Programme Content
What you Learn: How you Learn:
Understand the progression and evolution of data science from analytics to Machine Learning and more recently Generative AI
Recognise the communicative power of analytics and its role in decision-making
Consider the evidence for Generative AI’s capabilities strengths and limitations
Explore different state of the art Generative AI models and understand the importance of prompt engineering
Address concerns related to data hallucinations and the importance of verifying insights
Discuss data privacy issues with generative AI and ways to overcome related challenges
Engage practically with ChatGPT loading data and conversing to derive actionable insight
Distinguish between visualisation as a discovery tool and as a tool to communicate insights
Describe the principles of good data visualisation and some of the pitfalls
Explore chat GPT and its “code interpreter” capability for efficient visualisations
Understand and apply storytelling principles using data
Engage in hands-on practice of visualisation techniques with real-world datasets
Present examples on how firms generate value using supervised learning
Discuss three key applications for supervised learning: inference prediction classification
Understand the model of linear regression and the ordinary least squares algorithm
Quantify the relationship between variables the notions of statistical error confidence intervals and p-values
Apply the model of linear regression on large real world datasets
Use the framework of linear regression to conduct A/B testing
Appreciate why correlation does not imply causality
Recognise the importance of transparency and reproducibility in the hypothesis testing process and master documentation workflow
Explore relationships between multiple variables within linear regression framework
Understand how to control for confounding variables
Engage in feature engineering to enhance predictive capabilities
Become familiar with decision tree-based models and random forests
Apply linear regression techniques to make predictions
Extend the model of linear regression to make predictions for categorical variables
Understand the fundamentals of logistic regression and the maximum likelihood algorithm
Understand risk scoring its relevance and how it’s used in classification
Master the interpretation and application of the confusion matrix ROC (Receiver Operating Characteristic) and AUC (Area Under the Curve) metrics
Understand the challenges in prediction like overfitting and distinguish between training vs. testing datasets
Understand and apply nearest neighbour algorithms for various use-cases
Dive deep into clustering algorithms focusing primarily on k-means and hierarchical clustering methodologies
Develop an understanding of how datadriven recommendation engines operate
Synthesise the skills and techniques learned throughout the course in a comprehensive capstone case
Learn how two industry experts working in different industries are leveraging analytics to make an impact
On this hands-on GenAI Optimisation course you’ll delve deep into the world of data analytics without writing a single line of code.
Use case studies and real-world datasets to make quantitative predictions evidence-driven recommendations and generate high quality data visualizations.
Use the latest ChatGPT as a data scientist assistant to help you be ready to apply tools and techniques to your own datasets with confidence.
Learn online from anywhere in the world working through six modules at your own pace in the space of six weeks.
Engage with a diverse community of peers from many different industries and businesses helping you build new connections and expand your network.
Take guidance from a dedicated learning manager who will help you navigate your learning journey and reach your objectives.
