Data Driven Digital Analytics

This module, Business Analytics Using Tableau, equips learners with the skills to harness Tableau, a leading data visualization software, to transform complex data into intuitive insights. Tableau’s ability to connect with various data sources, from Excel sheets to complex databases like Oracle or Amazon Web Services, offers unparalleled versatility. Designed for users of all skill levels, this module ensures that no programming expertise is needed to create impactful visualizations and dashboards.

Participants will delve into the core principles of Tableau, including data preparation, visualization techniques, and storytelling with data. Additionally, the module emphasizes advanced Tableau features, such as predictive analytics, calculated fields, and dynamic dashboards, fostering deeper insights and decision-making capabilities. Learners will progress from fundamental visualizations to complex dashboards and gain practical knowledge to integrate predictive analytics and statistical models into their Tableau workflows.

By the end of this module, learners will be able to:

• Understand the latest trends and frameworks in data visualization and business analytics.

• Utilize Tableau to prepare and manage data efficiently for visualization purposes.

• Master various types of Tableau charts and best practices in visualization design.

• Design dynamic dashboards and interactive data stories tailored to specific audiences.

• Apply Tableau's predictive analytics features, such as trend lines, clustering, and forecasting.

• Create calculated fields to derive insights and enhance analytical capabilities.

• Explore advanced data storytelling techniques to drive impactful decision-making.

• Integrate external statistical models with Tableau for enhanced analysis and actionable insights.

Topic 1: Introduction to Data-Driven Digital Analytics

Topic 2: Foundations of Big Data and Data Science

Topic 3: Business Intelligence (BI) and Its Applications

Topic 4: Data Mining Techniques and RFM Analysis

Topic 5: Understanding Machine Learning and CRISP-DM Framework

 Topic 6: Data Quality, Preparation and Visualization

 Topic 7: Machine Learning Modelling

Topic 8: Supervised Vs Unsupervised Learning