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Data Science and Big Data Analytics are exciting new areas that combine scientific inquiry, statistical knowledge and computer programming.

Organisations are turning to customer data in order to innovate and respond quickly to shifts in the market. Meanwhile, Governments are using to data to help guide policy decisions, making this a prime area for social scientists with an interest in quantitative methods.

This course aims to provide an introduction to the quantitative analysis of data, blending classical statistical methods with recent advances in computational and machine learning. You will cover key topics such as the challenges of analysing big data using statistical methods, and how machine learning and data science can aid in knowledge generation and improve decision-making. You will also explore quantitative methods of text analysis, including mining social media and other online resources.

Engaging with leading faculty, you will cover the main analytical methods from this field and the hands-on application of these methods using example datasets. As a result, the course allows you to gain experience and confidence in using the methods covered during the course in different contexts.

Prerequisites: Students should already be familiar with quantitative methods at an introductory level, including linear regression analysis. Familiarity with computer programming or database structures is a benefit, but not formally required.

If you are not using R, we strongly encourage you to familiarise yourself before the start of the course. This will enable you to spend less time building familiarity with the tools, and more time focussing on the methods.

Level: 300 level. Read more information on levels in our FAQs

Fees: Please see Fees and payments

Lectures: 36 hours

Classes: 18 hours

Assessment: Two take-home assessments

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU)

Please note: Assessment is optional but may be required for credit by your home institution. Your home institution will be able to advise how you can meet their credit requirements. For more information on exams and credit, read Teaching and assessment

This course is suitable if you already have prior training in quantitative methods and coding, and want to enhance this training with the fundamental concepts and techniques of Data Science and Data Analytics. It is also suitable for practitioners from industry, government, or research organisations with some basic training in quantitative analysis or computer programming.

The course surveys diverse techniques and methods, making it an ideal foundation for more advanced or specific training. If you are targeting a role in government, politics, data science, research, law, business management, consulting or marketing you should consider this course.

• Gain an introduction into the challenge of working with big data using statistical methods

• Understand how to integrate the insights from data analytics into knowledge generation and decision-making

• Analyse how to acquire data, both structured and unstructured; process it, store it, and convert it into a format suitable for analysis

• Discuss approaches to normalising data, using a database manager (SQLite) and working with SQL database queries

• Understand the basics of statistical inference including probability and probability distributions, modelling and experimental design

• Gain an overview of classification methods and related methods for assessing model fit and cross-validating predictive models

• Analyse the difference between supervised and unsupervised learning approaches

• Discuss quantitative methods of text analysis, including mining social media and other online resources

The design of this course is guided by ÐÓ°ÉÂÛ̳ faculty, as well as industry experts, who will share their experience and in-depth knowledge with you throughout the course.