Core courses

Analytics core

• Applied statistics

  Using state-of-the-art business software, build a critical understanding of statistical models, including issues of credibility, overfitting and generalisation. Learn how to communicate and reason with data models.
  
  Key concepts taught:

  
  

  • R and R studio for data analysis and decision making
  • Multiple, logistical and nonlinear regression analysis
  • Inferential Statistics, including sampling and Bootstrapping.
• Intro to R for Data Science

  Sometimes referred to as the “golden child” of Data Science, R is a vital programming language for all big data analysts.
  
  Key concepts taught:

  
  

  • Write and use R script files and functions
  • Loops
  • Basic plotting
  • Data modelling
  • Graphics.
• Intro to Python for Data Science

  One of the most popular programming languages, Python is an important part of your data analysis toolkit.
  
  Key concepts taught:

  
  

  • Conditional statements and loops
  • Work with strings, lists and dictionaries
  • Read and write data
  • Pandas for data analysis
  • Time series and data frames
  • Matplotlib for visualisation.
• Data Science for Business I

  It is now relatively cheap to collect, store and retrieve data, thanks to widespread use of the world wide web and advances in computer technology. Learn the fundamentals of data science and the data science project cycle, identifying applications of data mining in business problems.
  
  Key concepts taught:

  
  

  • Predictive modelling
  • Regression-based methods for data mining
  • Model selection
  • Data fitting and over fitting
  • Model testing
  • Cross-validation and learning curves
  • Classifications
  • Clustering.
• Data Science for Business II

  Next, learn to identify applications of data mining in more complex business problems. Assess which learning algorithms best suit different situations and master data mining tools for data exploration.
  
  Key concepts taught:

  
  

  • Clustering
  • Regression trees
  • Nearest neighbours
  • Advanced classification methods, such as Naïve Bayesian learning.

• Data Visualisation and Storytelling

  Understand how to effectively communicate information about data. Use graphical, verbal and visual means targeting three major audiences: data experts (e.g. Head of Analytics); consumer and presentation experts (e.g. Chief Marketing Officer); and executive leadership (e.g. Chief Executive Officer).
  
  Key concepts taught:

  
  

  • Visualisation tools in Tableau & R
  • Design principles for effective charts and graphs
  • Visualising different types of data (e.g. categorical time series and geospatial data)
  • Effective dashboard design and digital presentations.
• Data Management

  Explore the fundamentals of data storage and build essential skills in data cleansing and retrieval. Learn to facilitate data usage to ensure data quality in organisations and data science projects.
  
  Key concepts taught:

  
  

  • Data management, including RDMS and ERD
  • Database design
  • Data analysis with SQL
  • Data warehouses and data lakes
  • Internet database environments.
• Machine Learning for Big Data

  How do you use dimension reduction techniques to deal with large data? Use contemporary machine learning methods to analyse large data sets and build your understanding of how to use text data for prediction, classification and data exploration.
  
  Key concepts taught:

  
  

  • Dimension reduction
  • Text mining
  • Discriminant Analysis
  • Neural networks
  • Processing text data and text mining.
• Decision Analytics and Modelling

  Use data to turn real-world problems into actionable business decisions.
  
  Key concepts taught:

  
  

  • Decision Modelling
  • Linear, Convex and Integer programming
  • Risk Analysis
  • Monte Carlo simulation
  • Decision trees.