Working with Data
This module teaches practical data analytics skills, including data extraction, cleaning, transformation, and analysis. Learners acquire programming knowledge and learn to work with various tools, frameworks, and libraries for data manipulation and modeling. The module emphasizes data visualization, summary statistics, and basic data handling techniques. It also covers working with dates, strings, and other variables to enhance data research and visualization. The learning outcomes include analyzing data using visualization tools, acquiring basic data handling skills, using the pipe operator for data tidying operations, and working with various data analysis techniques and Python libraries.
Data Analytics in Business Processes
This module covers reliable spreadsheet models, mathematical translations, and their application in Excel. It includes Excel functions, auditing for accuracy, and Decision analysis. Microsoft Power BI enables practical knowledge extraction from data, facilitating corporate decision-making. Learning outcomes include critical analysis of business data, understanding business analytics principles, applying data management techniques, and using statistical analysis for informed decisions. Content covers creating spreadsheet models, What-If analysis, predictive modeling, Decision Analysis, and Power BI features such as data streaming, visualization, and sorting.
Data Mining Techniques
The module covers data mining techniques, including data processing, pattern discovery, and trends analysis. Learners use Python matrix libraries to perform tasks like classification, segmentation, and forecasting. The learning outcomes include understanding text mining fundamentals, applying scalable pattern discovery techniques, and discussing pattern evaluation metrics. The content covers data mining introduction, Python-based environment, data warehouses, finding patterns, database mining, text representation, and exemplary techniques such as bag of words and frequent subgraph mining.
Algorithms in Data Science
This module provides knowledge on data splitting and model development for predictive mining. It covers various performance metrics and popular modeling approaches such as neural networks, support vector machines, and decision trees. The module focuses on addressing prediction challenges when traditional methods fail. The learning outcomes include understanding algorithmic concepts, sorting data, using data structures, and utilizing industry-standard techniques like logistic regression and ensemble modeling.
Specialisation Modules - Statistical Data Modelling
This module teaches learners predictive modeling, particularly linear regression. It covers complex statistical methodologies like generalized linear and additive models for modeling real-world interactions. The module focuses on developing proficiency in linear regression analysis, experimental design, and extended linear and additive models, enabling learners to interpret data, discover relationships, and make predictions effectively. The learning outcomes include mastering linear regression, understanding various model algorithms, analyzing logistic regression results, and maximizing analytical productivity. The content covers topics such as sample selection, estimation, hypothesis tests, regression models, Tableau data modeling, and data transformation.
Specialisation Modules - Applications of Data in Artificial Intelligence & Blockchain
This module provides a comprehensive understanding of AI applications in business, including AI decision-making and its connection to IoT and Blockchain. Learners will explore algorithms and their potential for enhancing or replicating human behavior across diverse applications. The curriculum covers AI, IoT, Blockchain, and machine learning components, offering a solid conceptual framework to tackle real-world challenges. The learning outcomes include introducing AI in the business domain, creating AI implementation plans, exploring Blockchain components, and understanding the impact of IoT in applied business models. Additionally, the module delves into Deep Learning, NLP, and the structure and benefits of Blockchains.
Capstone Project - Research Methods & Dissertation
This module focuses on the role of Data science in organizations, emphasizing its impact on performance and competence. It aims to develop learners' understanding and skills in research, design, and systematic study. Learners are encouraged to choose a data science project that reflects their past learning. The module covers various aspects of Data Science, including data visualization, probability, inference, modeling, data mining, regression, and machine learning. It also highlights the significance of data analytics and modeling in planning, decision-making, and organizational change. By completing the module, participants gain comprehensive knowledge and a data product to showcase their expertise to employers or educational programs. Learning outcomes include independent research, problem-solving, effective communication, and professional documentation in the field of Data Science.
