1. Overview of Data Science

• Definition and Importance
• Evolution of Data Science
• Role of Data Scientist in Various Industries
• Data Science Workflow (CRISP-DM, KDD)

2. Data Science Applications

• Finance
• Healthcare
• Marketing
• E-commerce
• Engineering

3. Data Science Tools and Ecosystem

• Overview of Tools (Python, SQL, etc.)
• Introduction to Jupyter Notebook, Anaconda

1. Introduction to Python Programming

• Installation and Setup (Anaconda, Jupyter Notebook)
• Python Syntax, Variables, and Data Types
• Control Flow: Conditional Statements and Loops
• Functions and Modules in Python

2. Python Libraries for Data Science

• *NumPy*: Arrays, Mathematical Operations, Broadcasting
• *Pandas*: DataFrames, Series, Data Manipulation
• *Matplotlib and Seaborn*: Data Visualization, Plotting Graphs, Histograms, Bar Charts

3. Advanced Python Concepts

• List and Dictionary Comprehensions
• Lambda Functions, Map, Filter, and Reduce

1. Descriptive Statistics

• Measures of Central Tendency (Mean, Median, Mode)
• Measures of Dispersion (Variance, Standard Deviation, Range, IQR)
• Skewness and Kurtosis
• Visualization Techniques (Box Plot, Histogram)

2. Probability Theory

• Basic Probability Concepts (Sample Space, Events)
• Conditional Probability, Bayes Theorem
• Random Variables and Distributions (Normal, Binomial, Poisson)

3. Inferential Statistics

• Estimation (Point Estimation, Confidence Intervals)
• Central Limit Theorem
• Z-test, t-test, Chi-square test

1. Introduction to SQL

• Basic SQL Commands (SELECT, INSERT, UPDATE, DELETE)
• Filtering and Sorting Data (WHERE, ORDER BY, LIMIT)
• Aggregate Functions (SUM, AVG, COUNT, MIN, MAX)

2. Joins and Subqueries

• Types of Joins (INNER, LEFT, RIGHT, FULL)
• Subqueries and Nested Queries
• Using Group By and Having Clauses

3. Advanced SQL Concepts

• Window Functions (ROW_NUMBER, RANK, PARTITION BY)
• Common Table Expressions (CTEs)
• Working with Databases and Handling Missing Data

4. SQL in Python

• Integrating SQL with Python using libraries like sqlite3 and SQLAlchemy
• Querying Databases from Python Notebooks

1. Introduction to Time Series

• Definition and Importance of Time Series

• Time Series Components (Trend, Seasonality, Noise)

• Time Series Plotting and Visualization

2. Time Series Decomposition

• Additive and Multiplicative Models
• Moving Averages and Exponential Smoothing
• Seasonal Decomposition of Time Series (STL Decomposition)

1. Fundamentals of Hypothesis Testing

• Null and Alternative Hypothesis
• Type I and Type II Errors
• P-value and Statistical Significance
• One-tailed vs Two-tailed Tests

2. Types of Hypothesis Tests

• Z-test and T-test for Means
• Chi-Square Test for Independence

3. A/B Testing and Business Applications

• Understanding A/B Testing
• Designing Experiments
• Analyzing Results and Drawing Conclusions