Data Science with Python: Step by Step

An eight-module course on the data science workflow: setup, pandas, cleaning, exploration, statistics, machine learning, evaluation, and finishing a project.

8 lessons · about 77 minutes of reading

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1. 1

   Data Science with Python Module 1: What Data Scientists Do and Setting Up Python

   Understand the data science workflow, then set up Python, a virtual environment, and Jupyter with the core libraries.

   8 min read

2. 2

   Data Science with Python Module 2: NumPy and pandas Basics

   Work with arrays in NumPy and tables in pandas: select, filter, sort, and summarize data with groupby.

   9 min read

3. 3

   Data Science with Python Module 3: Loading and Cleaning Data

   Read files into pandas, then fix missing values, wrong types, duplicates, inconsistent text, and outliers.

   10 min read

4. 4

   Data Science with Python Module 4: Exploratory Data Analysis and Visualization

   Summarize a dataset, look at distributions and relationships, and draw clear charts with matplotlib.

   10 min read

5. 5

   Data Science with Python Module 5: Statistics for Data Science

   Learn distributions, sampling, confidence intervals, and hypothesis tests, and run them with SciPy.

   10 min read

6. 6

   Data Science with Python Module 6: Machine Learning with scikit-learn

   Train and test a regression model and a classifier with scikit-learn, and combine preprocessing and a model in a pipeline.

   11 min read

7. 7

   Data Science with Python Module 7: Evaluating and Improving Models

   Choose the right metrics, use cross-validation, tune hyperparameters, and avoid data leakage and overfitting.

   10 min read

8. 8

   Data Science with Python Module 8: From Notebook to a Finished Project

   Structure a project, save and reload a model, write a prediction function, and communicate results clearly.

   9 min read