Foundations of Data Science (2 weeks)

1.	Introduction to Data Science
•	Definition and scope
•	Applications in various industries

2.	Python for Data Science
•	Basics of Python programming
•	Libraries: NumPy, Pandas, Matplotlib

  Data Manipulation and Cleaning (3 weeks)

• Data Acquisition
•	Importing data from various sources
•	APIs and web scraping

• Data Cleaning and Preprocessing
•	Handling missing values
•	Data normalization and standardization

  Data Manipulation and Cleaning (3 weeks)

• Data Acquisition
•	Importing data from various sources
•	APIs and web scraping

• Data Cleaning and Preprocessing
•	Handling missing values
•	Data normalization and standardization

  Exploratory Data Analysis (2 weeks)

• Descriptive Statistics
•	Measures of central tendency and dispersion
•	Visualization techniques

• Statistical Analysis with Python
•	Hypothesis testing
•	Correlation and regression analysis

  Machine Learning Fundamentals (4 weeks)

• Introduction to Machine Learning
•	Supervised vs. unsupervised learning
•	Types of machine learning algorithms
•	Model Training and Evaluation
•	Splitting datasets
•	Cross-validation

• Supervised Learning Algorithms
•	Linear regression, logistic regression
•	Decision trees, random forests

• Unsupervised Learning Algorithms
•	Clustering (K-means, hierarchical)
•	Dimensionality reduction (PCA)

  Advanced Topics (3 weeks)

• Feature Engineering
•	Importance and techniques

• Deep Learning
•	Neural networks basics
•	Introduction to TensorFlow or PyTorch

• Model Deployment
•	Basics of deploying machine learning models
•	Basics of Cloud services (AWS, Azure)

  Capstone Project (4 weeks)

• Capstone Project
•	Apply learned skills to a real-world problem (Kaggle)
•	Presentation and documentation

  Introduction to Data Analysis (2 weeks)

• Introduction to Data Analysis
•	Definition and importance in decision-making
•	Applications across industries

• Excel for Data Analysis
•	Basics of Excel
•	Data cleaning and manipulation using Excel functions

  Data Visualization (3 weeks)

• Principles of Data Visualization
•	Best practices in visualizing data
•	Common pitfalls to avoid

• Graphs and Charts with Tableau
•	Introduction to Tableau
•	Creating interactive visualizations

  Data Cleaning and Wrangling (2 weeks)

• Data Cleaning Techniques
•	Identifying and handling missing data
•	Dealing with outliers

• Data Wrangling with SQL
•	Basic SQL queries for data extraction
•	Joining and aggregating data

  Statistical Analysis (4 weeks)

• Descriptive Statistics
•	Measures of central tendency and dispersion
•	Frequency distributions

• Inferential Statistics with Python
•	Introduction to statistical hypothesis testing
•	Confidence intervals

• A/B Testing
•	Designing experiments
•	Analyzing A/B test results

  Advanced Data Analysis Techniques (3 weeks)

• Regression Analysis
•	Simple and multiple regression
•	Interpretation of regression coefficients

• Introduction to Machine Learning for Data Analysts
•	Overview of machine learning algorithms
•	Application of machine learning in data analysis

• Time Series Analysis with Python
•	Basics of time series data
•	Forecasting techniques

  Statistical Analysis (4 weeks)

• Capstone Project
•	Analyzing and interpreting a real-world dataset
•	Creating a comprehensive data analysis report