Data Analytics

Module 1: Introduction to Data Analytics

• What is Data Analytics?
• Importance of Data Analytics in Business and Industry
• Difference Between Data Analytics, Data Science, and Business Intelligence
• Data Analytics Lifecycle
• Key Tools for Data Analytics (Excel, SQL, Python, Tableau, Power BI)

Module 2: Basics of Statistics and Mathematics for Data Analytics

• Fundamentals of Descriptive Statistics
  • Measures of Central Tendency (Mean, Median, Mode)
  • Measures of Dispersion (Variance, Standard Deviation, Range)
• Probability Basics and Distribution (Normal, Binomial, Poisson)
• Inferential Statistics (Confidence Intervals, Hypothesis Testing, p-values)
• Correlation and Regression Analysis

Module 3: Data Collection and Preprocessing

• Types of Data: Structured vs. Unstructured Data
• Data Sources: Databases, APIs, Web Scraping, IoT Devices
• Handling Missing Data, Outliers, and Duplicate Values
• Data Cleaning Techniques and Tools (Pandas, OpenRefine)
• Data Transformation, Encoding, and Feature Engineering

Module 4: SQL for Data Analytics

• Basics of SQL and Relational Databases
• Data Extraction Using SELECT, WHERE, GROUP BY, HAVING
• Joins and Subqueries
• Window Functions and Aggregate Functions
• Data Manipulation and Performance Optimization

Module 5: Data Visualization and Reporting

• Importance of Data Visualization in Analytics
• Introduction to Data Visualization Tools: Tableau, Power BI, Excel
• Creating Charts and Dashboards
• Data Storytelling Techniques
• Best Practices for Effective Data Visualization

Module 6: Business Analytics and Intelligence

• Introduction to Business Intelligence (BI)
• Key Performance Indicators (KPIs) and Metrics
• Market Basket Analysis and Customer Segmentation
• Predictive and Prescriptive Analytics
• Case Studies in Retail, Healthcare, Finance, and Marketing

Module 7: Excel for Data Analysis

• Data Cleaning and Formatting in Excel
• Pivot Tables and Pivot Charts
• Data Validation and Conditional Formatting
• Excel Functions for Data Analytics (VLOOKUP, INDEX-MATCH, IF, COUNTIF, etc.)
• Data Analysis ToolPak in Excel

Module 8: Python for Data Analytics

• Introduction to Python for Data Analysis
• Data Handling with Pandas and NumPy
• Data Visualization with Matplotlib and Seaborn
• Exploratory Data Analysis (EDA) with Python
• Automating Data Analysis Tasks

Module 9: Machine Learning for Data Analytics

• Basics of Machine Learning and Its Role in Data Analytics
• Supervised vs. Unsupervised Learning
• Regression and Classification Models
• Clustering Techniques (K-Means, Hierarchical Clustering)
• Model Evaluation and Interpretation

Module 10: Big Data Analytics and Cloud Computing

• Introduction to Big Data and Its Challenges
• Hadoop and Apache Spark for Data Processing
• Cloud-Based Analytics Solutions (AWS, Google Cloud, Azure)
• Real-Time Data Processing and Stream Analytics

Module 11: Data Ethics, Privacy, and Governance

• Importance of Data Security and Privacy
• GDPR and Other Data Protection Regulations
• Ethical Issues in Data Analytics
• Data Governance Frameworks and Compliance

Module 12: Real-World Applications and Capstone Project

• Applying Data Analytics to Real-World Business Problems
• Case Studies in Various Industries
• Hands-on Data Analytics Project
• Creating Reports and Presenting Insights