Data Science Basics

---

1. Introduction to Data Science

• What is Data Science
• Importance and application of data science
• Overview of the data science process

2. Introduction to Programming for Data Science

• Python basics (or another language such as R)
• Introduction to libraries like NumPy, Pandas, and Matplotlib

3. Data Collection and Cleaning

• Web scraping
• APIs
• Data cleaning and preprocessing
• Handling missing data

4. Exploratory Data Analysis (EDA)

• Descriptive statistics
• Data visualization
• Data distributions

5. Probability and Statistics for Data Science

• Probability basics
• Distributions
• Hypothesis testing
• Regression analysis
• Bayesian methods

6. Introduction to Machine Learning

• Overview of machine learning
• Supervised learning (classification and regression)
• Unsupervised learning (clustering and dimensionality reduction)
• Evaluation of machine learning models

7. Data Science Tools and Techniques

• Databases and SQL
• Big Data tools (e.g., Hadoop, Spark)
• Version control (e.g., Git)

8. Data Science Projects

• End-to-end data science projects
• Presentation of results
• Storytelling with data

9. Ethics in Data Science

• Privacy and data security
• Bias and fairness