Module 1: Foundations for Data Specialist (77 hours)

• Introduction to Data Roles: Explore diverse data career paths and responsibilities
• Programming Foundations: Build simple calculators and tackle API integration
• Version Control: Learn and apply Git and GitHub for collaborative development
• Data Analysis Foundations: Understand terminologies, data types, workflow processes, privacy, and security principles
• Project Development Foundations: Practice with Agile and SCRUM methodologies

Module 2: Data Analysis & Visualization (112 hours)

• Python in Data Analysis: Use Python programming for data cleaning, manipulation and analysis, learn statistical foundations
• Statistics Required for Data Analytics: Introduction to different types of statistics and how to read and use them in data analysis
• SQL for Data Analysis: Develop SQL proficiency for database querying and analysis
• Effective Communication for Data Analysts: Learn effective stakeholder communication through targeted business interaction strategies and clear analytical presentation techniques
• Data Visualization & Reporting: Learn industry-specific domain knowledge to contextualize their analyses meaningfully, while mastering data visualization principles and reporting best practices to transform complex insights into compelling visual narratives

Module 3: Data Engineering & Management (154 hours)

• Data Modelling, Design & Architecture: Explore and get familiar with data management concepts, relational and non-relational data systems, and understand data warehousing approaches, including an introduction to Snowflake.
• Cloud Platforms for Data Engineering: Practice and learn how to use platforms such as Microsoft Azure and Microsoft Fabric while developing a deep understanding of data architecture principles
• Distributed & Real-Time Data Processing: Introduction to PySpark and Databricks as a foundation to understand how cloud platforms, big data technologies, and streaming analytics are integral to organizational data strategies

Module 4: Data Pipelines & Advanced Analytics (147 hours)

• Data Pipeline & Workflow Orchestration: Learn to apply tools like Shell, Airflow and Docker to develop data pipelines
• Testing & Data Quality in Pipelines: Introduction to testing and data quality in preparation of managing sophisticated data systems
• Big Data & Machine Learning Foundations: Understand big data, machine learning and how to use AI applications to manage data
• Large Language Models and Agentic AI Systems Foundations: Develop a foundational understanding of how these systems are built, how they function, and where they can be applied

Assessment

Group project and presentation

Certification

Participants who meet at least 75% of the required course attendance and pass the assessment will be awarded the Certificate of Completion.