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Python for Data Science: 85+ Hands-On Coding Exercises
New
4 students
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
Created bySoundhar K
Last updated 10/2026
English
What you'll learn
- 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.
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Coding Exercises
This course includes our updated coding exercises so you can practice your skills as you learn.
See a demo
Course content
7 sections • 6 lectures • 29m total length
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.
Description
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?
You’re not alone.
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.
Not the perfect student who already knows NumPy and Pandas inside out… but the person who is tired of feeling left behind, who wants to finally understand data science, and who is ready to prove to themselves that they can do hard things.
Here, you won’t just watch someone else code. You will write the code yourself — one clear, practical coding exercise at a time. From your very first NumPy array to cleaning real-world data with Pandas, from building your first machine learning model with Scikit-Learn to finishing three complete projects you can proudly show anyone.
By the end of this journey, something quiet but powerful will change:
You will stop saying “I don’t know data science.”
You will start saying “I can do this.”
This is not just another Python course. It is a step-by-step path from self-doubt to real confidence — built through 86 hands-on coding exercises covering NumPy, Pandas, SymPy, and Scikit-Learn, plus three full capstone projects: Retail Sales Analysis, Customer Churn Prediction, and Property Price Prediction.
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.
Let’s begin.
Who this course is for:
- Aspiring data analysts, data scientists, Python developers, and career switchers who want practical, hands-on coding exercises in NumPy, Pandas, SymPy, and scikit-learn. Perfect for beginners who prefer learning by doing rather than long theory lectures. Also ideal for students and professionals preparing for data science interviews or real projects.