Lesson 1 Setting Up Your Data Analysis Environment

This project is purely Python-based, unless explicitly stated otherwise.

Before we begin exploring data, you need a simple and reliable workspace so you can run code smoothly and follow every lesson without issues.

1.1 Recommended Setup

1.1.1 Option 1 — Easiest (Recommended)

Use Jupyter Notebook via the Anaconda distribution.

Why this option?

Anaconda installs Python, Jupyter Notebook, and essential data-science libraries in one streamlined setup.

Steps:

Download Anaconda: https://www.anaconda.com/download
Install it (default settings are fine)
Open Anaconda Navigator
Launch Jupyter Notebook

This guide uses widely adopted open-source tools from the Python data-science ecosystem, including Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and Jupyter. These tools form the foundation of modern data analysis and machine learning workflows (Harris et al., 2020; Hunter, 2007; McKinney et al., 2010; Pedregosa et al., 2011; Python Software Foundation, 2024; Team, 2023; Waskom, 2021).

1.2 Installing Required Libraries

Run the following command in a Jupyter Notebook cell.

!pip install pandas numpy matplotlib seaborn

Note:
If you are using Anaconda, these libraries are usually preinstalled.
The command above ensures your environment is up to date.

1.3 Practice Dataset

We will use the Iris dataset in the early lessons of this course.

You do not need to download it manually at this stage.

Lesson 01 will automatically generate data/iris.csv
This lesson focuses only on ensuring your environment is ready

1.4 Test Your Setup

You may run the test below after completing Lesson 01, once the dataset exists.

import pandas as pd

df = pd.read_csv('data/iris.csv')
print(df.head())

   sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa

1.5 Troubleshooting

If something does not work as expected:

Restart your notebook and try again
Confirm the file path is data/iris.csv
Ensure the data/ folder exists
Re-run the installation command if needed

Setup issues are common — take your time.

1.6 Exercise

Confirm that Jupyter Notebook opens successfully
Run the library installation command without errors
Verify that you can run a simple Python cell

1.7 Summary

You set up a Python-based data analysis environment
You installed essential data science libraries
You verified that your notebook can execute Python code
You prepared your system for working with real datasets

Continue to Lesson 02 — Load and Explore a Dataset.

References

Harris, C. R. et al. (2020). Array programming with NumPy. Nature. https://doi.org/10.1038/s41586-020-2649-2

Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering. https://doi.org/10.1109/MCSE.2007.55

McKinney, W. et al. (2010). Pandas-dev/pandas: pandas. Zenodo. https://doi.org/10.5281/zenodo.3509134

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in python. In Journal of Machine Learning Research (Vol. 12, pp. 2825–2830). https://scikit-learn.org/

Python Software Foundation. (2024). Python language reference. https://www.python.org/

Team, J. D. (2023). Project jupyter. https://jupyter.org/

Waskom, M. L. (2021). Seaborn: Statistical data visualization. Journal of Open Source Software. https://doi.org/10.21105/joss.03021