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Welcome to the Coursebook program

Congratulations on taking your first step towards becoming a data professional!

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Pre-requisites

Estimated time: None to 30 minutes Before you get started, make sure you have the following installed on your machine with the right privileges. If your machine is issued by your company, you may have to ask your IT department to grant you the necessary permissions and firewall access.
  • Install Python 3.7 or higher
  • Install Git
  • Install a code editor (I recommend Visual Studio Code, or VSCode for short)
  • Install Tableau Public
The Python courses will be taught using Python 3.7 or higher. For the most part, if you’re using a version of Python that’s slightly older, you should be fine.

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Tooling and Setup

Estimated time: 45 minutes
Throughout this learning path, you’ll be asked to write code and develop solutions to the problems presented. To help you with this, your instructors have developed a set of materials that you can clone and use on your local machine.
Throughout the course, you’ll be working with a variety of tools and services. To ensure that your code runs correctly, you’ll need to set up environment variables that contain sensitive information such as API keys and database credentials.Follow the instructions in the repository to set up your environment variables. At no point should you ever commit your environment variables to the repository or use them in your code directly.
Hop to the following section to perform the exercises in setting up your Python environment.Successful completion of this section will ensure that you have the necessary tools and libraries installed to work with Python, and that is a good starting point for the rest of the course.

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Python programming environment

To test that you have set up everything correctly, complete the following steps: Estimated time: 1 Hour
1

Pull external data into Python

  1. Use a command prompt or your Terminal to launch your command line interface, then pip install requests to install the Requests library.
  2. Type python or python3 to launch the Python interpreter.
  3. Run the following code snippet to pull external data into Python:
import requests
response = requests.get('https://raw.githubusercontent.com/supertypeai/idx_total_historical_1995/main/market_cap_history.csv')
print(response.text)
If the requests library is installed correctly, this code should execute without errors, and response.status_code should return 200, which is the HTTP status code for a successful request.If the requests library is not installed, Python will raise an ImportError, indicating that the requests module is not found.
To verify that you’ve performed the example correctly, the output should resemble the following:
  year,currency,market_cap
  1995,USD,66584940000
  1996,USD,90997080000
  1997,USD,29050020000
  1998,USD,22077860000
  ...
  2023,USD,717936458435
Reading in data from external sources is a common task in data analysis and data science. By using the requests library, you can easily pull data from APIs, websites, and other online sources directly into Python for further analysis and processing. Did you know? The data you just pulled in the total historical market capitalization of the IDX (Indonesian Stock Exchange) from 1995 to 2023. In fact, it is used in this visualization on a financial intelligence app we’ve built:
2

Use Pandas and Matplotlib

  • pandas is a powerful data manipulation library that allows you to work with structured data in Python.
  • matplotlib is a plotting library that enables you to create visualizations from your data. We’ll be using these libraries extensively throughout the Python data analysis courses.
Install these two libraries by running pip install pandas matplotlib in your terminal.Then, run the following code snippet to visualize the data you pulled in the previous step:
import pandas as pd
import matplotlib.pyplot as plt

url = 'https://raw.githubusercontent.com/supertypeai/idx_total_historical_1995/main/market_cap_history.csv'
df = pd.read_csv(url)

df['market_cap'].plot()
plt.show()
  • If the pandas and matplotlib are installed correctly, this code should execute without errors, and it will use the matplotlib library to plot the market capitalization data. Calling plt.show() will display the plot in a new window.
  • If pandas or matplotlib is not installed, Python will raise an ImportError, indicating that the module is not found.

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Practice your skills directly

Estimated time: 30 Mins You really only get better at programming by, well, programming. So, we’ve set up a series of exercises that you can work through to practice your skills.

GitHub Repositories

A gentle introduction to Python and R programming, for the absolute beginner.