F71BA - Practical Data Science

Aims

This course provides an introduction to the fundamental concepts of practical data science. It explores data management topics, descriptive, predictive, and prescriptive analytics, along with effective data storytelling to communicate insights. By the end of the course, students will be equipped with a foundational understanding of how data is used to inform decision-making in organisations.

Syllabus

1. Data Management (1.1 Understanding data types structured/unstructured/semi-structured, temporal/spatial , 1.2 Sources of business data: Internal vs. External; Open Source , 1.3 Data quality: Accuracy, completeness, consistency)

2. Big Data (2.1 Overview of Big Data: Definition, characteristics Volume, Velocity, Variety, Veracity, Value , 2.2 Big Data technologies and ecosystem , 2.3 Introduction to Cloud Computing for Big Data Scalability, flexibility, cost-efficiency)

3. Introduction to Descriptive Analytics (3.1 Descriptive analytics: What happened and why? , 3.2 Key performance indicators KPIs and metrics , 3.3 Use cases: Sales reporting, customer segmentation, marketing analytics , 3.4 Basic data summarisation cross-tabulation, pivot tables)

4. Predictive Analytics (4.1 Predictive analytics: What will happen? , 4.2 Overview of regression analysis linear, multiple , 4.3 Introduction to classification models logistic regression, decision trees , 4.4 Model evaluation train/test split, accuracy, confusion matrix , 4.5 Time series analysis and forecasting , 4.6 Introduction to machine learning algorithms KNN, random forests , 4.7 Cross-validation and model tuning , 4.8 Overfitting and underfitting in predictive models)

5. Prescriptive Analytics (5.1 Prescriptive analytics: What should we do? , 5.2 Optimisation techniques linear programming, decision rules , 5.3 Simulation models Monte Carlo simulations , 5.4 Use cases: resource allocation , 5.5 Multi-criteria decision-making , 5.6 Advanced simulation techniques , 5.7 Sensitivity analysis and scenario planning , 5.8 Case studies in prescriptive analytics pricing strategy, inventory management)

6. Data Storytelling and Visualisation (6.1 Importance of data storytelling in business analytics , 6.2 Principles of effective data communication clarity, simplicity, relevance , 6.3 Common chart types and when to use them bar charts, line charts, heatmaps , 6.4 Crafting narratives around data insights , 6.5 Communicating data-driven decisions to non-technical stakeholders , 6.6 Case studies: Examples of effective and ineffective data storytelling , 6.7 Best practices for combining data, visuals, and narratives.)