Programme Parameters
Graduate / Intermediate
8 Weeks (80 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Program offered DSTC is designed to equip participants with advanced skills in data visualization and analysis using Tableau. The program covers essential topics such as creating interactive dashboards, integrating data from multiple sources, and utilizing Tableau for business intelligence to drive data-informed decisions. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Program offered DSTC is designed to equip participants with advanced skills in data visualization and analysis using Tableau. The program covers essential topics such as creating interactive dashboards, integrating data from multiple sources, and utilizing Tableau for business intelligence to drive data-informed decisions.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Introduction to Tableau and Data Visualization Basics
Getting Started with Tableau : Installing and navigating the Tableau interface, understanding Tableau’s capabilities. • Introduction to Data Visualization : Principles of effective data visualization, types of charts and when to use them. • Connecting to Data Sources : Importing data from various sources like Excel, CSV, and databases.
Data Preparation and Cleaning in Tableau
Data Connection and Preparation : Handling data sources, joining, blending, and pivoting data. • Data Cleansing Techniques : Filtering, renaming, and working with metadata for clean, usable data. • Basic Calculations and Data Aggregation : Applying simple calculations and aggregations, understanding calculated fields.
Building Basic Visualizations
Core Chart Types in Tableau : Creating bar charts, line charts, scatter plots, and heat maps. • Tables, Cross Tabs, and Text Visuals : Displaying data in tables, using crosstabs for analysis. • Enhancing Visuals with Labels and Tooltips : Adding contextual information to visualizations for clarity.
Advanced Visualizations and Customization
Advanced Chart Types : Funnel charts, Pareto charts, bullet graphs, and waterfall charts. • Geographic Mapping : Creating maps, customizing geographic data, and using maps in BI contexts. • Using Parameters and Control Elements : Dynamic filtering, input controls, and parameter-driven visualizations.
Interactive Dashboards and Storytelling
Dashboard Design and Layout : Principles of dashboard layout, adding interactive elements. • Dashboard Actions : Using filters, highlight actions, and URL actions for interactivity. • Storytelling with Data : Creating a story with Tableau, navigating through steps and enhancing narrative flow.
Data Analysis and Calculations in Tableau
Advanced Calculations : Using calculated fields, date functions, and logic statements. • Table Calculations and Level of Detail (LOD) Expressions : Applying table calculations, creating LOD expressions for deeper analysis. • Trend Lines, Forecasting, and Statistical Analysis : Adding trend lines, forecasting future values, and interpreting statistical summaries.
Performance Optimization and Best Practices
Data Optimization Techniques : Managing large datasets, using extracts, and optimizing data connections. • Dashboard Performance Optimization : Improving load times and performance with best practices. • Publishing and Sharing : Publishing dashboards to Tableau Public and Tableau Server, best practices for sharing.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
| Covered Tool / Platform | Tableau |
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