What You'll Learn
- Write clean, Pythonic code for data analysis tasks
- Wrangle messy datasets with Pandas and NumPy
- Visualize data with Matplotlib, Seaborn, and Plotly
- Build and evaluate supervised ML models with Scikit-Learn
- Complete 5 real-world projects with datasets from healthcare, finance, and e-commerce
- Deploy a machine learning model as a web API
Course Curriculum
3 sections · 14 lessons · 42h
Python Foundations5 lessons
- Why Python for Data Science?Free preview8m
- Variables, Types & Control FlowFree preview16m
- Functions, Modules & OOP Basics18m
- Python Practice Lab
- Python Foundations Quiz
Data Wrangling & Visualization5 lessons
- NumPy Arrays & Vectorized Operations20m
- Pandas DataFrames: Load, Clean & Merge24m
- Exploratory Data Analysis (EDA)22m
- Visualization with Matplotlib & Seaborn18m
- Data Wrangling Quiz
Machine Learning with Scikit-Learn4 lessons
- Supervised Learning: Regression & Classification25m
- Model Evaluation: Cross-Validation & Metrics20m
- Project: Predicting Patient Readmission
- ML Fundamentals Quiz
Requirements
- No programming experience required
- Basic math and statistics (high school level)
- A computer with Python installed (setup guide included)
Who This Course Is For
- Career changers entering data science
- Analysts wanting to level up from Excel to Python
- Students preparing for data science job interviews
Your Instructor
NT
Nadia Torres
- Rating
- 4.8
- Students
- 11,000 students
- Courses
- 5 courses
Senior Data Scientist with experience at a major healthcare analytics firm and two AI startups. PhD in Applied Statistics from UC Berkeley.