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 learning 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.

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.

In addition, learners will 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.

The module culminates with a focus on data visualization using Matplotlib. 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 and Matplotlib. 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 in Python programming, data manipulation with Pandas, numerical computing with NumPy, and data visualization with Matplotlib. They will be able 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 'Data Science Essentials' module is designed to provide learners with a solid foundation in essential knowledge and skills required in the field of data science. Through a comprehensive set of Instructional Units (IU), learners will acquire the necessary expertise to excel in various aspects of data science.

In IU 1, learners will be introduced to the fundamentals of data science. They will gain an understanding of key concepts, methodologies, and the role of data science in decision-making processes. This IU sets the stage for learners to explore the exciting field of data science.

IU 2 focuses on data validation and transformation. Learners will learn techniques to assess the quality and integrity of data, identify errors or inconsistencies, and implement appropriate validation processes. They will also acquire skills in transforming data to ensure its suitability for analysis.

The IU 3 delves into data transfer and modelling. Learners will explore different methods and tools for transferring data between systems or platforms. They will also acquire knowledge of data modelling techniques to organize and structure data in a way that supports analysis and decision-making.

IU 4 introduces learners to data integration using Microsoft Azure. They will gain hands-on experience in leveraging Azure services to seamlessly integrate data from various sources, ensuring a unified view for analysis. This IU equips learners with the skills to harness the power of cloud computing in data integration.

Finally, IU 5 focuses on the deployment and management of data science models on Microsoft Azure. Learners will gain insights into the process of deploying models into production environments and managing their lifecycle. They will also learn best practices for monitoring, updating, and maintaining data science models.

Throughout the module, learners will engage in a practical project that encapsulates the key learnings. They will develop a data science project that involves validating, transferring, integrating, and deploying models using Microsoft Azure. By working on this project, learners will apply their knowledge and skills in a real-world scenario, gaining hands-on experience and solidifying their understanding of data science essentials.

In summary, the 'Data Science Essentials' module equips learners with the knowledge and skills necessary to excel in the field of data science. From data validation and transformation to data integration and model deployment on Microsoft Azure, learners will develop a comprehensive understanding of essential data science concepts and practices. By completing the project, learners will demonstrate their ability to apply these skills to real-world scenarios, preparing them for success in data-driven industries.

The 'Principles of Machine Learning' module equips learners with the essential knowledge and skills required to understand and apply machine learning techniques. Through a comprehensive set of Instructional Units (IU), learners will acquire a strong foundation in the principles and methodologies of machine learning.

In IU 1, learners will be introduced to classification, a fundamental concept in machine learning. They will explore various algorithms and techniques to classify data into different categories, enabling them to make predictions or decisions based on input features.

IU 2 focuses on regression, another key aspect of machine learning. Learners will learn how to build models that can predict continuous numeric values. They will gain insights into different regression algorithms and understand how to evaluate and interpret their performance.

IU 3 delves into improving machine learning models. Learners will explore techniques to enhance model performance, such as feature selection, dimensionality reduction, and model evaluation metrics. They will learn how to fine-tune models and optimize their hyperparameters for better accuracy and generalization.

In IU 4, learners will delve into tree and ensemble methods. They will gain an understanding of decision trees, random forests, and other ensemble techniques that leverage the power of combining multiple models to achieve better predictive performance.

IU 5 introduces learners to optimization-based methods. They will explore optimization algorithms such as gradient descent and genetic algorithms, enabling them to optimize model parameters for improved performance.

Throughout the module, learners will apply their knowledge and skills through a practical project. They will develop a data science project that involves validating, transferring, integrating, and deploying models using Microsoft Azure. This project will allow learners to apply the principles of machine learning in a real-world scenario, enhancing their understanding and practical abilities.

By completing this module, learners will gain a comprehensive understanding of the principles of machine learning. They will acquire the skills necessary to implement various machine learning techniques, optimize models, and deploy them using Microsoft Azure. The combination of theoretical knowledge and hands-on experience gained in this module will empower learners to apply machine learning in a wide range of domains and contribute to data-driven decision-making processes.

The 'Capstone Project-Data Analytics' module serves as the culminating experience for learners in the data science program, allowing them to apply their knowledge and skills to a real-world data analytics project. Through a series of Instructional Units (IU), learners will acquire the necessary expertise in various stages of the data analytics process.

In the first IU, learners will gain proficiency in project proposal and planning. They will learn how to define project objectives, outline the scope of work, and develop a comprehensive plan to guide the entire project.

IU 2 focuses on data acquisition and preparation. Learners will explore various techniques for acquiring data from different sources, perform data cleaning and preprocessing tasks, and ensure data quality and consistency.

IU 3 covers exploratory data analysis, where learners will employ visualization and statistical techniques to gain insights, identify patterns, and uncover relationships within the dataset.

In IU 4, learners will delve into data analytics and modelling. They will apply advanced analytics techniques, such as machine learning algorithms and predictive modelling, to extract meaningful information and make data-driven decisions.

IU 5 emphasizes results evaluation and interpretation. Learners will learn how to evaluate the effectiveness of their data analytics models, interpret the results, and draw actionable insights.

Finally, IU 6 focuses on presentation and documentation. Learners will develop skills in effectively communicating their findings, creating compelling visualizations, and documenting the entire project process.

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

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