FINANCIAL ANALYSIS & FORECASTING
“Turning Complex Data into Competitive Advantage”
Course Schedule
| Date | Venue | Fees (Face-to-Face) |
|---|---|---|
| 03 – 07 Mar 2025 | Dubai, UAE | USD 3495 per delegate |
| 11 – 15 Aug 2025 | Dubai, UAE | USD 3495 per delegate |
Course Introduction
Data is the most powerful asset in today’s digital economy—if managed and analyzed correctly. Organizations need professionals who can harness data science and big data analytics to drive insight, innovation, and smarter decision-making. This course is a comprehensive, practical immersion into the world of data science, analytics, machine learning, and data-driven strategy.
Over five days, participants will gain hands-on experience with analytical models, data visualization tools, and predictive techniques used by leading global organizations. The course bridges technical concepts with business value, empowering attendees to become confident data interpreters and strategic decision-makers.
Course Objectives
By the end of this course, participants will be able to:
- Understand the end-to-end data science lifecycle and analytics value chain.
- Apply statistical, predictive, and machine learning models.
- Analyze and visualize structured and unstructured datasets.
- Use Python and other tools for real-world data exploration.
- Translate insights into strategic and operational decisions.
- Build dashboards and visual narratives to communicate results.
Key Benefits of Attending
- Gain technical and business-oriented data science skills in one program.
- Learn how leading organizations use big data to gain competitive advantage.
- Develop your ability to lead and collaborate in data-centric teams.
- Build and present end-to-end analytics projects.
- Equip yourself with a future-proof skillset applicable across industries.
Intended Audience
This program is designed for:
- Business and data analysts
- Data scientists and engineers
- IT and digital transformation leads
- Strategy and innovation professionals
- Managers looking to build data-driven decision capability
Individual Benefits
Key competencies that will be developed include:
- Data literacy and analytical thinking
- Hands-on use of tools like Python, Excel, Power BI or Tableau
- Modeling, visualization, and storytelling with data
- Agile problem solving and data-driven communication
- Business application of AI, ML, and analytics models
Organization Benefits
Upon completing the training course, participants will demonstrate:
- Enhanced data-driven culture and operational efficiency
- Better insight generation from customer and market data
- Smarter product, pricing, and process decisions
- Reduced reliance on external data consultants
- More proactive risk and performance management
Instructional Methdology
- The course follows a blended learning approach combining theory with practice:
- Interactive Lectures – Core data science concepts and methods
- Tool-Based Labs – Python, Excel, Tableau/Power BI exercises
- Group Projects – Data analytics case work and presentations
- Real-World Cases – Industry-specific analytics success stories
- Practical Templates – Model templates, analytics checklists
- Daily Feedback – Clarification and personalized guidance
Course Outline
Detailed 5-Day Course Outline
Training Hours: 7:30 AM – 3:30 PM
Daily Format: 2–3 Learning Modules | Coffee breaks: 09:30 & 11:15 | Lunch Buffet: 01:00 – 02:00
Day 1: Introduction to Data Science and the Analytics Value Chain
Module 1: Foundations of Data Science (07:30 – 09:30)
- What is data science?
- The role of data in digital transformation
- Introduction to the analytics value chain
Module 2: Data Collection and Preparation (09:45 – 11:15)
- Types and sources of data (structured/unstructured)
- Data cleaning, wrangling, and preprocessing
- Tools: Excel, Python (Pandas), SQL basics
Module 3: Descriptive Analytics & Business Intelligence (11:30 – 01:00)
- Basic statistics and data summary techniques
- Visualizing distributions and identifying patterns
- Dashboards and BI tools overview
Module 4: Hands-On Lab – Exploring a Dataset (02:00 – 03:30)
- Data import, cleaning, and summary in Excel and Python
Day 2: Predictive Analytics and Statistical Modeling
Module 5: Exploratory Data Analysis (07:30 – 09:30)
- Correlation, regression, outlier detection
- Feature engineering and variable importance
Module 6: Introduction to Predictive Modeling (09:45 – 11:15)
- Linear and logistic regression
- Model accuracy, fit, and validation techniques
Module 7: Classification Models and ML Basics (11:30 – 01:00)
- Decision trees, random forests, KNN
- Training, testing, and confusion matrix
Module 8: Lab – Build and Evaluate a Simple Model (02:00 – 03:30)
- Apply regression/classification in Python
Day 3: Big Data & Machine Learning in Action
Module 9: Big Data Concepts and Technologies (07:30 – 09:30)
- Volume, variety, velocity: what makes data “big”
- Hadoop, Spark, cloud-based analytics platforms
Module 10: Advanced ML Techniques (09:45 – 11:15)
- Clustering (K-means, DBSCAN), time series, anomaly detection
- Use cases in finance, marketing, operations
Module 11: AI, Ethics, and Governance (11:30 – 01:00)
- Bias in AI and model risk
- Data privacy, compliance, and responsible analytics
Module 12: Group Exercise – ML Use Case Design (02:00 – 03:30)
- Develop and pitch a use case for big data analytics in your industry
Day 4: Data Visualization, Storytelling & Dashboarding
Module 13: Data Storytelling Principles (07:30 – 09:30)
- Designing visuals for decision-making
- Choosing the right chart for your data
- Communicating uncertainty and assumptions
Module 14: Building Dashboards (09:45 – 11:15)
- Tableau, Power BI, or Excel dashboard techniques
- Filtering, interactivity, layout and flow
Module 15: Presenting to Non-Technical Audiences (11:30 – 01:00)
- Framing problems and solutions
- Using visuals to support a business narrative
- Answering stakeholder questions with confidence
Module 16: Lab – Build a Dashboard (02:00 – 03:30)
- Create and present a live analytics dashboard
Day 5: Integrated Analytics Project & Strategy Alignment
Module 17: Analytics Strategy and ROI (07:30 – 09:30)
- Aligning data projects to business goals
- Building an internal analytics roadmap
- Measuring analytics impact
Module 18: Final Project – End-to-End Analytics Workflow (09:45 – 11:15)
- Apply concepts to solve a case with real data
- Forecasting, segmentation, visualization
Module 19: Group Presentations and Review (11:30 – 01:00)
- Present insights to simulated stakeholders
- Feedback on technical and strategic value
Module 20: Certification Briefing and Wrap-Up (02:00 – 03:30)
- Final reflections, career pathways, and Q&A
Certification
Participants who complete the program will receive a Certificate of Completion in The Complete Course on Data Science & Big Data Analytics, recognizing their practical readiness in applying data science tools, models, and strategies to real business environments.