Data Analytics Training

1. Data Analytics: Insights, Strategy & Visualisation

Overview:

This course empowers professionals with the ability to make data-driven decisions through practical
analytics techniques. Participants will learn how to gather, clean, analyse, and visualise data to
uncover actionable business insights. The programme blends core data skills with modern tools such
as Power BI, Python, Excel, and SQL.

Ideal For: Business analysts, finance teams, marketing strategists, operations managers, and junior data scientists.

Outcomes:

• Understand data lifecycle and governance principles
• Build dynamic dashboards and reports using Power BI & Excel
• Perform exploratory and predictive data analysis
• Interpret trends and KPIs to inform strategic business decisions
• Leverage data storytelling techniques to communicate insights

Duration: 3 – 5 Days

2. Data Lakes, Data Lakehouses & Data Warehouses: Architecting Enterprise Data Platforms

Overview:

This in-depth course unpacks the foundational and advanced concepts behind modern data
storage architectures. It’s designed to help technical and non-technical teams understand
how to build, govern, and scale enterprise-grade data environments that support analytics,
AI, and compliance needs.

Ideal For: IT managers, data engineers, solution architects, business intelligence leads, and
transformation teams looking to modernize their data infrastructure.

Outcomes:

• Understand the differences between Data Lakes, Lakehouses, and Warehouses
• Learn when and how to use each architecture for performance, flexibility, and cost efficiency
• Explore modern cloud-native solutions (Azure, AWS, Google Cloud, Snowflake, Databricks)
• Understand ingestion pipelines, metadata management, and security considerations
• Design scalable environments that support analytics, AI, and governance
• Get hands-on with architecture blueprints and best practices

Duration: 3 – 7 Days

3. Advanced Analytics, Machine Learning & Deep Learning: Transforming Data into Intelligent
Decisions

Overview:

This course is designed to empower corporate teams to move beyond traditional BI into the
world of predictive, prescriptive, and cognitive analytics. It introduces participants to
statistical modeling, machine learning algorithms, and deep learning frameworks using realworld business applications.

Ideal For: Data analysts, data scientists, IT leads, business strategists, product teams, and innovation officers aiming to embed intelligence into operations.

Outcomes:

• Understand the full analytics spectrum: descriptive → diagnostic → predictive →
prescriptive
• Learn supervised vs unsupervised learning, feature engineering, and model evaluation
• Introduction to neural networks, CNNs, RNNs, and foundational deep learning concepts
• Get hands-on with ML platforms (Azure ML, Google Vertex AI, AWS Sagemaker, Python with
Scikit-Learn & TensorFlow)
• Learn how to structure an ML project from business case to deployment
• Ethical AI, governance, explainability and bias mitigation
Duration: 5 – 10 Days (customisable by skill level)

4. Artificial Intelligence & Process Automation: Streamline, Scale, and Smartly Operate

Overview:

This course empowers organizations to harness AI and automation for operational
excellence. Participants will explore how to design intelligent workflows, integrate AI tools,
and automate repetitive business processes driving efficiency, reducing costs, and improving
speed-to-insight.

Ideal For: Ops managers, IT teams, automation specialists, innovation leads, process analysts, and
business strategists.

Outcomes:

• Understand AI foundations and its application in process automation
• Learn about RPA (Robotic Process Automation), Intelligent Document Processing (IDP), and
AI chatbots
• Explore tools like Power Automate, UiPath, Zapier, OpenAI integrations, and Google
Dialogflow
• Map and optimize workflows using AI-based decision logic
• Learn to identify automation opportunities and calculate ROI
• Integrate AI into business processes for customer service, finance ops, HR, and supply chain

Duration: 3 – 7 Days (customisable per department or use-case)

5. Data Governance: Building Trust, Compliance & Control in the Age of Data

Overview:

This course is designed to equip professionals with the skills to manage, protect, and govern
enterprise data effectively. Participants will learn how to create data policies, enforce quality
standards, align with regulations (like POPIA & GDPR), and ensure data is used ethically and
efficiently across departments.

Ideal For: Compliance officers, data stewards, IT managers, business analysts, data custodians, and
department heads responsible for decision-making.

Outcomes:

• Understand the fundamentals of Data Governance, including frameworks (DAMA-DMBOK,
COBIT, etc.)
• Learn to define and manage data ownership, stewardship, and accountability
• Create policies and standards for data quality, access control, privacy, and lifecycle
management
• Master regulatory requirements and how to stay compliant (e.g., POPIA, GDPR, King IV)
• Establish a Data Governance Council and build a culture of data responsibility
• Learn to integrate governance into data strategy, analytics, and AI pipelines

Duration: 2 – 5 Days (customisable for executive, operational, or technical teams)