on wrangling linked datasets with pandas, including filtering, reordering, saving cleaned tables and merging DataFrames to prepare real data for analysis.

    In this lesson, you will:

    • Reorder related datasets to ensure that they are in the same order
    • Extract specific rows from a DataFrame using the isin operator
    • Save a DataFrame as a new CSV file
    • Use the merge() function to combine two DataFrames

  2. Plotting basics with Matplotlib and Seaborn

    This lesson introduces data visualization in Python using Matplotlib and Seaborn, showing how to build scatterplots, adjust aesthetics and customize labels to create clear figures.

    In this lesson, you will:

    • Explain the concept of layering in plotting and how to build a plot step by step
    • Create a scatterplot using MatPlotLib and customize its aesthetics with Seaborn
    • Apply different themes to a plot and adjust axis labels and titles

  3. Boxplots

    This lesson shows how to create and customize boxplots with Matplotlib and Seaborn to visualize distributions, identify outliers and adjust colors.

    In this lesson, you will:

    • Generate a boxplot
    • Customize the aesthetics of a boxplot
    • Find hexadecimal codes for colors and use them to change the colors of a boxplot
  1. Submit your work:
  • Each lesson above contains exercises; please go through each of them.
  • Submit your answers to the exercises using this Google form on the day before the next class.

Questions?

  • If you get stuck due to an error while running code in the lesson, email us

Day 4

Time Topic Instructor
10:00 - 10:45 Self-learning lessons discussion All
10:45 - 10:50 Break
10:50 - 11:40 Machine Learning - Random Forests Noor
11:40 - 11:50 Discussion, Q & A All
11:50 - 12:00 Wrap-up Will

Resources

Other Python courses

We acknowledge that there are many other groups that have created excellent Python courses:

Learning more AI and ML

Python is one of the most popular languages for machine learning and artificial intelligence. If you are interested in learning more about these topics, we recommend the following resources as good starting points:

Datasets for more practice

There are many websites that aggregate interesting datasets that you can use to practice your Python skills. Here are a few popular ones: