COURSE OVERVIEW

In today’s data-driven world, raw numbers alone do not drive decisions. The ability to analyze, interpret, and present data effectively is crucial for professionals seeking to influence decisions and drive meaningful change. This course equips participants with the skills to transform data into compelling stories through structured analytics and visualization techniques.

LEARNING OBJECTIVES

Here are the learning objectives for the two days training program; after completing this program, participants will be able to:

• Understand and apply data analytics methodologies to extract meaningful insights.
• Structure and communicate data-driven stories tailored to different audiences.
• Utilize data visualization principles to present information clearly and effectively.
• Create compelling dashboards and reports for impactful storytelling.
• Develop confidence in presenting data narratives to support decision-making.

DURATION

• Time: 9.00 a.m. – 5.00 a.m.
• Days: 2 days

TARGET GROUP

• Senior Management Executive.
• Business Analysts
• Data Analysts
• Marketing and Sales Professionals
• Researchers and Academics
• Project Managers
• Policy Makers and NGO Professionals
• Anyone looking to improve their data storytelling and visualization skills

LANGUAGE

• English

COURSE CONTENT

• Introduction to Data Analytics & Storytelling
  1. The Role of Data in Decision-Making
  2. The Data Analytics Process: Collection, Cleaning, Analysis, Interpretation
  3. Why Storytelling Matters in Analytics
  4. Key Characteristics of an Effective Data Story
  5. Activity: Case study discussion – How organizations use data storytelling for impact

• Data Analytics Methodologies

1. 1. Types of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
   2. Defining Business Questions for Analysis
   3. Data Sources: Structured vs. Unstructured Data
   4. Methods for Data Cleaning and Preparation
   5. Activity: Hands-on exercise – Cleaning and preparing sample datasets using minitab

• Data Exploration & Finding Insights

1. 1. Using minitab as analysis tool
      1. Main function and analysis capabilities
      2. Summarizing data
   2. Exploratory Data Analysis (EDA) Techniques
   3. Identifying Patterns, Trends, and Outliers
   4. Understanding Correlation vs. Causation
   5. Using Aggregations, Filters, and Statistical Summaries
   6. Activity: Group work – Analyzing real-world datasets to extract insights

• Data Visualization
  1. Principles of Effective Data Visualization
  2. Selecting the Right Visualization for the Data Type
  3. Visualization Plots
     1. Box plot
     2. Trend Charts
     3. Relational Plots
     4. Pareto Charts/Stacked Charts
  4. Common Mistakes to Avoid in Visualization
  5. Enhancing Readability and Interpretability
  6. Activity: Participants use data visualization techniques using real data.

• Statistical Analysis to enhance story telling
  1. Buliding regressive relationship analysis
  2. Hypothesis Testing
  3. ANOVA
  4. Regression Techniques

• Turning Analysis into a Narrative
  1. The Storytelling Framework: Context, Conflict, Resolution
  2. Structuring a Data Narrative with Visuals
  3. Understanding Your Audience & Tailoring Your Message
  4. The Role of Emotions in Data Storytelling
  5. Activity: Participants outline a data story using a provided dataset

• Advanced Visualization Techniques
  1. Creating Dashboards for Interactive Storytelling
  2. Using Charts, Maps, and Infographics for Impact
  3. The Role of Color, Typography, and Layout in Visual Design
  4. Best Practices for Presenting Quantitative Information
  5. Activity: Hands data analysis and presentation for process improvement

• Communicating Insights Effectively
  1. Storytelling with Numbers: Simplifying Complexity
  2. The Power of Comparisons and Benchmarks
  3. Building Credibility with Transparent Data Interpretation
  4. Presenting Data to Non-Technical Audiences
  5. Activity: Participants refine and prepare their final data stories

• Final Data Story Presentations
  1. Participants present their data stories
  2. Peer and instructor feedback