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Diploma in Data Science

Diploma in Data Science

Diploma in Data Science

The Graduate Diploma in Data Science (1-year), with all academic direction from the London School of Economics, aims to prepare graduates for a career as a data scientist, in the corporate sector, for entrepreneurship, public policy or even academia. While focusing on the core statistical, quantitative and computing skills required in these careers, this Data Science course also arms students with business domain knowledge so they can add value as data scientists.

What is Data Science?

Rapid digitisation and tremendous growth in computing power have made it possible to collect and store copious amounts of data. The science of analysing this data, which is often messy and unstructured, to discern patterns and make predictions, is called Data Science. Its applications are widening every day, and already range from politics to healthcare to retail marketing. Another related and common question is: what is Data Science and Analytics, or what is the difference between Data Science and Data Analytics? Broadly speaking, Data Analytics primarily involves the extraction of insights from various sources of data. It is thus a subset of Data Science, which also incorporates data cleaning and preparation, including through the use of machine learning, and making future predictions using the data.

Why is Data Science important, or Why Data Science?

Given the humongous amount of data we’re collecting in today’s world, it is easy to see why Google’s Chief Economist and eminent economic author Hal Varian famously said in 2009 that “The sexy job in the next 10 years will be statisticians”. Analysing all this data gives companies and organisations the ability to make more informed business decisions, and solve real-world problems based on evidence, across sectors and industries. This helps to drive gains in efficiency, productivity and eventually in the bottom-line. In turn, this helps make Data Scientists one of the sexiest avatars of statisticians for the coming decades.

How to start a career in Data Science?

In terms of skills, data science lies at the intersection of mathematics, computing, statistics and business/domain knowledge. Therefore, a good way to start a career in Data Science is to undertake a programme which trains you in, or hones, as many of these skills as possible. In this context, it is worth mentioning that the combinations of these skills is quite scarce today, as are programmes which integrate the business/management skills of data-driven decision-making with the core quantitative skills needed to analyse data. This is what makes the Graduate Diploma in Data Science a unique programme for those wondering how to learn Data Science, or how to start learning Data Science, in order to launch a career as a data scientist.

Are you ideal for this course?

You are well-suited to pursue the Graduate Diploma in Data Science if:

  • You have a strong quantitative undergraduate degree, such as in Statistics, Mathematics, Computing, Economics, the physical sciences, or engineering, to name a few
  • You enjoy working with numbers to glean trends and patterns from them
  • You want to pursue a rigorous Data Science programme, with applications in social, political, economic, legal, business and marketing fields.

Why the Graduate Diploma in Data Science at ISBF?

  • All academic input and direction for the programme comes from the London School of Economics (LSE), which is one of the top schools in the world for economics, statistics, econometrics, management and finance
  • LSE faculty members have developed this programme and the curriculum and study material for it, and they set and grade the final examinations for all courses on the programme
  • ISBF is one of the select few Teaching Institutions featured on LSE’s website, for the University of London programmes
  • Most universities in Canada and the US accept the Graduate Diploma as the 16th year of education. Several ISBF students have obtained Master’s offers from prestigious institutions in Europe, US and Canada.
  • ISBF graduates have a strong track record of securing year-long work placements in London and New York with leading investment banks. Graduates also receive training and a plethora of placement opportunities within India.

How to Get a Job in Data Science?

The best way to get a job as a data scientist is to get placed from a Data Science programme. Given the rigour of the Graduate Diploma and the long-standing corporate connections of ISBF’s Career Services Division (CSD), graduates of this programme can expect strong placements. CSD also organises various lectures, workshops and industry visits to give students hands-on experience of the corporate world, and trains graduates extensively in order to make them job-ready. All these help students make the most of the placement opportunities that CSD arranges around the year. In recent years, ISBF graduates have been placed in firms like S&P Capital IQ, Ernst & Young, Moody’s Analytics, IHS Markit, Citibank and Walmart, to name a few. Jobs for Data Science graduates may range from asset management to operations, and from marketing to banking and other financial services.

Programme Structure

At this stage, it is important to ask – what is the Data Science syllabus or programme structure, and what will your learning outcomes be as a graduate of the Data Science diploma? In the Graduate Diploma programme, you will learn to model and interpret data, and to assist management in making data-driven decisions. This programme will help you understand:

  • The techniques and subject matter based on theoretical statistics and machine learning to analyse and interpret information and predictions from unknown datasets
  • How to perform independent data analysis by selecting appropriate statistical methods for a given situation and drawing appropriate conclusions following empirical analysis to form the basis of managerial decision-making
  • How to use statistical software to analyse datasets

You will study four courses on this programme – Machine Learning, Business Analytics – Applied Modelling and Prediction, Elements of Econometrics, and Information Systems Management. By the time you graduate, you will have learnt social, political, economic, legal and business applications of Data Science.

This module helps students understand the management of information systems in organisations that affect the design and implementations of Information and Communication Technologies (ICTs). Besides offering a basic principles of project management and governance, it overviews the common techniques associated with ICT management and the tools used to manage them. Overall, the study of this module provides a holistic view of benefit management and information systems strategy alignment.  The topics covered include:

  1. Background and models of information systems management
  2. Managing information systems projects
  3. Information systems and benefit management

This module helps the students to study a broad range of model based and algorithmic machine learning methods illustrated in various real-world applications and datasets. Simultaneously, the students also gain a theoretical foundation of the methodology. The topics covered include:

  1. Linear regression and regularisation (via least squares and maximum likelihood)
  2. Bayesian Inference
  3. Classification
  4. Resampling methods
  5. Clustering
  6. Non-linear models
  7. Tree-based methods
  8. Support Vector Machines
  9. Random forests
  10. Gaussian Processes

The module develops a deep understanding of econometrics to equip students to identify and evaluate the most applied analysis of cross-sectional data and be able to independently undertake such analysis themselves. Some of the topics include:

  1. Random variables and sampling theory
  2. Simple regression analysis
  3. Properties of the regression coefficients
  4. Multiple regression analysis
  5. Transformation of variables
  6. Dummy variables

The module teaches students to apply modelling at varying levels of the management process, understand basic principles of complex multivariate datasets analysis-extracting relevant data, and be able to demonstrate the wide applicability of mathematical models, identifying the limitations and possible misuse. Some of the topics included are:

  1. Introduction to data analysis and decision-making
  2. Time series data
  3. Outliers and missing values
  4. Pivot tables
  5. Probability distributions
  6. Decision making under uncertainty
  7. Methods for selecting random samples
  8. Nonparametric tests
  9. Stepwise regression
  10. Time series forecasting
  11. Regression-based trend models
  12. The random walk model
  13. Autoregressive and moving average models
  14. Exponential smoothing
  15. Seasonal models
  16. Introduction to linear programming
  17. Product mix models
  18. Sensitivity analysis
  19. Monte Carlo simulation
  20. Applied simulation examples

Note: The “Graduate” Diploma in Data Science is a “postgraduate” diploma or qualification, since only students who have completed an undergraduate degree are eligible for it. The nomenclature “Graduate” Diploma is used because this diploma is a University of London award, and in the UK, “postgraduate” students are referred to as “graduate” students, while students pursuing “graduation” are referred to as “undergraduate” students.