INTRODUCTION TO DATA ANALYTICS
Building a Solid Foundation in Data-Driven Decision-Making and Analytical Thinking
Course Schedule
| Date | Venue | Fees (Face-to-Face) |
|---|---|---|
| 05 – 09 May 2025 | Dubai, UAE | USD 3495 per delegate |
Course Introduction
In today’s digital landscape, organizations are flooded with data—but only those who know how to harness it effectively gain competitive advantage. Data analytics empowers professionals to extract meaningful insights, spot trends, and support decision-making through evidence-based approaches.
This foundational course is designed to introduce participants to the world of data analytics. It covers the core principles, tools, and techniques used in analyzing data, visualizing patterns, and generating actionable insights. Through practical exercises and real-world applications, participants will gain hands-on experience in transforming raw data into strategic value.
Course Objectives
By the end of this course, participants will be able to:
• Understand the fundamental concepts and lifecycle of data analytics
• Identify the types of data and analytical techniques appropriate for various scenarios
• Use tools such as Excel, Power BI, or Python for data analysis and visualization
• Apply basic statistical methods to interpret data
• Communicate insights effectively using dashboards and storytelling techniques
Key Benefits of Attending
• Build confidence in using data to support business decisions
• Gain practical experience with commonly used analytics tools
• Bridge the gap between data and decision-making across departments
• Develop a foundation for further specialization in data science or business analytics
• Stay competitive in a data-driven economy
Intended Audience
This program is designed for:
• Professionals with no prior background in data analytics
• Business managers seeking to enhance data literacy
• Finance, marketing, operations, and HR staff using data in daily tasks
• IT support staff transitioning to analytics roles
• Anyone looking to understand and work with data more effectively
Individual Benefits
Key competencies that will be developed include:
• Basic data exploration and cleaning
• Use of visual tools and dashboards for analysis
• Understanding of descriptive and diagnostic analytics
• Ability to draw insights from structured data sets
• Presentation of data findings to business stakeholders
Organization Benefits
Upon completing the training course, participants will demonstrate:
• Improved use of internal data for problem-solving and planning
• Enhanced data-driven culture and decision-making processes
• Stronger reporting capabilities across departments
• Reduction in guesswork and increased reliance on facts
• More effective collaboration between business and technical teams
Instructional Methdology
The course follows a blended learning approach combining theory with practice:
• Strategy Briefings – Key concepts in analytics, business value, and frameworks
• Case Studies – Real-life scenarios from marketing, finance, and operations
• Workshops – Hands-on practice with data sets and visualization tools
• Peer Exchange – Group exercises and problem-solving activities
• Tools – Excel, Power BI (or Tableau), and optional Python-based exercises
Course Outline
Training Hours: 7:30 AM – 3:30 PM
Daily Format: 3–4 Learning Modules | Coffee breaks: 09:30 & 11:15 | Lunch Buffet: 01:00 – 02:00
Day 1: Introduction to Data and Analytics
- Module 1: What is Data Analytics? (07:30 – 09:30)
• Data types, sources, and the analytics lifecycle - Module 2: The Role of Data in Decision-Making (09:45 – 11:15)
• Business use cases and data strategy - Module 3: Data Collection and Cleaning Basics (11:30 – 01:00)
• Structured vs. unstructured data, data preparation - Module 4: Workshop – Exploring and Cleaning Data in Excel (02:00 – 03:30)
• Missing values, formatting, and consistency
Day 2: Descriptive Analytics and Basic Statistics
- Module 1: Summarizing Data with Metrics (07:30 – 09:30)
• Means, medians, modes, variances - Module 2: Understanding Trends and Patterns (09:45 – 11:15)
• Charts, histograms, distributions - Module 3: Introduction to Correlation and Basic Inference (11:30 – 01:00)
• Scatter plots, relationship analysis - Module 4: Workshop – Data Summary and Insights (02:00 – 03:30)
• Analyze marketing or sales data in Excel or Power BI
Day 3: Diagnostic Analytics and Visualization
- Module 1: Root Cause Analysis and KPIs (07:30 – 09:30)
• Drill-down methods and key indicators - Module 2: Data Visualization Principles (09:45 – 11:15)
• Effective dashboards, chart selection - Module 3: Creating Dashboards in Power BI or Tableau (11:30 – 01:00)
• Live visuals, filters, interactivity - Module 4: Workshop – Build an Interactive Dashboard (02:00 – 03:30)
• Create and share a sample business dashboard
Day 4: Intro to Predictive Thinking and Tools
- Module 1: What Comes After Descriptive Analytics? (07:30 – 09:30)
• Overview of predictive and prescriptive analytics - Module 2: Exploring Forecasting Models (09:45 – 11:15)
• Linear trends, moving averages - Module 3: Optional Intro to Python for Data Analysis (11:30 – 01:00)
• Pandas, NumPy basics (for interested participants) - Module 4: Workshop – Run a Simple Forecast (02:00 – 03:30)
• Excel or Python-based predictive exercise
Day 5: Communicating Insights and Business Application
- Module 1: Data Storytelling and Interpretation (07:30 – 09:30)
• Tailoring insights to your audience - Module 2: Building a Data-Driven Presentation (09:45 – 11:15)
• Crafting messages from dashboards - Module 3: Final Project – Analyze and Present a Data Case (11:30 – 01:00)
• Participants apply end-to-end process - Module 4: Presentations and Feedback (02:00 – 03:30)
• Group reviews and certification
Certification
Participants will receive a Certificate of Completion in Introduction to Data Analytics, confirming their ability to apply foundational analytical techniques and tools to derive insights and support evidence-based decision-making.