MSc Data Science

This module aims to provide you with the programming skills you will need to carry out for programming tasks you will encounter in the other modules in this programme. You'll learn about general programming techniques such as variables, functions and control flow. You'll explore how to work with different types of data structures such as arrays and dictionaries.

You will develop data processing pipelines, which allow you to convert raw data into data that you can analyse. You will apply mathematical and statistical procedures to data. You'll learn how to plot graphs of various types. You'll also familiarise yourself with an industry-standard data science programming environment which you can use throughout the programme.

The following is an indicative list of topics:

1. Data structures
2. Reading and writing data on the filesystem
3. Retrieving data from the web
4. Retrieving data from databases using query languages
5. Cleaning and restructuring data, part 1
6. Cleaning and restructuring data, part 2
7. Data plotting
8. Version control systems
9. Unit tests
10. Data processing pipelines

This module gives you an introduction to machine learning and statistical learning. Topics include supervised learning (regression and classification as parametric and non-parametric methods), and unsupervised learning, but discusses also semi-supervised learning, reinforcement learning, and elements of the learning theory.

You'll explore recent applications of machine learning and deep learning by illustrating practically, using modern software libraries, how machine learning algorithms and methods are applied effectively, and how models’ performances can be optimised using real-world datasets. Ethical aspects related to machine learning are also discussed.

In this module, you'll learn all about Big Data which is a key element of contemporary applications of Data Science. You'll gain practical skills related to working with Big Data computing resources as well as the conceptual context of how Big Data relates to methods and technologies in statistics and computing.

We'll cover ten topics on various theoretical, methodological, and technical aspects of big data analysis. A substantial amount of time will be spent on understanding big data technologies like Apache Hadoop and computing engines like Hadoop's MapReduce and Apache's Spark.

You'll start with a brief introduction to the basics of big data and the importance of distributed computing. We'll focus on the importance of bringing computing to the distributed data sets and how Hadoop Distributed File System (HDFS) is so crucial for doing that.

We'll then move on to distributed computing framework and intuitively understand the programming methodologies like MapReduce. We consider cluster analysis as our first example for big data analysis. We use MapReduce methodology and implement k-mean clustering algorithm by performing the required computation on the cluster of computers. You'll learn the basics (that are sufficient for the application developers) about the scheduling aspects of Hadoop by knowing the details of various scheduling algorithms related to Hadoop's YARN.

Later, we change gears towards Apache Spark where we focus our attention on Spark's Resilient Distributed Datasets (RDDs) and the concepts of Spark's transactions and actions. We go through the basics of functional programming in Spark. Towards the end, we focus on implementing machine learning algorithms on Hadoop cluster using Spark framework. Finally, you'll study Recommender Systems, which are so useful in filtering the required information from the rich data sets that are usually produced by the digitalisation of businesses in the real world.

In this module, you'll learn the key statistical concepts and techniques for data analysis and statistical data mining. We'll cover some of the most instrumental methods of data analysis and statistical data mining including:

• Descriptive statistics
• Elements of probability theory including Bayes theorem
• Elements of statistical inference (statistical tests and confidence intervals)

You'll explore methods for regression and classification such as Linear Regression and Logistic Regression, Bayesian methods such as Naïve Bayes and Discriminant Analysis, K-Nearest Neighbours, and also Decision Trees which are mostly applied in ensemble based methods.

You'll learn how to apply these statistics and data mining methods to analyse various datasets, and how to interpret the results.

The module introduces you to research topics related to Data Science, for example:

• Modern applications of Machine Learning
• Data Mining, Computational Statistics
• Computational Social Sciences
• Cognitive computing
• Business analytics and its applications

In particular, you will have an opportunity to learn from Data Science and Computing industry professionals and academics whose mono-disciplinary or interdisciplinary research in areas of Computer Science, Sociology, Bioinformatics, Biomedical Statistics and other disciplines is based or involves data analysis. The module will guide your work in exploring a research theme of interest, as a preliminary phase preparing you for the Final Project. 

The module will also expose you to learning techniques of conducting research and writing scientific reports. Moreover, you will be taught approaches for devising and optimising your strategies adapted to your skills in seeking and securing an employment, whether in research or the industry sector. The module will also expose you to a discussion of the ethical and legal issues related to the area of Data Science and its applications. 

Visualisation is essential for understanding and communicating information, and for making informed decisions based on data. This module takes the view that data visualisation is a core interdisciplinary component of data science. Effective visualisation requires a combination of computational skills, statistical knowledge, an understanding of human visual perception, and a rigorous and creative approach to working with data.

In this module, you'll explore both the practical skills necessary for manipulating and visualising data, as well as the theoretical knowledge essential for making judgements about how to most effectively discover and communicate insights from data.

You'll become familiar with core visualisation tools and techniques within the Python data science ecosystem. You'll establish a critical approach to data visualisation, starting with interrogating how data is collected, continuing throughout each stage of the visualisation process culminating in the communication of information and possible real-world implications. You'll gain practice answering questions with data and build confidence in presenting your findings, ensuring they are trustworthy and valid. You will gain knowledge of the human visual system, which is essential for designing effective visual communication.

This is an extended project where you are required to work independently to develop a research idea and then to carry it out. It pulls together the streams of study, knowledge and practical skills gained in other modules into a single, long-form project.  

In this module, you'll undertake a substantial independent research project that will allow you to demonstrate a wide range of skills: project planning, management, research, and written presentation. You'll integrate the knowledge gained throughout the programme and use skills acquired in other modules in the implementation of your final project in Data Science. The work will consist of a combination of research and highly applicative elements in various proportions. For your project work you can make use of methodologies from various components of Data Science as instruments of research.   

As an alternative to undertaking an academic project under the supervision of Goldsmiths staff, students may opt to take an internship project, in which they do a work placement in which the work meets the learning outcomes of this module (and submit a final report on the work).

Through this module, you'll learn a wide range of Artificial Intelligence techniques. The module complements the Machine Learning module by focusing on Artificial Intelligence-related topics it does not cover, such as symbolic representations, modelling, task learning and game playing. Through this module, you'll widen and deepen your knowledge of Artificial Intelligence techniques, which should provide you with an additional set of methods with which you can address Data Science problems. 

This module is focused on Artificial Intelligence techniques. You'll understand the historical development of Artificial Intelligence including search, vision and planning. You will be familiar with the foundations of the agent-based approach to software design, decision making and problem-solving including under uncertainty. You'll have an opportunity to apply Artificial Intelligence techniques. 

Neural Networks are widely used techniques for modelling and classifying data. They are used in industry for data analysis applications such as image classification, speech analysis and regression tasks.

This module will provide you with specialised theoretical and practical knowledge of a range of Neural Network architectures that are appropriate for data-oriented tasks. This module is complementary to the Machine Learning and Artificial Intelligence modules in the programme, focusing on the area of neural computation.  

You'll be introduced to the theory and practice of neural computation. You'll learn the principles of neuro-computing with Neural Networks widely used for addressing real-world problems such as regression, pattern recognition and time-series prediction. 

In this module, you'll gain knowledge of machine learning from financial time series. We'll cover three main topics:

• Modelling
• Learning
• Applications.

First, you'll explore linear and nonlinear models, as well as density models of the means and variances of sequential data, with an emphasis on neural networks.

Then, you'll study computational learning with gradient descent techniques and Kalman filters for proper dynamic treatment of the models. The concepts of overfitting, generalization and performance evaluation are thought to complement the learning techniques.

Finally, we'll discuss several practical applications of neural networks and Extended Kalman Filters from the FinTech area of financial data analytics, including:

• Value-at-Risk estimation
• Option pricing
• Portfolio estimation
• Automated algorithmic trading.

Blockchain is the technology that underpins Bitcoin and other cryptocurrencies. Blockchain promises to become a dominant technology in financial and other transactions, whether cryptocurrencies thrive or die. This module will give you practical and theoretical knowledge of:

• How blockchains work
• Security issues with blockhains
• Where blockchains come from
• How to analyse competing notions for blockchains (i.e. proof of stake vs proof of work; and bitcoin vs ethereum)
• What applications there are on the horizon.  

This is both a practical and a theoretical module. It is practical in the sense that by running through the exercises you'll have designed and implemented your own blockchain by the end. It is theoretical in the sense that you will have learned the theory behind security issues and the claims for competitive paths to blockchain issues. Your implementation is meant to reflect your thinking on the theoretical issues.

This module will provide you with foundational knowledge of modern financial instruments. Such knowledge is crucial for people working in financial technology, who will have to create and understand software tools for optimising investment behaviour.

You'll gain the mathematical and qualitative tools to analyse modern financial markets, helping you develop strategies for market participants. You'll be introduced to a range of derivatives and market behaviour. We take a distinctly mathematical and computational approach, leading you to understand how financial markets work well enough to analyse, evaluate, and implement investment decisions involving financial instruments.

This module intends to broaden your knowledge beyond the Classical Linear Regression Model (CLRM) estimated by Ordinary Least Squares (OLS). You'll focus on multiple regression, violation of the assumptions of the CLRM, and issues relating to time series econometrics.

he module draws on datasets used by macroeconomists in their analysis of aggregate economic performance. You'll learn how to employ econometrics to analyse and test relationships using annual or quarterly data on investment, consumption, output (GDP), interest rates, wages and consumer and producer prices, and employment. Topics studied this term include:

• Dynamic modelling
• Conditional expectations
• Multiple regression analysis
• Heteroscedasticity
• Models using cross-section, time series, and panel data
• Autoregressive distributed lags
• Autocorrelation
• Cointegration.

This module extends your knowledge in econometric analysis by introducing you to the historical development of the tools that econometricians use, and by engaging you in the methodological and practical limitations that real world statistical analysis of social data faces. The focus of the module is the changing landscape in data collecting and inference by national governments and big organizations from the 1930a until today. You will consider core questions of doing quantitative analysis, such as how much does econometrics explain? What is the right methodology for social statistics? And what questions can and cannot be explained by data inference?

You will investigate three different aspects of the time series econometrics toolbox in depth to give context to the technical training from previous modules. These aspects are:

• A historical survey of the development of econometric techniques from the 1930s until today
• The development, limits and strengths of data collected by national governments for inference and policy analysis
• Open questions in the methodology of data analysis in the social sciences

Furthermore, you will consider how data collecting by national governments and other institutions has changed since the advent of the internet and the big data revolution. This follows naturally the progression of more data that national governments collect and collate since the beginning of the twentieth century, with the main difference being that the scale is now of a new level, and the type of data substantially different to the traditional census, national accounts or industrial production data that national governments habitually collect. New ways in which government organizations (e.g. Central Banks) use this new type of data will be presented and the analytical and methodological challenges of these new types of data will be analysed.

This module builds on the Econometrics mdoule by extending your knowledge in the field of multivariate analysis. You will gain more detailed knowledge of econometric techniques in the following fields:

• financial econometrics and other high-frequency data sets
• panel data that includes stationary and non-stationary panels
• Vector Autoregressive Models

You will also extend your knowledge of matrix algebra and its applpication and use in different topics.

Throughout this module you will not only extend your knowledge in statistical theory, but also gain practical experience in using appropriate computer packages to run statistical testing. You will enhance you knowledge of Eviews, Stata and other computer software and will also be informed of the practical issues relating to the use and limitations of these computer packages.

The objective of this module is to equip students with some of the knowledge and tools to analyse the internal and external business environment and devise marketing strategies that help to distinguish businesses from their key competitors whilst adding value to the product/service offering. The module will be divided into two section: one more theoretical and one more practice. The module will start defining the role of marketing strategy within the business strategy and the corporate strategy of the company. It will also help to differentiate the three levels, and it will highlight the relationships between these three levels of strategy. The module will then look into the process of creation of a marketing plan as a core tool for the definition of the strategy. The marketing planning process will start from understanding the market opportunities of the company, through the identification of attractive market segments, to the differentiation and brand positioning. The module will then move on the formulation of marketing strategies such as marketing strategies for new market entries, growth markets strategies, mature and declining markets strategies. Finally, students will learn how to implement and control strategy, and to measure effectively the performance of a specific strategy. In the second section, students will be required to complete a business simulation. In order to show a practical understanding of the concepts of the first part, students will be divided into teams and will be asked to complete in a simulation related to marketing strategy (i.e. Markstrat). This simulation will allow students to draw a parallel between marketing strategy and marketing tactics (4Ps). This will also allow them to apply the knowledge about other elements of marketing management that they have been studying in other modules.

Digital technologies allow for the creation and storage of an unprecedented amount of data. The advent of the Internet of Things will further accelerate the growth of digital data, as more and more devices and physical objects will connect to the internet. The ‘digital universe’ is expected to grow from 4.4 trillion gigabytes today to around 44 trillion gigabytes by 2020. This deluge of data presents an immense opportunity for marketing, yet seizing this opportunity requires specific market research skills.This module will introduce students to the rapidly growing field of data science and will familiarise them with its basic principles and general mindset. Students will learn concepts, techniques, and tools that are used to deal with various facets of large data sets. It is essential to develop a deep understanding of the complex ecosystem of tools and platforms, as well as the communication skills necessary to explain advanced analytics. This course will provide an overview of the wide area of data science and the tools available to analyse large amounts of data. The module will also highlight limitations of big data analytics. Specifically, big data analytics assist in improving and developing existing product portfolios, yet their ability to derive insights that may inform the creation of radical innovations and new markets is limited. Potential approaches to address this limitation will be discussed (e.g., combinations with qualitative/netnographic research methods).

In summary, this module aims to provide students with the skills needed to work in data-driven marketing environments.

Over the past three decades, the internet and digital technologies have transformed marketing landscapes beyond recognition. Indeed, they have created an entirely new marketing discipline: digital marketing. This module demonstrates how marketers navigate digital marketing environments successfully: how they implement effective marketing communication strategies, how they create successful digital business models, and how they build strong brands in digital marketing environments.

The module will ensure a solid understanding of fundamental theories on marketing communications and brand management. Based on this theoretical foundation, classroom discussions will be directed at the latest insights from an ever-growing body of research on digital marketing and digital branding. The first part of the module will focus on the idiosyncrasies of digital marketing communications. Students will learn how to develop a digital communication strategy and will be familiarised with relevant digital marketing metrics. Digital communication activities include, but are not limited to, mobile marketing, social media marketing, blogging, email marketing, and search engine optimisation. The lectures will also explain how to combine different social media (i.e., Blogs, Twitter, Facebook, Google+, YouTube, Instagram) in order to achieve strategic marketing objectives. In the second part of the module, the lecturer will examine different online business models including, for instance, internet retail, subscription and curated commerce, two-sided markets, freemium products, and the sharing economy. Finally, the third part of the module will identify effective branding strategies and tactics for digital marketing environments. Specifically, students will learn how digital technology has changed the nature of customer relationships with brands. The module aims to enable students to leverage digital technology for the development of compelling brand identities.

Throughout the module, students will be challenged to identify unintended negative social consequences of the growing digitalisation of consumer worlds, and to understand the dark side of social media. For example, mental health issues of users, the emergence of the “gig economy”, and the proliferation of “fake news” will be discussed. This aims to ensure that students will employ digital technology thoughtfully in their future careers.

This module aims to cover a wide range of applied data analysis techniques using R. With its strong focus on the practical application of the techniques, you'll explore the following topics:

• Data sources
• Data gathering
• Interpretation of statistical results
• Visualisation
• Principal component analysis
• Practical implementation using R
• Decision-making based on data analysis.