Module 1: Introduction to Data Science

• What is Data Science?
• Data Science lifecycle and workflow
• Role of Data Scientist and Data Analyst
• Applications of Data Science
• Types of data and data sources
• Real-world Data Science examples

Module 2: Python for Data Science

• Introduction to Python
• Variables, data types and operators
• Conditional statements and loops
• Functions and modules
• Lists, tuples, sets and dictionaries
• Introduction to NumPy and Pandas

Module 3: Mathematics and Statistics for Data Science

• Basic mathematical concepts
• Mean, median and mode
• Range, variance and standard deviation
• Probability fundamentals
• Correlation and covariance
• Basic statistical concepts

Module 4: Data Collection and Understanding

• Sources of data
• Structured and unstructured data
• Data collection methods
• Importing datasets
• Understanding columns and records
• Identifying data types and patterns

Module 5: Data Cleaning and Preprocessing

• Importance of data cleaning
• Handling missing values
• Removing duplicate records
• Handling incorrect and inconsistent data
• Data type conversion
• Outlier identification and treatment

Module 6: NumPy for Data Science

• NumPy arrays
• Array creation and indexing
• Array operations
• Mathematical operations
• Statistical functions
• Working with multidimensional arrays

Module 7: Pandas for Data Analysis

• Series and DataFrame
• Importing CSV and Excel data
• Selecting and filtering data
• Sorting and grouping data
• Data aggregation
• Merging and joining datasets

Module 8: Exploratory Data Analysis (EDA)

• Introduction to EDA
• Understanding dataset structure
• Univariate analysis
• Bivariate and multivariate analysis
• Identifying patterns and relationships
• Generating useful insights from data

Module 9: Data Visualization

• Importance of data visualization
• Matplotlib fundamentals
• Seaborn fundamentals
• Bar charts and line charts
• Histograms and box plots
• Scatter plots and correlation visualization
• Creating meaningful data reports

Module 10: SQL for Data Science

• Introduction to databases
• SQL fundamentals
• SELECT, WHERE and ORDER BY
• GROUP BY and aggregate functions
• Joins and subqueries
• Extracting data for analysis

Module 11: Machine Learning Fundamentals

• Introduction to Machine Learning
• AI vs Machine Learning vs Data Science
• Supervised and unsupervised learning
• Training and testing datasets
• Features and target variables
• Machine Learning workflow

Module 12: Supervised Learning

• Introduction to supervised learning
• Linear regression
• Multiple regression
• Logistic regression
• Classification concepts
• Decision tree fundamentals
• Model training and prediction

Module 13: Classification and Clustering

• Classification problems
• K-Nearest Neighbors
• Decision Tree
• Random Forest fundamentals
• K-Means clustering
• Customer segmentation
• Practical classification and clustering

Module 14: Model Evaluation

• Training and testing models
• Accuracy, precision and recall
• F1-score
• Confusion matrix
• Mean Absolute Error and Mean Squared Error
• Cross-validation concepts
• Comparing different models

Module 15: Feature Engineering

• Importance of features
• Feature selection
• Feature transformation
• Encoding categorical data
• Feature scaling
• Creating useful features
• Reducing unnecessary variables

Module 16: Advanced Data Science Concepts

• Ensemble learning concepts
• Hyperparameter tuning
• Model optimization
• Dimensionality reduction
• Introduction to PCA
• Predictive analytics
• Introduction to time-series analysis

Module 17: Data Science Tools and Libraries

• Jupyter Notebook
• Google Colab
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• Working with practical datasets

Module 18: Business Intelligence and Data-Driven Decision Making

• Understanding business data
• KPI and performance analysis
• Sales and customer analytics
• Data-driven decision making
• Creating analytical reports
• Introduction to dashboards
• Presenting insights to stakeholders

Module 19: Practical Data Science Projects

• Student performance analysis
• Sales data analysis
• Customer segmentation
• House price prediction
• Employee data analysis
• Customer churn prediction
• Sentiment analysis
• Business performance analysis

Module 20: Final Data Science Project & Assessment

• Selection of a real-world dataset
• Data collection and cleaning
• Exploratory data analysis
• Data visualization
• Machine Learning model development
• Model evaluation
• Final report and presentation
• Practical assessment