- What is data science?
- Applications of data science
- Data science lifecycle overview
- Sources of data
- Data cleaning techniques
- Handling missing and inconsistent data
- Descriptive statistics
- Data visualization with matplotlib, seaborn
- Identifying patterns and outliers
- Probability basics
- Hypothesis testing
- Regression analysis
- Supervised vs unsupervised learning
- Popular algorithms overview
- Model training and evaluation
- Decision trees and random forests
- Support vector machines
- Neural networks basics
- Introduction to Hadoop & Spark
- Data storage & retrieval
- Distributed computing basics
- Building a predictive model
- Data analysis case study
- Capstone project