Udemy
Practical Data Science using Python
What this course answers
A 31-hour, project-based course in data science with Python: statistics, exploratory data analysis, NumPy, Pandas and visualisation, then regression, classification, clustering, PCA, feature engineering and hyperparameter tuning with scikit-learn and statsmodels. It ends with deep neural networks in TensorFlow/Keras and deploying models with FastAPI and Streamlit.
Last verified25 Sep 2026
What you will be able to do
- Data science concepts, use cases, life cycle and methodologies
- Exploratory data analysis (EDA) and statistical techniques
- Python for data science and machine learning
- Linear regression and classification algorithms
- K-means clustering and dimensionality reduction with PCA
- Feature engineering, hyperparameter tuning and grid-search cross-validation
- An introduction to deep neural networks
Curriculum
Prerequisites
- Some exposure to a programming language is useful