Description
Description
Take the next step in your career as data visualization and storytelling professionals! Whether you’re an up-and-coming data visualization specialist, an experienced data analyst, aspiring data scientist specializing in visualization, or budding storyteller in data-driven narratives, this course is an opportunity to sharpen your data processing and storytelling capabilities, increase your efficiency for professional growth, and make a positive and lasting impact in the field of data visualization and storytelling.
With this course as your guide, you learn how to:
● All the fundamental functions and skills required for data visualization and storytelling.
● Transform knowledge of data visualization applications and techniques, data representation and feature engineering, data analysis and preprocessing, and storytelling techniques.
● Get access to recommended templates and formats for details related to data visualization and storytelling techniques.
● Learn from informative case studies, gaining insights into data visualization and storytelling techniques for various scenarios. Understand how the International Monetary Fund, monetary policy, and fiscal policy impact advancements in data visualization, with practical forms and frameworks.
● Learn from informative case studies, gaining insights into data visualization and storytelling techniques for various scenarios. Understand how the International Monetary Fund, monetary policy, and fiscal policy impact advancements in data visualization, with practical formats and frameworks.
The Frameworks of the Course
Engaging video lectures, case studies, assessments, downloadable resources, and interactive exercises. This course is designed to explore the field of data visualization and storytelling, covering various chapters and units. You’ll delve into data representation, feature engineering, data visualization techniques, interactive dashboards, visual analytics, data preprocessing, data analysis, data-driven storytelling, dashboard design, advanced topics in data visualization, and future trends.
The socio-cultural environment module using data visualization techniques delves into sentiment analysis and opinion mining, data-driven storytelling, and interactive visualization in the context of India’s socio-cultural landscape. It also applies data visualization to explore data preprocessing and analysis, data-driven storytelling, interactive dashboards, visual analytics, and advanced topics in data visualization. You’ll gain insight into data-driven analysis of sentiment analysis and opinion mining, data-driven storytelling, and interactive visualization. Furthermore, the content discusses data visualization-based insights into data visualization applications and future trends, along with a capstone project in data visualization.
The course includes multiple global data visualization projects, resources like formats, templates, worksheets, reading materials, quizzes, self-assessment, case studies, and assignments to nurture and upgrade your global data visualization and storytelling knowledge in detail.
Course Content:
Introduction and Study Plan
● Introduction and know your Instructor
● Study Plan and Structure of the Course
1. Introduction to Data Visualization
1.1.1 Introduction to Data Visualization
1.1.2 Why Data Visualization
1.1.3 Types of Data Visualization
1.1.4 Tools for Data Visualization
1.1.5 Best Practices for Data Visualization
1.1.6 Conclusion
2. Data Types and Visualization Techniques
2.1.1 Data Types and Visualization Techniques
2.1.2 Numerical Data
2.1.3 Categorical Data
2.1.4 Time Series Data
2.1.5 Text Data
2.1.6 Geospatial Data
2.1.7 Conclusion
3. Data Preparation and Cleaning for Visualization
3.1.1 Data Preparation and Cleaning for Visualization
3.1.2 Data Collection
3.1.3 Data Integration
3.1.4 Data Quality Assurance
3.1.5 Data Visualization
3.1.6 Conclusion
4. Exploratory Data Analysis (EDA)
4.1.1 Exploratory Data Analysis (EDA)
4.1.2 Data Collection and Familiarization
4.1.3 Data Visualization
4.1.4 Feature Engineering
4.1.5 Iterative Process
4.1.6 Conclusion
5. Advanced Data Visualization Techniques
5.1.1 Advanced Data Visualization Techniques
5.1.2 Interactive Visualizations
5.1.3 Parallel Coordinates
5.1.4 Network Graphs
5.1.5 Augmented Reality(AR) and Virtual Reality (VR
5.1.6 Conclusion
6. Visualizing Uncertainty and Projections
6.1.1 Visualizing Uncertainty and Projections
6.1.2 Error Bars
6.1.3 Prediction Intervals
6.1.4 Heatmaps with Uncertainty Bands
6.1.4 Animated Visualizations
6.1.5 Conclusion
7. Storytelling with Data
7.1.1 Storytelling with Data
7.1.2 Know Your Audience
7.1.3 Use Engaging Visuals
7.1.4 Add Storytelling Elements
7.1.5 Practice Ethical Data Storytelling
7.1.6 Conclusion
8. Design Principles and Aesthetics
8.1.1 Design Principles and Aesthetics
8.1.2 Clarity and Simplicity
8.1.3 Color Choice
8.1.4 Gestalt Principles
8.1.4 Continuation of Gestalt Principles
8.1.5 Conclusion
9. Ethical and Responsible Data Visualization
9.1.1 Ethical and Responsible Data Visualization
9.1.2 Accuracy and Truthfulness
9.1.3 Fairness and Equity
9.1.4 Consent and Respect
9.1.6 Continuous Learning and Improvement
9.1.7 Conclusion
10. Data Visualization Tools and Technologies
10.1.1 Data Visualization Tools and Technologies
10.1.2 General-purpose Visualization Tools
10.1.3 Specialized Visualization Tools
10.1.4 Programming Libraries and Frameworks
10.1.5 Business Intelligence (BI) Platforms
10.1.6 Conclusion
11. Capstone Project
11.1.1 Capstone Project
11.1.2 Project Overview
11.1.3 Visualization Design
11.1.4 Interactive Elements
11.1.5 Presentation and Documentation
11.1.6 Conclusion
Assignments
Student’s Academic Performance Dataset (xAPI-Edu-Data)
Data Visualization for Exploratory Data Analysis of the Titanic Dataset
