Data Science
- Introduction to Data Science concepts, career roles, and tools
- Mathematics for Data Science: statistics, probability, and linear algebra
- Python/R for data science: data structures, analysis, and visualization
- Data wrangling: cleaning, transforming, and preparing data
- Exploratory Data Analysis (EDA) and hypothesis testing
- Machine Learning basics: supervised and unsupervised learning, model selection
- Regression, classification, clustering and dimensionality reduction techniques
- Data visualization best practices: Matplotlib, Seaborn, Tableau basics
- Project cycle: from problem statement to delivery
- Capstone projects: building and deploying data science models
This course is ideal for those starting a career in data science. You’ll acquire hands-on skills in Python, explore statistical analysis, learn the basics of machine learning, and assemble end-to-end data pipelines to solve real-world problems.