Professional Diploma in Data Science and Artificial Intelligence (Top-Up)

The "Data Science essentials" module equips learners with essential knowledge and skills crucial for navigating the data-driven landscape. Covering a spectrum of topics, including Fundamentals of Python, Data processing using Python, 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 fundamentals of Python syntax, data types, functions, conditional statements, and Pandas data processing. The learners will get insights about the fundamentals of data and data storage. They will be familiarized with the implementation of machine learning tasks with AutoML providing practical insights into machine learning. 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 developing automated machine learning solutions.

The "Applied Machine Learning" 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 "Capstone Project - module serves as a pivotal opportunity for students enrolled in the "Professional Diploma in Data Science & Artificial Intelligence" to apply the knowledge and skills acquired in preceding course modules. Covering a spectrum of topics such as Data Analytics, Python Programming, Applied Machine Learning, Deep Learning, Generative AI, and Agile Project Management, the course lays a strong foundation. 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 Capstone 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.