Why this course?

Description

What you'll learn
●    You will explore the goals, overview, definition, and categories of business visualization. The stakeholders interested in business analysis and the primary objectives of business visualizations.
●    You will be able to explore key concepts in business visualization, including the foundational principles and applications of business data preparation, business data blending, and dashboard creation for business analysis. Part 1 covers the basics of designing effective business worksheets, enhancing user interaction, and implementing calculated fields in business workbooks.
●    Explore business visualization assessments, understanding various types of business analysis (e.g., business data preparation, calculated fields in business), and how business analysts apply and interpret their findings to improve dashboard effectiveness and user performance in business workbooks.
●    Explore business visualization assessments related to various business dashboard scenarios, including evaluating current and historical business performance data, understanding the impact of visualization factors on user behavior and business outcomes, and how business analysts address and present their findings to improve worksheet design, performance metrics, and overall dashboard effectiveness.
●    This training will be useful if your role involves conducting business visualization assessments, including evaluating business performance data, designing effective business worksheets, and preparing detailed business dashboards to enhance dashboard effectiveness and user development.
●    Discover how to gain knowledge in conducting business visualization assessments, understanding various evaluation techniques for business performance and user behavior, and recognizing the limitations and challenges in applying business visualization practices to improve dashboard outcomes and user effectiveness.


1.Assignment: Data Collection and Management
Objective: To assess students' understanding of fundamental data collection and management concepts and their ability to apply these concepts to real-world data scenarios.
2.Project:Enhancing Data Collection and Management for Improved Business Analytics
Objective: The primary objective of this project is to design and implement an effective data collection and management system that enhances the accuracy, efficiency, and reliability of business analytics. This system aims to streamline data collection processes, ensure high data quality, and facilitate seamless integration and management of data from various sources.
3.Assignment: Predictive Analytics
Objective: To assess students' understanding of Predictive Analytics concepts and their ability to analyze data and implement predictive models effectively.
4.Project: Implementing Predictive Analytics to Optimize Customer Churn Prediction
Objective: In this project, the focus is on optimizing customer churn prediction through predictive analytics.
5.Assignment: Data Visualization with Python & Tableau
Objective: To evaluate students' understanding of fundamental data visualization concepts and their ability to apply these concepts to real-world data scenarios using Python and Tableau.
6.Project:Analyzing Sales Data with Python and Tableau
Objective: To analyze and visualize sales data to identify trends, patterns, and key metrics using Python for data preparation and Tableau for interactive visualization.
7.Assignment: Example Workflow
Objective:To assess students' understanding of predictive analytics concepts and their ability to analyze data and implement predictive models effectively, the evaluation will involve a workflow in descriptive analytics. 
8.Project: Implementing Predictive Analytics to Optimize Customer Churn Prediction
Objective: In this project, the focus is on optimizing customer churn prediction through predictive analytics. Initially, key predictors influencing customer churn are identified by analyzing historical data, which reveals the factors most strongly associated with churn. Predictive models are then developed and evaluated to accurately forecast which customers are likely to churn. Using insights gained from these models, targeted retention strategies are designed and implemented to address the specific needs and behaviors of at-risk customers. Finally, the effectiveness of both the predictive models and the retention strategies is assessed by measuring their impact on reducing churn rates and improving overall customer retention.