Python for Data Science: 85+ Hands-On Coding Exercises

Master NumPy, Pandas, SymPy & Scikit-Learn with Real Capstones –Retail Sales, Churn Prediction & Property Price Modeling

Course Curriculum

• Master NumPy & Pandas essentials: create arrays, clean data, filter, group, pivot, merge, and reshape real-world datasets with confidence.

• Build intermediate Pandas skills: rolling averages, resampling, IQR outlier removal, one-hot encoding, time features, and multi-file stacking.

• Apply scikit-learn end-to-end: scaling, imputation, train-test split, linear/logistic regression, Random Forest, pipelines, and cross-validation.

• Solve symbolic math with SymPy and complete 3 real capstones: Retail Sales Analysis, Customer Churn Prediction, and Property Price Prediction.

• Write clean, vectorized Python code and evaluate models with R², RMSE, accuracy, precision, recall, and confusion matrices.

• Beginner NumPy & DataFrame Essentials 6:01

• Pandas: DataFrames, Reading & Cleaning 5:47

• Intermediate Pandas 4:30

• SymPy for Data Science 4:29

• Scikit-Learn: Preprocessing to Pipelines 6:42

• End-to-End Employee Modeling 2:10

Compatibility & Requirements

• Basic Python knowledge (variables, lists, loops, functions) is helpful but not mandatory. No prior data science or machine learning experience required. A computer with internet and free Google Colab or local Python + Jupyter is enough.

• Have you ever felt like data science is only for “smart people” who already know everything?

• Have you opened a Python tutorial, watched for twenty minutes, and then closed the tab because everything still felt confusing and overwhelming?

• Most of us start with the same quiet fear: “What if I’m not good enough for this?”

• This course was created for that version of you.

• By the end of this journey, something quiet but powerful will change:

• You will stop saying “I don’t know data science.”

• If you’ve been waiting for the right moment to start your data science journey… this is it.

• Your future self is already proud of you for taking this step.