Module 1: Introduction to Data Visualization & SQL

Duration: 5 Hours
Objective: Establish foundational knowledge of SQL and data visualization concepts.

• Data Visualization Overview

  • Importance of data visualization for business and decision-making: communicating insights, spotting trends, improving communication.

  • Key visualization types: line charts, bar charts, scatter plots, pie charts, histograms.

• Introduction to SQL

  • Basic SQL for querying databases: SELECT, FROM, WHERE clauses.

  • Filtering and aggregating data: SUM, COUNT, GROUP BY, WHERE clauses.

• Types of Data

  • Overview of common datasets: time-series data, transaction data, sensor data, log files.

Module 2: Power BI for Data Visualization

Duration: 6 Hours
Objective: Learn to use Power BI for visualizing and analyzing data.

• Power BI Overview

  • Connecting Power BI to various data sources: databases, spreadsheets, cloud services.

  • Data preparation using Power Query: cleaning, transforming, and structuring data.

• Basic Power BI Visualizations

  • Building common visualizations: bar charts, line charts, pie charts, and scatter plots.

  • Creating interactive dashboards: filters, slicers, and drill-through functionality.

• Publishing and Sharing Reports

  • Publishing reports to Power BI Service.

  • Setting up scheduled data refreshes.

  • Sharing reports and collaborating with teams.

Module 3: Tableau for Data Visualization

Duration: 6 Hours
Objective: Learn how to create interactive and compelling data visualizations with Tableau.

• Tableau Overview

  • Connecting to different data sources: Excel, SQL databases, cloud services.

  • Data preparation in Tableau: cleaning, blending, and transforming data.

• Creating Visualizations in Tableau

  • Building basic visualizations: bar charts, line charts, histograms, and scatter plots.

  • Adding interactivity to visualizations: filters, tooltips, and actions.

• Creating Dashboards

  • Combining multiple visualizations into dashboards.

  • Creating interactive dashboards with drop-downs, parameters, and actions.

Module 4: Advanced SQL for Data Preparation & Analysis

Duration: 7 Hours
Objective: Learn advanced SQL techniques for transforming and analyzing complex datasets.

• Advanced SQL Queries

  • JOINs (INNER, LEFT, RIGHT) for combining data from multiple tables.

  • Subqueries and Common Table Expressions (CTEs) for complex reporting.

• Working with Time-Series Data

  • Handling time-series data: calculating rolling averages, growth rates, and trends.

  • Window functions: running totals, moving averages, and lag/lead analysis.

• Optimizing SQL Queries

  • Performance tuning: indexing, query optimization techniques for large datasets.

Module 5: Data Analysis & Forecasting Techniques

Duration: 6 Hours
Objective: Learn statistical and forecasting methods to analyze trends and make predictions.

• Introduction to Forecasting

  • Time-series forecasting methods: moving averages, exponential smoothing, ARIMA models.

  • Forecasting tools in Power BI and Tableau.

• Predictive Analytics

  • Introduction to regression analysis: linear regression, multiple regression.

  • Implementing regression models in Power BI/Tableau for trend analysis and prediction.

Module 6: Integrating Data from Multiple Sources

Duration: 6 Hours
Objective: Learn how to integrate and consolidate data from different systems into BI tools for unified reporting.

• Connecting Multiple Data Sources

  • Combining data from different sources: databases, Excel files, cloud services, APIs.

  • Data blending techniques: combining disparate data sources in Tableau and Power BI.

• Data Transformation

  • Using Power Query in Power BI to clean, transform, and load data.

  • Data wrangling: handling missing values, outliers, and inconsistent data.

• Creating Unified Dashboards

  • Building consolidated dashboards from multiple data sources: sales, finance, operations, etc.

Module 7: Geospatial Analysis & Visualization

Duration: 6 Hours
Objective: Learn how to perform geospatial analysis and create maps using BI tools.

• Introduction to Geospatial Data

  • Working with geographic data: latitude/longitude, shapefiles, and geoJSON.

  • Introduction to mapping features in Power BI and Tableau.

• Creating Maps and Geospatial Visualizations

  • Creating maps in Power BI using Map visuals and ArcGIS integration.

  • Visualizing geographic data in Tableau using built-in mapping features and custom geospatial data.

• Geospatial Analysis Techniques

  • Heat maps, choropleth maps, and symbol maps for visualizing geographic trends.

  • Identifying patterns in location-based data: sales regions, customer density, etc.

Module 8: Real-Life Case Studies for Data Analysis & Visualization

Duration: 6 Hours
Objective: Apply the concepts learned in the course to real-world case studies.

• Case Study 1: Sales Dashboard

  • Build a dashboard to track key sales metrics: total revenue, units sold, and regional performance.

  • Using forecasting techniques to predict future sales trends.

• Case Study 2: Financial Dashboard

  • Create a financial dashboard to track budgets, expenses, and variances.

  • Integrating data from various sources (e.g., ERP systems, spreadsheets) to create a unified report.

• Case Study 3: Customer Analytics Dashboard

  • Build a dashboard to monitor customer behavior, acquisition rates, and churn analysis.

  • Visualizing customer demographics and segmentation using maps and demographic data.

Module 9: Power Sector Integration & Final Project

Duration: 12 Hours
Objective: Integrate all tools (SQL, Power BI, Tableau, GIS) into a comprehensive project focused on the power sector.

• Power Sector Integration Overview

  • SQL + BI Tools Integration: Using SQL to extract, clean, and aggregate power sector data for visualizations.

  • Connecting Real-Time Data: Integrating SCADA systems, IoT sensors, and weather data into Power BI/Tableau for live monitoring of power generation and consumption.

  • Geospatial Data in Power Sector: Mapping and visualizing grid performance, energy production facilities, and distribution networks using GIS and BI tools.

  • Predictive Analytics for Power Sector: Applying forecasting models to predict energy demand and supply, and using regression analysis for energy consumption patterns.

• Final Project: Power Sector BI Dashboard

  • Students will develop a comprehensive BI dashboard for the power sector, integrating real-time data (SCADA, IoT), geospatial data (GIS), and predictive analytics (forecasting energy demand).

  • The project will involve creating visualizations to monitor grid performance, energy production, demand forecasting, and system efficiency.

  • Project Presentation: Students will present their final dashboards, demonstrating the integration of SQL, Power BI, Tableau, GIS, and predictive analytics in a real-world power sector application.

Course Wrap-Up & Certification

• Review of Key Learnings: Recap of core concepts and tools applied throughout the course.

• Q&A Session: Address any remaining questions or areas of interest.

• Course Certification: Awarding of certificates to students upon completion of the final project.