This training can be customized to fit your organisational needs. Contact us for a pre-briefing session.
| Date | Venue | Duration | |
|---|---|---|---|
| 17 - 21 August 2026 | Sandton | 5 Days |
Data Science and Business Analytics play pivotal roles in transforming vast amounts of data into actionable insights that drive effective decision-making and competitive advantages. This Masterclass provides a comprehensive understanding and practical application of the latest analytical techniques, programming languages, and strategic frameworks essential for today’s data-driven organizations.
Certification upon completion
Access to industry experts
Networking opportunities with peers and professionals
Skills to transform organizational data into strategic advantages
Tools:
| Tool | No. of Days | Purpose |
|---|---|---|
| Power BI | 1 | Business Intelligence, Data Analytics & Visualization |
| SQL | 1 | SQL Database: Structured Querying |
| Cassandra | 1 | No-sql Database |
| Python | 5 | Data Science Programming Language |
| Azure | 1 | Cloud Computing and Model Deployment |
| Apache Spark | 1 | High-level light-weight distributed computing |
| Total | 10 |
Tools: Power BI, SQL, Python, Azure
| Tool | No. of Days | Purpose |
|---|---|---|
| Power BI | 1 | Business Intelligence, Data Analytics & Visualization |
| SQL | 1 | SQL Database: Structured Querying |
| Python | 2 | Data Science Programming Language |
| Azure | 1 | Cloud Computing and Model Deployment |
| Total | 5 |
Participants will:
Develop a robust understanding of data science and business analytics and their interconnectedness.
Gain proficiency in analytical techniques and methodologies.
Learn to apply data analytics strategically in real-world business scenarios.
Build skills in data visualization, predictive modelling, and effective data communication.
Enhance critical thinking, problem-solving, and strategic decision-making abilities.
This Data Science and Business Analytics Masterclass course is suitable for:
Data Managers
Business Analysts
Graduates and Scholars
IT Professionals
Project Managers
Entrepreneurs
Quality Analysts
Our diverse instructional approaches ensure effective learning:
– Lectures & Presentations: Engage with expert-driven, stimulating content.
– Course Material: Access well-crafted supporting resources.
– Group Work: Collaborate on discussions and case studies for practical insights.
– Workshops & Role-Play: Participate in immersive, scenario-based activities.
– Practical Application: Focus on applying theoretical knowledge in real situations.
– Post-Training Support: Receive extensive support after training for skill implementation.
Module 1: Introduction to Data Science
Overview and scope of Data Science
Big Data fundamentals and applications
Descriptive statistics and hypothesis testing
Data warehousing essentials
Application in marketing, finance, and operations
Module 2: Business Analytics Essentials
Understanding business intelligence and analytics
Customer and risk analytics
Detective analysis and root cause identification
Data science vs. business analytics: Strategic roles
Building effective analytics dashboards
Module 3: Techniques of Data Science
Advanced statistical analysis (ANOVA, Regression)
Dimension reduction methods
Data and text mining techniques
Supervised vs. unsupervised learning
Exploratory data analysis (EDA)
Module 4: Forecasting and Predictive Analytics
Fundamentals of predictive modelling
Time series forecasting methods
Advanced data science programming
Machine learning algorithms and applications
Optimization models: Linear and goal programming
Module 5: Data Exploration Techniques
Objectives and methods of data exploration
Qualitative vs. quantitative data analysis
Steps for effective data exploration
Visualization strategies for exploration
Module 6: Data Visualization and Storytelling
Principles of effective visualization
Creating interactive performance dashboards
Visual analytics and insights
Storytelling techniques to influence stakeholders
Module 7: Framing Business Problems
Defining and formulating business problems
Hypothesis generation and testing
Transformative problem-solving strategies
Identifying and setting dependent and independent variables
Module 9: Advanced Data Analysis
Advanced data manipulation
Sorting, filtering, conditional formatting
Scenario analysis using What-if Analysis
Building and interpreting Pivot Tables
Automation of analytics
Module 8: Advanced Predictive Modelling
Prediction vs. interpretation methodologies
Data preprocessing and transformation
Classification and clustering models
Handling and interpreting outliers
Validating and optimizing models
Module 10: Emerging Trends in Data Science
Augmented analytics and AI-driven insights
Data cleaning and preparation techniques
Machine learning automation
Cloud-based analytics solutions
Ensuring data quality and governance
Cognitive and prescriptive analytics
Module 11: Industry Applications of Data Science and Business Analytics
Analytics in financial services: Fraud detection, risk management
Customer analytics: CRM, customer segmentation, campaign management
Data-driven decision making in IT and software industries
Case studies: Retail, healthcare, manufacturing
Module 12: Capstone Project
Practical application of course knowledge
Hands-on projects solving real-world business problems
Presentation and feedback sessions
Project documentation and reporting standards
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