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Data Analysis: Syllabus
Mastering Data Analysis & Intelligence: Concepts, Techniques & Best Practices
Data Analysis and Business Intelligence (BI) are crucial for organizations to make data-driven decisions. This book provides a hands-on approach to mastering data analysis, reporting, and dashboarding techniques using SQL, Power BI, Tableau, and Python. It covers key methodologies for extracting insights, defining KPIs, and automating business reporting processes.
Module 1: Introduction to Data Analysis & Business Intelligence
- What is Data Analysis and Business Intelligence?
- Differences between Data Analysts and BI Specialists
- The role of SQL in data analysis and BI
- Understanding the modern data stack (Data Warehouses, ETL, Visualization Tools)
Module 2: Data Collection & Storage for Analysis
- Data Warehouses vs. Data Lakes (Snowflake, Redshift, BigQuery)
- Understanding OLAP vs. OLTP databases
- Connecting BI tools to databases (SQL Server, PostgreSQL, MySQL)
- Best practices for structuring data for analysis
Module 3: SQL for Data Analysis & Reporting
- Writing SQL queries for data retrieval
- Filtering, aggregations, and joins for insights
- Using CTEs, Window Functions, and Subqueries
- Query optimization for efficient reporting
- Automating reports with SQL views and stored procedures
Module 4: Data Visualization & Dashboarding
- Principles of effective data visualization
- Creating interactive dashboards in Power BI and Tableau
- Designing KPI-driven dashboards for business insights
- Customizing visualizations for different audiences
Module 5: Statistical Analysis & Data Interpretation
- Descriptive vs. Inferential statistics in data analysis
- Identifying trends, correlations, and outliers
- Using Python (Pandas, NumPy, Seaborn) for statistical analysis
- Time-series forecasting techniques for business decisions
Module 6: Business Intelligence & KPI Measurement
- Defining Key Performance Indicators (KPIs)
- Building automated reports for executive decision-making
- Implementing KPI tracking with BI tools
- Case studies on KPI-driven business decisions
Module 7: Data Automation & Reporting Workflows
- Automating data pipelines with SQL and ETL tools
- Scheduling automated reports using Power BI & Tableau
- Integrating BI tools with cloud-based data storage
- Implementing alert-based reporting for critical KPIs
Module 8: Real-World Case Studies in Data Analysis & Intelligence
- Analyzing customer behavior for e-commerce businesses
- Sales performance tracking and revenue forecasting
- Fraud detection using statistical methods
- Workforce and HR analytics for business planning
Hands-On Examples & Best Practices
Example 1: Building a Sales Performance Dashboard
- Writing SQL queries to retrieve sales data
- Creating KPIs for revenue, profit margin, and growth
- Designing an interactive dashboard in Power BI
Example 2: Customer Segmentation Analysis with SQL & Python
- Querying customer transaction data
- Using Python to perform clustering analysis
- Visualizing customer segments in Tableau
Example 3: Automating a Weekly Financial Report
- Creating an SQL query for finance data extraction
- Automating the report generation in Power BI
- Sending automated alerts for anomalies
Example 4: Predictive Analytics for Demand Forecasting
- Using time-series forecasting techniques
- Building a predictive model with Python
- Visualizing forecasted trends for stakeholders
Example 5: Real-Time Data Monitoring with BI Dashboards
- Connecting BI tools to a live data source
- Implementing real-time tracking of KPIs
- Designing an alert-based reporting system