(SCTP) Data Analytics and Business Intelligence Programme

Module 1: Data Fundamentals, Database Management (SQL), Data Wrangling and Cleaning (OpenRefine, Trifacta Wrangler)

• Differentiate important data concepts including between data, information, and knowledge, and identify the various types of data and data quality issues
• Explain the fundamentals of relational databases and the importance of database normalisation
• Use SQL syntax query to manipulate and manage data within databases effectively
• Utilise tools such as OpenRefine and Trifacta Wrangler for cleaning, transforming, and enriching data

Module 2: Python for Data Analysis

• Develop a comprehensive understanding of Python syntax including data types, control flow, and functions
• Produce well-structured, maintainable Python code
• Master numerical computations and array operations using the NumPy library

Module 3: Statistics and Data Visualisation with libraries like Matplotlib and Seaborn

• Grasp fundamental statistical concepts such as descriptive statistics, probability distributions, hypothesis testing, and regression analysis
• Apply appropriate statistical techniques to data analysis
• Create effective visualisations, tailored to specific audiences using Matplotlib and Seaborn
• Communicate data insights through effective storytelling

Module 4: Machine Learning

• Define machine learning and its various applications, including supervised, unsupervised, and reinforcement learning
• Build and evaluate predictive models using algorithms such as linear regression, decision trees, and neural networks, addressing business problems like customer churn and fraud detection
• Implement data preprocessing, feature engineering, and hyperparameter tuning to optimise model performance
• Leverage machine learning within data analytics and business intelligence workflows, while considering ethical implications and decision-making processes

Module 5: Business Intelligence Concepts and Tools

• Explain the fundamental components of Business Intelligence (BI) and define its role in organisations
• Apply popular BI tools, such as Power BI and Qlik, to extract, transform, and load (ETL) data, solving real-world business challenges
• Evaluate how BI techniques support data-driven decision-making and enhance organisational performance
• Assess the effectiveness of BI implementations and their contribution to overall business performance

Module 6: Capstone Project on Data Analytics and Business Intelligence Programme

• Synthesise and apply data mining, statistical analysis, machine learning, and data visualisation methods to solve complex business problems encountered in the Capstone Project
• Perform the entire data analysis process, including data acquisition, cleaning, exploration, modelling, visualisation, and utilising skills acquired in the previous five modules
• Develop interactive dashboards and reports using business intelligence tools, effectively communicating insights to stakeholders
• Identify and address data-related challenges, and present findings to varied audiences, including business executives, technical teams, and potential employers