Module 1: Introduction to Data Science and Analytics
Unit 1.1: Overview of Data Science and Analytics
Unit 1.2: Role and Responsibilities of a Data Scientist
Unit 1.3: Applications of Data Science in Various Industries
Unit 1.4: Ethical Considerations in Data Science
Module 2: Fundamentals of Statistics and Mathematics for Data Science
Unit 2.1: Descriptive Statistics and Data Visualization
Unit 2.2: Probability Distributions and Hypothesis Testing
Unit 2.3: Linear Algebra and Calculus for Data Science
Unit 2.4: Optimization Techniques in Data Science
Module 3: Data Collection and Cleaning
Unit 3.1: Data Collection Methods and Sources
Unit 3.2: Data Cleaning and Preprocessing Techniques
Unit 3.3: Handling Missing Data and Outliers
Unit 3.4: Exploratory Data Analysis (EDA)
Module 4: Data Wrangling and Feature Engineering
Unit 4.1: Data Transformation and Reshaping
Unit 4.2: Feature Scaling and Selection
Unit 4.3: Handling Categorical Data
Unit 4.4: Time Series Analysis and Feature Extraction
Module 5: Machine Learning for Data Science
Unit 5.1: Supervised Learning Algorithms (Regression, Classification)
Unit 5.2: Unsupervised Learning Algorithms (Clustering, Dimensionality Reduction)
Unit 5.3: Model Evaluation and Cross-Validation
Unit 5.4: Ensemble Learning and Model Stacking
Module 6: Big Data Technologies
Unit 6.1: Introduction to Big Data and Distributed Computing
Unit 6.2: Hadoop and MapReduce
Unit 6.3: Apache Spark and SparkSQL
Unit 6.4: Real-time Data Processing with Kafka
Module 7: Data Visualization and Interpretation
Unit 7.1: Data Visualization Principles and Tools
Unit 7.2: Building Interactive Dashboards
Unit 7.3: Communicating Data Insights Effectively
Unit 7.4: Storytelling with Data
Module 8: Advanced Analytics Techniques
Unit 8.1: Predictive Modeling and Regression Analysis
Unit 8.2: Time Series Forecasting
Unit 8.3: Text Analytics and Natural Language Processing (NLP)
Unit 8.4: Anomaly Detection and Fraud Analytics
Module 9: Ethics and Privacy in Data Science
Unit 9.1: Responsible Data Collection and Use
Unit 9.2: Privacy and Security Concerns
Unit 9.3: Bias and Fairness in Machine Learning Models
Module 10: Capstone Project
