MBA with Diploma in Data Science - 12 months

Data reduction is a key process in business analytics projects. In this unit, learners will learn data reduction

methods such as PCA, factor analysis and MDS. They will also learn to form segments using cluster analysis

methods. Forming segments and then analysing is a key technique for large groups of data and their intrinsic

information comes out in detail once segmented thoughtfully.

The objective of this unit is to discuss time series forecasting methods. Learners will analyse and forecast

macroeconomic variables such as GDP and inflation. Panel data regression methods will also be discussed in

this unit.

In this unit, learners are introduced to model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing and clinical research and this unit covers detailed model building processes for binary dependent variables. In addition, multinomial models and ordinal scaled variables will also be discussed.

This unit provides a strong foundation for predictive modelling. Its objective is to define the entire modelling process with the help of real life case studies. Many concepts in predictive modelling methods are common and therefore, these concepts will be discussed in detail in this unit. A good understanding of predictive modelling leads to a smart data scientist as many business problems are related to successfully predicting future outcomes.

This unit provides learners with an in-depth understanding of statistical distribution and hypothesis testing. Statistical distributions include Binomial, Poisson, Normal, Log Normal, Exponential, t, F and Chi Square. Parametric and non-parametric tests used in research problems are covered in this unit. The unit will help learners to formulate research hypotheses, select appropriate tests of hypothesis, write mainly R programs to perform hypothesis testing and to draw inferences using the output generated. Learners will also study planned experiments as part of the unit.

This unit provides learners with an in-depth understanding of R and Python programming and the

fundamentals of statistics. This includes writing R and Python commands for data management and basic

statistical analysis. The unit will help the learner to understand and perform descriptive statistics and present

the data using appropriate graphs/diagrams and serves as a foundation for advanced analytics. Most industry

analysis starts with Exploratory Data Analysis and a thorough study of this will help learners to perform data

health checks and provide initial business insights.

The convergence of Cloud computing, Big Data, Artificial Intelligence and The Internet of Things will see organisations of all shapes and sizes either survive and thrive or face extinction. New operational and strategic norms, types of organisations, the nature of work and employment are changing fundamentally across vast parts of the global economy. This unit introduces learners to the strategic and managerial challenges generated by the impact of digital technology on business and organisations.

In this , learners will learn how to analyse unstructured data using text mining. The focus will be on

sentiment analysis of text data, including data available on social media. For building interactive web apps

straight from R, the concept of the “SHINY” package will be introduced. Big Data concepts and artificial

Intelligence will be covered in the unit, as well as an introduction to SQL programming and how it is used to

handle data

Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this unit, learners will understand applications of various machine learning algorithms for classification problems.