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1. Introduction to Data Science
- Basic concepts and the role of data today
- Life cycle of a Data Science project
- Ethics and privacy in data management
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2. Fundamentals of Statistics for Data Science
- Basics; population, sample, and statistical parameters
- Variables and measurement scales
- Sampling, Methods, and Bias
- Descriptive statistics
- Probability and distributions
- Statistical inference
- Correlation and regression
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3. Programming in R Language
- Working environment and basics
- Data manipulation; import, export, cleaning, and processing
- Viewing and Creating Charts
- Descriptive Statistics in R
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4. Case Study
- Problem statement and objectives
- Data collection and preparation
- Data analysis using R
- Visualizing Results and Generating Reports
- Interpretation of results, limitations, and improvements
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