Professional Diploma in Data Science

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.

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. 

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.

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.

The "Machine Learning Algorithms and Methods" module empowers learners with a profound understanding of key concepts and practical skills required in real-world applications. Through a structured curriculum, participants delve into Regression tasks, Classification tasks, and Clustering tasks, and harness the power of Azure AI Services for Pre-built models, culminating in a focus on Text Analysis with the Language Service.

Learners master the intricacies of Regression tasks, acquiring the ability to select and apply appropriate algorithms for predictive modelling. The module extends to Classification tasks, providing insights into classifying data into distinct categories, and delves into Clustering tasks, unravelling techniques to group data points based on inherent patterns. Additionally, participants gain proficiency in leveraging Azure AI Services for Pre-built models, enhancing their toolkit with readily available models for diverse applications. The module concludes with a deep dive into Text Analysis using the Language Service, unravelling the complexities of processing, and extracting insights from textual data.

Through hands-on projects, participants implement machine learning tasks using Azure ML Designer, facilitating a comparative analysis of algorithmic performance. The culmination involves the implementation of regression, classification and clustering using Azure ML Studio. As a result, learners emerge with a robust skill set, ready to apply machine learning principles to solve complex problems in diverse domains.

The "Deep Learning" module provides learners with a comprehensive understanding of key concepts and skills in the realm of deep learning. Covering crucial learning units including Introduction to Computer Vision, Face Recognition and Optical Character Recognition, Image Classification, Introduction to Natural Language Processing, and Conversational Language Understanding, this module offers a robust foundation for those seeking to apply deep learning techniques in practical scenarios.

Through hands-on projects, participants will translate theoretical knowledge into tangible skills. The module commences with the implementation of Optical Character Recognition using the Azure AI Vision portal, allowing learners to gain practical insights into extracting and processing text from images. Subsequently, participants delve into the realm of text analysis using the Azure Language portal, showcasing the application of deep learning in extracting meaningful information from textual data.

By the conclusion of the module, learners will have honed their abilities in computer vision, image classification, and natural language processing. The practical projects not only reinforce technical skills but also instil a proficiency in leveraging deep learning tools for real-world applications, empowering participants to engage in face recognition, text extraction, and analysis tasks using state-of-the-art technologies.

The Agile Management course offers a transformative experience, equipping businesses with essential skills and tools to thrive in today's dynamic environment. Participants gain proficiency in Agile principles, fostering adaptability, collaboration, and continuous improvement. Comprehensive Instructional Units shape agile leaders capable of navigating complexity, employing Scrum methodology, leading teams, and delivering value-driven outcomes. The Agile Management Capstone provides a framework for implementing agility in Business-as-Usual activities, ensuring efficient and customer-centric delivery while reducing risk.

Beginning with "Adapt to Complexity using Empiricism and Scrum," participants delve into core Agile Management principles, mastering complexity and Scrum. Subsequent units cover leadership, organizational agility, growth strategies, metrics, and creating an agile culture. Participants learn to create efficient workspaces, facilitate Scrum events, and plan releases predictably. "Build Effective Scrum Teams & Prioritize Valuable Business Outcomes" focuses on team dynamics and product backlog management.

"Conduct Effective Scrum Events for High Performing Teams" refines skills in sprint planning and daily scrum ceremonies. The core units conclude with "Implement Continuous Growth and Development," exploring the learning loop concept within the broader Agile ecosystem.

Completing the course, participants emerge as Agile Management champions, adept at navigating business complexities, optimizing outcomes, and propelling organizations toward sustainable success in an agile world.

The " Data Science Modelling Project” module serves as a pivotal opportunity for students enrolled in the "Professional Diploma in Data Science " to apply the knowledge and skills acquired in preceding course modules. The Capstone Project module extends this foundation, enabling students to refine their practical skills by engaging in real-world projects and addressing industry challenges.

This module immerses learners in the day-to-day operations of live industry projects, allowing them to apply technical skills in data analysis, machine learning model development, and the utilization of deep learning techniques. Through hands-on experience with industry-standard tools, collaboration with professionals, and project management, students gain a comprehensive understanding of the data science lifecycle. The practical experience obtained not only enhances technical proficiency but also cultivates critical thinking, problem-solving, and communication skills within a professional context.

Ultimately, the Data Science Modelling Project module positions students as well-rounded data science and AI professionals, providing a competitive edge in the job market. Bridging the gap between theory and practice, learners showcase their ability to solve real-world problems and deliver tangible results. The acquired hands-on experience establishes a robust foundation for future careers, enabling graduates to contribute effectively to the industry and make a positive impact in the dynamic field of data science and AI.