Advanced Certificate in Data Science

The "Data Analytics & Visualization" module equips learners with the necessary knowledge and skills to perform data analysis and visualization tasks using Python. Through a series of Instructional Units (IU), learners will acquire proficiency in various key areas.

In the initial learning units, learners will gain an introduction to Python programming, including essential concepts such as functions, conditionals, and file handling. This foundational knowledge will provide learners with the necessary programming skills to work with data effectively. Learners will also explore the functionalities of NumPy, a fundamental library for numerical computing in Python. They will acquire skills in array manipulation, mathematical operations, and statistical analysis using NumPy.

In addition, the module also covers data manipulation using Pandas, a powerful data manipulation library in Python. Learners will learn how to load, clean, and transform data using Pandas, enabling them to perform various data wrangling tasks efficiently. The module culminates with a focus on data visualization using Matplotlib and Seaborn. Learners will learn how to create visually appealing and informative plots, charts, and graphs to effectively communicate insights derived from data analysis.

Through the module"s projects, learners will have the opportunity to apply their acquired knowledge and skills in a real-world context. They will develop a Python project centred around analysing and visualizing sales data using Pandas, Matplotlib and Seaborn. This project will allow learners to practice data manipulation techniques, extract meaningful insights from the data, and present their findings through compelling visualizations.

By the end of the module, learners will have gained proficiency to perform data analysis tasks, apply statistical techniques, and effectively communicate their findings through visualizations. These skills will empower learners to make data-driven decisions and present insights in a clear and concise manner in various professional settings.

The "Principles of Machine Learning" module provides participants with foundational knowledge and skills crucial for understanding and applying machine learning techniques. Delivered through a comprehensive set of Instructional Units (IUs), this program ensures participants establish a robust understanding of the principles and methodologies inherent in machine learning.

In the initial IU, participants delve into classification, mastering various algorithms to classify data into distinct categories, facilitating informed decisions based on input features. The second IU shifts focus to regression, where participants learn to create models predicting continuous numeric values using diverse regression algorithms.

The next IU concentrates on enhancing machine learning models, covering techniques such as regularization, and hyperparameter optimization using sci-kit learn. This includes refining the ability to fine-tune models and optimize hyperparameters for heightened accuracy and generalization. The fourth IU introduces tree and ensemble methods, leveraging the combination of multiple models for enhanced predictive performance.

Furthermore, the module explores optimization-based methods, exploring algorithms like Artificial Neural Networks and Support Vector Machines. This equips participants to optimize model parameters for improved performance. Throughout the module, participants apply their knowledge in a practical project involving validating, transferring, integrating, and deploying models using Microsoft Azure. This real-world project allows participants to practically apply machine learning principles, reinforcing their understanding and practical capabilities.

Upon completing this module, participants possess a comprehensive grasp of machine learning principles, along with the skills to implement various techniques, optimize models using python libraries like Sci-kit learn. The blend of theoretical knowledge and hands-on experience empowers participants to apply machine learning across diverse domains, contributing effectively to data-driven decision-making processes.

The "Data Science Essentials" module equips learners with the essential knowledge and skills needed to excel in the realm of data science, specifically focusing on Azure Machine Learning. Through a series of Instructional Units (IU), participants will delve into foundational concepts and practical applications.

In the initial learning units, participants will receive a comprehensive introduction to data science and the utilization of Azure Machine Learning for model development. The module guides learners through the intricacies of working with data and compute resources, laying a solid foundation for effective dataset processing and management.

The module progresses to training models using scripts, ensuring learners acquire the scripting proficiency necessary for successful model development. Emphasis is placed on optimizing model training, honing the learners" ability to fine-tune machine learning algorithms for superior predictive analytics outcomes.

Furthermore, the module explores the critical aspects of managing and deploying models, emphasizing the seamless integration of data science solutions into real-world scenarios. Through hands-on projects utilizing Microsoft Azure Machine Learning, participants apply their knowledge to develop end-to-end data science projects, covering pre-processing, training, testing, optimization, and deployment.

By the conclusion of the module, learners will have gained a profound understanding of data science essentials and Azure Machine Learning, showcasing proficiency in handling diverse datasets, scripting for model training, and effectively deploying models. The hands-on projects will empower participants to translate theoretical knowledge into practical solutions, ensuring they are well-prepared for the dynamic field of data science. This module equips learners with versatile skills and a deep understanding of essential concepts, enabling them to contribute meaningfully to the evolving landscape of data science.

The "Capstone Project-Data Science and AI" module stands as the culmination of the data science program, providing learners with the opportunity to apply their knowledge and skills to a real-world data analytics project. Initially, learners focus on project proposal and planning, gaining proficiency in defining objectives, outlining scope, and developing a comprehensive plan to guide the entire project. Moving forward, attention shifts to data acquisition and preparation, where participants explore techniques for acquiring data, performing cleaning and pre-processing tasks, and ensuring data quality.

As learners progress, they engage in exploratory data analysis, utilizing visualization and statistical techniques to gain insights, identify patterns, and uncover relationships within the dataset. Subsequently, the focus turns to data analytics and modelling, where participants apply advanced analytics techniques, including machine learning algorithms and predictive modelling, to extract meaningful information and make data-driven decisions.

Further progression leads to an emphasis on results evaluation and interpretation, teaching learners to assess the effectiveness of their data analytics models, interpret results, and draw actionable insights. The final stage centres on presentation and documentation, where participants develop skills in effectively communicating findings, creating compelling visualizations, and documenting the entire project process.

Throughout the module, participants undertake a capstone project that integrates all learned skills and knowledge. This comprehensive data science project involves validating, transferring, integrating, and deploying models using Microsoft Azure Machine Learning, providing hands-on experience in working with real-world datasets, applying data analytics techniques, and effectively presenting findings.

By successfully completing this module, learners demonstrate their ability to conceptualize and execute a data analytics project from start to finish. Proficiency in project management, data acquisition and preparation, exploratory data analysis, analytics modelling, results evaluation, interpretation, and effective presentation and documentation prepares learners to tackle complex data challenges and contribute to data-driven decision-making processes across various domains.