Most industry analysis starts with exploratory data analysis and a thorough study of this will help you to perform data health checks and provide initial business insights. You will gain a sound understanding of R and Python programming, as well as the fundamentals of statistics. This includes writing R and Python commands for data management, basic statistical analysis, performing descriptive statistics and presenting data using appropriate graphs and diagrams. This unit serves as a foundation for advanced analytics.
In this unit you will gain an in-depth understanding of statistical distribution and hypothesis testing. This includes Binomial, Poisson, Normal, Log Normal, Exponential, t, F and Chi Square distributions, as well as parametric and non-parametric tests used in research problems. The unit will help you to formulate research hypotheses, select appropriate tests for them, write R and Python programs to perform hypothesis testing and draw inferences using the outputs generated.
A good understanding of predictive modelling is an essential part of being an effective data scientist as many business problems are related to successfully predicting future outcomes. This unit provides a strong foundation for predictive modelling and covers the entire modelling process in the context of real life case studies. Many concepts in predictive modelling methods are commonly used in business and therefore these concepts will be discussed in detail.
In this unit you will learn model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing and clinical research and detailed model building processes for binary dependent variables are covered. In addition, multinomial models and ordinal scaled variables will also be discussed.
In this unit, time series forecasting methods are introduced and explored. You will analyse and forecast macroeconomic variables such as GDP and inflation, as well as look at complex financial models using ARCH and GARCH, ARIMA, time series regression, exponential smoothing and other models.
Data reduction is a key process in business analytics projects and you will learn to apply data reduction methods such as principal component analysis, factor analysis and multi- dimensional scaling. They will also learn to segment and analyse large data sets using clustering methods, another key analytical technique that brings out rich business insight if carried out skillfully.
Machine learning algorithms are new generation algorithms and used in conjunction with classical predictive modelling methods. In this unit learners will understand applications of various machine learning techniques including the Naïve Bayes Method, Support Vector Machine Algorithm, Decision Tree, Random Forest, Association Rules and Neural Networks.
In this unit, learners are introduced to further key knowledge areas associated with data science. This includes analysis of unstructured data using Text Mining, handling data with SQL and building interactive web apps straight from R using the Shiny package. Learners also explore the Hadoop framework and further concepts in Big Data Analytics and Artificial Intelligence.
This is a business focussed unit that introduces you to key business concepts at a postgraduate level to complement your data science skills and knowledge. It covers topics including leadership skills, entrepreneurship, innovation, ethics and sustainability, globalisation and organisational culture, encourging you to evaluate data science within wider contexts.