Course Brief

Upon completion of the Professional Diploma in Data Science, learners will be well-equipped to embark on a thriving career in the rapidly expanding field of data science. This course offers a comprehensive pathway to various high-demand roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and Business Intelligence Professional. Graduates will find opportunities in diverse sectors, ranging from finance and healthcare to technology and retail, where they can apply their skills to extract meaningful insights from data, drive decision-making, and contribute significantly to their organizations" success.

The course begins with "Data Modelling & Visualization", where learners are introduced to Data Analytics fundamentals and trained in utilizing Power BI for data transformation, modelling, and creating impactful reports and dashboards. This module lays the foundation for visualizing and interpreting complex data sets. The course then introduces "Generative AI", where learners delve into Generative AI Models & Tools, learn the fundamentals and advanced techniques of prompt engineering, explore Microsoft 365 Copilot use cases for productivity, and create chatbots using Microsoft Power Virtual Agents. This module offers insights into the cutting-edge field of generative AI and its practical applications.  Next, the "Python for Data Science" module dives into fundamental Python programming, covering aspects like functions, conditional statements, and data processing. Also, the learners can upskill themselves in how to streamline business processes through Power Platform, automating workflows and building model-driven apps. In the “Data Science Principles” module, the groundwork is laid with comprehensive insights into data, AI, and machine learning. This module provides a strong foundation in the principles and practices of data science. The "Machine Learning Algorithms and Methods" module builds on this foundation, covering regression, classification, clustering, and the use of Azure AI Services.

The deep learning module delves into computer vision, face recognition, image classification, and natural language processing, fostering expertise in these advanced AI domains. This module is essential for understanding complex AI models and their applications. The "Agile Project Management" module equips learners with skills in empirical process control principles, servant leadership, workflow optimization, constructing high-performing Scrum teams, and executing Scrum events proficiently, thereby preparing them to manage data science projects effectively. Finally, the "Data Science Modelling Project" allows learners to apply their knowledge in a practical setting, encompassing project planning, data preparation, model evaluation, and comparison. This capstone project is a culmination of the skills learned throughout the course and provides hands-on experience in executing a comprehensive data science project.

Throughout this course, learners will engage in a hands-on learning experience, gaining practical skills through the instructional units within each module. By mastering these skills learners will acquire the necessary expertise to tackle complex data challenges.

Course Knowledge, Skills & Ability Summary

At the end of the course, you will be able to acquire the following:


  • Identify various techniques in data modelling, visualization, and analysis using Power BI.
  • Describe the fundamentals and advanced concepts of Generative AI and its practical applications.
  • Evaluate the business value of Power Platform and articulate its significance in process automation.
  • Differentiate between key machine learning and deep learning algorithms, including classification, regression, and clustering methods.
  • Analyze the use of Azure AI Services for implementing pre-built models.
  • Summarize agile project management principles and their implementation in data science projects.


  • Design and create dynamic reports and dashboards using Power BI for insightful data visualization.
  • Develop generative AI applications using prompt engineering and Microsoft Generative AI tools.
  • Utilize Power Automate core components to automate workflows and streamline business processes.
  • Implement machine learning and deep learning models to solve real-world problems and optimize their performance.
  • Utilize computer vision and natural language processing techniques for efficient AI application development.
  • Execute a data science modelling project from planning to development, integrating diverse data science skills.


Upon completion, learners will possess the ability to analyse, design, and develop data science solutions effectively, by integrating data analytics, low code app development, machine learning, deep learning, and generative AI techniques in real-world projects in diverse industries. 

Blended Learning Journey

(804 Hours)

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104 Hours

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Flipped Class

96 Hours

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Mentoring Support (Sync) (Assignment)

96 Hours

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Mentoring Support (Sync) (Project)

84 Hours

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Mentoring Support (Async)

90 Hours

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Additional Practice

330 Hours

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Summative Assessment

4 Hours

Module Summary

WSQ Data Modelling and Visualization (SF)

Module Brief

The module "Data Modelling & Visualization" is designed to empower learners with the essential knowledge and skills required to thrive in the dynamic field of data analysis. Through a structured series of learning units, participants will establish a robust foundation in various facets of data analytics. The module commences with an exploration of fundamental concepts in data analytics and Power BI, enabling participants to adeptly prepare and transform data, create insightful visualizations and reports, develop data models, and proficiently format and present information using interactive dashboards.

Expanding on the expertise garnered from the learning units, participants will engage in hands-on, real-world scenarios through a comprehensive project. This project offers a unique opportunity to implement a data analytics solution employing Power BI. By leveraging Power BI, participants will gain practical experience in analyzing and interpreting data and making informed decisions based on data insights. This practical application will foster the development of the participant"s capacity to extract meaningful insights, collaborate effectively, and actively contribute to the success of businesses.

Upon successful completion of the "Data Modelling & Visualization" module, participants will emerge with a profound understanding of data analytics principles and techniques. They will possess proficiency in utilizing Power BI, equipped with the necessary skills to prepare and transform data, craft impactful visualizations and reports, develop data models, and design interactive dashboards. Furthermore, participants will be empowered to apply their acquired knowledge in practical settings, utilizing data analysis to drive informed decision-making and address intricate business challenges.

Other Information
  • SSG Module Reference No: TGS-2024043488
  • Module Validity Date: 2025-01-31

WSQ Generative AI (SF)

Module Brief

In the "Generative AI" module, learners will learn about different AI models and how they"re used in real life. We"ll start with the basics of these AI models, and understand how they work. We"ll also explore practical tools like ChatGPT, Copilot, and Power Virtual Agents from Microsoft. By using these tools, learners will discover how Generative AI can be applied in various ways, like research and creating content.

In this module, learners will explore the practical applications of ChatGPT and Microsoft 365 Copilot. They"ll uncover how ChatGPT is used in real life, such as in research, creating content, boosting business productivity, and assisting with customer support. Through hands-on experiences, participants will learn how to use AI tools like ChatGPT and Microsoft Copilot to craft engaging and interactive content. Furthermore, the module will equip learners with the skills to create functional ChatBots using Microsoft Power Virtual Agents and Copilot, facilitating smooth interactions with users.

By the end of this module, participants will have a solid grasp of AI models, as well as practical expertise in effectively utilizing ChatGPT, Copilot, and Microsoft Power VA for tasks like content creation, multimedia presentations, improving business productivity, and enhancing customer support. 

Other Information
  • SSG Module Reference No: TGS-2024043483
  • Module Validity Date: 2025-01-31

WSQ Python for Data Science (SF)

Module Brief

Learners in the "Python for Data Science" module will acquire fundamental knowledge and skills in Python programming, encompassing functions, conditional statements, and data processing. They will also gain insights into the business value of Power Platform, exploring its core components and dynamic decision-making within Power Automate workflows.

Through projects, learners will develop practical competencies. They will automate workflows using Power Automate Templates, enhancing efficiency and productivity. Additionally, learners will demonstrate their ability to implement dynamic decision-making through conditional branches, facilitating adaptable workflow structures. Another project involves automated document generation and tracking of approval status, showcasing proficiency in streamlining processes and maintaining transparency.

Furthermore, learners will apply their acquired skills to implement a data model and build a model-driven approach, contributing to effective data management and analysis. Lastly, they will construct a Canvas app, demonstrating proficiency in creating user-friendly interfaces for data visualization and interaction. Overall, this module equips learners with essential Python skills and practical experience in leveraging Power Platform for data-driven decision-making and process automation.

Other Information
  • SSG Module Reference No: TGS-2024043484
  • Module Validity Date: 2025-01-31

WSQ Data Science Principles (SF)

Module Brief

The "Data Science Principles" module equips learners with essential knowledge and skills crucial for navigating the data-driven landscape. Covering a spectrum of topics, including Fundamentals of Data, Artificial Intelligence, Data Pre-processing, Introduction to Machine Learning, and Automated Machine Learning, this module lays a robust foundation for aspiring data scientists.

Through hands-on projects, learners will apply theoretical concepts to real-world scenarios. The module kicks off with the Implementation of data pre-processing and data cleaning on a dataset with low code solutions, providing practical insights into managing and refining raw data. Subsequently, participants leverage the pre-processed data asset to delve into the realm of Automated Machine Learning (AutoML) for a regression task. This hands-on experience reinforces the understanding of how automation can streamline complex machine-learning processes.

The culmination involves an exploration of various machine learning algorithms within the AutoML framework, followed by a comparative study of the models generated. This project not only sharpens technical proficiency but also cultivates the ability to assess and select optimal models for different scenarios. By the module"s conclusion, learners will emerge with a comprehensive skill set encompassing data fundamentals, AI principles, pre-processing techniques, and practical experience in implementing automated machine learning solutions.

Other Information
  • SSG Module Reference No: TGS-2024043486
  • Module Validity Date: 2025-01-31