- A level requirements: AAB
- UCAS code: N3I4
- Study mode: Full-time
- Length: 4 years
In choosing to study this course, you will receive rigorous training in areas such as econometrics for finance, programming with Python, theoretical finance, and computational methods. You will have the opportunity to study cutting- edge topics such as data mining, machine learning, financial data visualisation, and financial technology. You will be able to combine your knowledge of finance, programming in Python, and analytics to solve finance problems and generate insights.
The BSc Finance and Data Analytics Year in Industry programme blends together the areas of finance and data analytics and equips you with the knowledge, skills, and abilities to follow a range of careers, e.g. finance and consulting.
You will be equipped with the foundational knowledge of finance and introduced to Python, the programming language. Your understanding of advanced finance theories and their implications for the industry will be developed, and you will learn to use Python and industry-leading datasets to empirically test finance theories. You will also learn to use data visualisation techniques to communicate your insights with the audience.
During your final year, you will deepen your knowledge of the finance industry and have the opportunity to take cutting-edge modules such as Data Mining and Machine Learning. You will also develop a range of transferable skills, including flexibility, problem-solving ability, commercial awareness, and communication, which are highly valued by employers.
Studying Finance with Data Analytics at Liverpool will enable you to reach your full potential as you will benefit from our range of industry links and research expertise in addition to being exposed to our cutting-edge curriculum, the state-of-the-art computer room, and innovative teaching methodologies.
Discover what you'll learn, what you'll study, and how you'll be taught and assessed.
In the first year of study, you will acquire the foundational knowledge of accounting, finance, and data analytics. During this year, you will learn about the key concepts and datasets and their implications for decision-making. You will also begin to develop an expertise in the area of data analytics and computational methods by using Python to write computer codes to solve finance problems. In addition to your technical abilities, you will also develop key employability skills that will be important in your future career.
ACFI101 aims to develop a sound understanding of the fundamental principles and techniques of financial accounting.The context and purpose of financial statements is introduced, after which students are introduced to the techniques of recording financial transactions, adjusting financial records and preparing basic financial statements. Successful students will possess a sound base of knowledge for progression towards studying financial reporting in greater depth in the second and final years: the preparation of complex financial statements in conformity with International Financial Reporting Standards (IFRS), both for single entities and groups of companies, and for entities undertaking a wide range of accounting transactions.This module is delivered by means of lectures and tutorials, supported by online self-study question material. The assessment is conducted via two mid-term tests (30%) and a final exam (70%).
The module aims to provide an introduction to quantitative methods that will assist students in establishing basic quantitative and statistical skills for the study of accounting and finance.
This module introduces students to fundamental concepts in finance. The course aims to provide a firm foundation for the students to build on later on in the second and third years of their programmes, by covering basic logical and rational analytical tools that underpin financial decisions. The course covers topics such as the structure of firms and time value of money. Building on these notions, we then discuss the valuation of simple securities such as bonds and equities. The course also introduces students to project appraisal techniques.
This module aims to introduce students to the graduate recruitment process by providing them with the skills required to secure a work placement or summer internship. It also provides the opportunity to acquire, develop and apply these skills.
This module equips students with the tools to successfully analyse data. The module introduces students to programming with Python and highlights the usefulness of programming for data analytics. The module begins with an overview of various data types/structures. It then covers topics such as mathematical and logical operators, flow control, exception handling, functions, data exploration, and data wrangling. The seminar/lab sessions provide students with the opportunity to apply their knowledge by using Python to extract and analyse data. The assessment is conducted via a group project (50%) and an individual project (50%). Overall, the students will develop a range of skills, including communication skills, IT literacy, numeracy skills, and problem-solving skills.
This module equips students with the building blocks of economics that are important for a good understanding of finance. The course’s main aim is to develop the student’s understanding of decision-making in an uncertain world. The course discusses the concepts of risk, risk aversion, expected utility and their implications for portfolio choice. It also covers behavioural theories. The module presents both the theory and the empirical evidence, thus enabling the students to compare theory and practice.
Throughout the module, students will learn about the applications of these economic principles to decision-making when faced with risk and uncertainty. While the lectures introduce the conceptual framework, the seminars are more problem-based. The assessment is conducted via an individual exam (100%) at the end of the course. Overall, the students will develop a range of skills, including problem-solving and communication skills.
This module equips students with both theoretical and practical knowledge of computational methods used in the financial services industry. The module’s topic coverage includes: series expansion, root-finding, optimisation methods, interpolation techniques, quadrature, and simulation methods. The lectures introduce the key concepts while the seminars are more practical. During the seminars, students will apply their knowledge of computational methods through the use of Python. The assessment is conducted via a mid-term test (30%) and an individual project (70%) at the end of the module. Overall, the students will develop a range of skills, including communication, IT literacy, numeracy, and problem-solving skills.
In the second year of study, you will build upon the foundations of the first year to learn about advanced finance theories and evaluate their implications for the business world. You will also develop the ability to not only theoretically evaluate key concepts but also to leverage financial datasets to evaluate their empirical relevance. In so doing, you will improve your analytical skills and learn to use Tableau to prepare insightful visual elements. You will achieve this goal by combining your knowledge of finance theory, programming, and econometrics.
Econometrics for Finance I is an important module that equips students with the knowledge of quantitative techniques and the practical expertise needed to solve finance problems. The course involves the analysis of real-world financial datasets to solve finance problems pertinent to a wide range of financial markets. This course is very relevant to students with a keen interest in the quantitative modelling of financial markets or those interested in pursuing an MSc degree in finance or related areas.
The lectures equip students with the theoretical knowledge necessary to formulate, estimate, and correctly interpret multivariate econometric models. In particular, the students will learn about multiple regression models, multiple hypotheses tests, diagnostic checks, and parameter stability. The seminar sessions, which take place in the computer lab, will provide students with the opportunity to apply their knowledge by using Eviews, a statistical software, to analyse real-world data from leading financial databases and solve finance problems. Overall, the students will develop a range of skills, including communication skills, digital fluency, analytical skills, and problem-solving skills. The assessment is conducted via a mid-term multiple choice test (30%), a written coursework (60%), and an individual pre-recorded video presentation (10%) at the end of the course.
This module will cover the main issues related to financial reporting and its evaluation and analysis that is relevant for a graduate in finance. The module will also allow cover the topics specified under the heading Financial Reporting and Analysis as envisaged in the CBOK of the CFA.
This module will offer students the opportunity to critically reflect on the ethical dimension of finance. The module also critically evaluates the regulatory environment in which financial institutions operate.
This module introduces students to important corporate finance topics. The course will explore several questions including: How do firms choose between different sources of funding: equity and debt financing? Is there an optimal combination of equity and debt financing? How do firms select the projects to invest in? How do investors value a firm? Why do firms get involved in mergers and acquisitions (M&A)?
The course provides a detailed understanding of the key stages of the lifecycle of a firm. It starts by analysing the ways firms raise capital, e.g., initial public offering and bond issuance. It then discusses the various trade-offs associated with equity and debt financing and the optimal capital structure. Next, the course focuses on capital budgeting, namely the decision-making process associated with capital investment. Furthermore, the course sheds light on the valuations of firms and highlights the implications for mergers and acquisitions. Throughout the module, students will be exposed to prominent theories in corporate finance, the empirical evidence, as well as examples from the business world. While the lectures introduce the key concepts, the seminar provide students with the opportunity to discuss case studies and to confront the theories with real data from the finance industry. The assessment is conducted via a practical group project that consists in preparing an M&A pitchbook (40%) and an individual exam (60%). Overall, the students will develop a range of skills, including their commercial awareness, problem-solving, and communication skills.
Database management is increasingly important as digitalisation transforms the financial sector. A thorough understanding of database management systems, data security, and emerging applications for finance is provided. The module equips students with the knowledge and skills relevant to understand the challenges in managing large databases and ensuring data security. During the lectures, students will learn about the key concepts. The seminars are computer-based. In particular, they enable students to gain a practical understanding of database management systems, data security, and their importance for addressing business problems. The assessment is conducted via a group project (50%) and an individual project (50%). Overall, the students will develop a range of skills, including commercial awareness, IT literacy, and problem-solving skills.
This module builds on Econometrics for Finance I (ACFI225) to develop the students’ ability to design and implement the right empirical methodology to test finance theories. The emphasis on this module is on the effective use of programming, quantitative techniques, and the relevant data to answer real-world finance questions. The lectures introduce students to finance topics related to predictability, volatility modelling, event studies, and ethical issues in quantitative modelling. The seminar sessions, which take place in the computer lab, are mostly practical. They will provide students with the opportunity to apply their knowledge by using python, a programming language, to extract, process, and analyse data from industry databases. The course will involve the replication of academic studies to illustrate the applications of the concepts. The assessment is conducted via a group project (40%), an individual report (50%), and a presentation (10%) at the end of the course. Overall, the students will develop a range of skills, including communication, digital fluency, analytical, and problem-solving skills.
This module introduces students to key theories related to financial markets. It aims to shed light on a broad range of questions such as: What does finance posit about portfolio choice? What are the determinants of expected returns? How can one identify mispriced securities? Can investors really outperform the stock market?
The course starts with an analysis of portfolio choice. It then explores issues related to the pricing of securities in financial markets. The students will learn about the predictions of the most prominent theories and how to empirically test these predictions. Finally, the course will discuss the evidence from the “real-world”, thus enabling students to compare theory and practice. The seminar sessions will provide students with the opportunity to apply their knowledge by using real-world data to test financial market theories. Students will also develop their commercial awareness by evaluating the implications of the empirical results for market participants. The assessment is conducted via a mid-term MCQ assessment (30%) and an individual report (70%) at the end of the course. Overall, the students will develop a range of skills, including their commercial awareness, digital fluency, and communication skills.
A picture is worth a thousand words! As datasets become larger and more complex, it is critical to effectively and efficiently communicate their key features through visualisation tools. These visualisation tools must be easily understandable by end-users of varying levels of sophistication and languages. How should we design effective visualisation tools? What are the differences between various visual aids? How can we actually create the visual aids of interest? These are some of the questions that this module explores.
The course introduces the principles and techniques of data visualisation. It starts with a detailed discussion of important visualisation principles. It then introduces the students to Tableau, a data visualisation software that is very popular in the industry. Next, the module focuses on the visualisation of datasets across time and the design of maps. Building on this knowledge, the module focuses on interactive visualisations, motions, and dashboards. The students will learn to design effective visualisation tools. The hands-on seminar sessions, which take place in the computer lab, will provide students with the opportunity to apply their knowledge of Tableau to financial data. The assessment is conducted via a group project (30%), a pre-recorded video presentation (10%), and an individual project (60%) at the end of the course. Overall, the students will develop a range of skills, including their problem-solving , digital fluency, and communication skills.
The third year of your programme is spent on placement. You will complete a year-long graduate-level placement, providing valuable work experience and an opportunity to further enhance the skills and attributes sought after by top employers.
You can apply for UK-based placements with a large organisation or smaller company or even seek a placement overseas. You will be visited at least twice by University staff whilst on placement with support from the Placement Team throughout and complete related assessments in May.
The placement experience is not just about gaining work experience, it is also about developing as a more nuanced and sophisticated practitioner. Throughout the placement you will examine current business theory through the lens that is your personal experience and develop real insight into how theory and practice fit together. The less sophisticated try and force theory to work where it won’t or simply abandon it altogether. This module will help you develop an understanding of the role of theory in your personal practice enabling you to target your effort more effectively.
In the final year, you will have the opportunity to develop your understanding of the key players of the financial sector, their business models, the key asset classes in financial markets and the impact of technology on the finance sector. You will also have the opportunity to further develop your Python programming skills by learning about topics such as data mining and machine learning. Furthermore, you will have the unique opportunity to put your knowledge of finance, Python programming, and data analytics to practice by completing a year-long research project analysing a substantive finance issue. This is a great opportunity to develop and demonstrate key skills desired by graduate employers.
In the last three decades, derivatives have become increasingly important in the world of finance. Futures and options are now traded actively on many exchanges and OTC around the world. Yet most of our undergraduate finance courses, which mostly study underlying assets and institutions, simply do not have enough time for an in-depth discussion of derivatives. This class presents both a practical and theoretical approach to derivatives markets. The course starts with basic definitions and properties of put and call options, and forward and futures contracts. Payoff diagrams are used to illustrate these basic notions. Determinants of derivatives values are discussed. The basic no-arbitrage pricing relationships between different types of derivatives are established.
The module will provide students with an opportunity to undertake an empirical piece of work (in the style of an academic paper) on a finance topic by drawing from their knowledge and skills. This module is strongly based on the University of Liverpool hallmarks of research-connected teaching, active learning and authentic assessment.
The module on Financial Risk Management covers both the internal aspects of financial institutions and the external factors that affect the investment arena and modern financial markets in general. The degree provides an in-depth understanding of the theoretical foundations that underpin modern investment and risk management techniques. The course has a strong practical dimension so you can acquire a sound knowledge of how to apply these techniques to equity and credit markets as well as investment strategies in general.
This module will provide an advanced, albeit non-technical, critical understanding for students with a variety of academic backgrounds of a number of current issues faced by both academics and practitioners in the area of finance for development. These will include, for example, trying to answer the question of whether foreign aid works, the impact of multinational firms upon the economy of host countries, the evolving roles of the IMF and World Bank and whether microcredit really provides a “silver bullet” solution to poverty. The module will also cover the main principles of Islamic Finance. This a very strongly research-led module based on the “flipped classroom” approach, whereby students access learning material before the class and the class is used for discussion. In-class discussions will be based on “real” data and documents used by practitioners involving active learning. Finally, the final module will have a developmental form of assessment in the form of a research-based proposal for a new textbook that will allow students to apply the knowledge they have acquired in the module.
The module will introduce students both to the theoretical arguments and the empirical evidence related to financial crises and defaults. The module will discuss in detail the main aspects of the recent financial crisis that shook the world economy including (a) the economic and financial environment in the years just before the crisis, (b) the causes of the crisis and (c) the action taken by policy-makers to deal with the crisis. The module will proceed by discussing the main aspects of the recent Eurozone debt crisis, as well as GREXIT and BREXIT issues. Noting the increasing role of social media both in becoming a popular open forum for analysing economic issues and reflecting public sentiment minute by minute, the module will draw on recent academic work to explain the impact of #Grexit tweets on the Eurozone bond market over and above the impact of economic fundamentals.
Some of the module content will be applied. In particular, using the econometric software EVIEWS and a number of macroeconomic and financial datasets, case studies will be developed to (a) test the adverse impact of excessive debt on economic growth for the G7 economies and (b) provide an empirical assessment of whether financial assets become mispriced prior, during and after financial crises. Students taking this module will need to have basic knowledge of macroeconomic and statistical theory as well as very basic knowledge of simple regression analysis. Prior knowledge of EVIEWS is not essential. A short introduction to EVIEWS will be offered as part of the module.
Behavioural finance attempts to explain several widely cited market phenomena (including e.g. excess volatility, overreaction, bubbles and crises) by drawing on concepts and principles from psychology and their applications in investors’ behaviour. The module provides a thorough introduction to this area, with theoretical analyses being enhanced through the use of short videos, experiments and quantitative finance both during lectures and seminars. Its assessment relies 100% on a final exam.
The module covers the history and development of corporate governance, and the key principles and systems that underpin corporate governance today. It also provides the opportunity to assess the practical application of corporate governance systems across major international organisations based on UK regulation.
This module introduces students to cutting-edge techniques in the areas of data mining and machine learning. The module covers topics such as: regularisation techniques, classification techniques, deep learning methods, and natural language processing. The lectures expose students to the main concepts. The seminars provide opportunities for students to apply their knowledge to real-world datasets. The assessment is conducted via a group project (50%) and an individual project (50%). Overall, the students will develop a range of skills including IT literacy and problem-solving.
This module focuses on recent developments in financial technology, their applications, and implications for the finance industry. What are the recent technological innovations relevant for finance? What is the impact of these developments on society? What are the potential benefits, challenges, and unintended consequences of these innovations? What can policymakers do to manage these innovations better? These are some of the questions that the module sets out to explore.
By the end of this module, students will have a deep understanding of the impact of financial technology on major areas of finance, e.g. banking and trading. The emphasis of this module is on commercial awareness, with classrooms discussions strongly encouraged during both the lectures and seminars. The assessment is conducted via a group report (40%), an individual report (50%), and an individual poster presentation (10%) at the end of the course. Overall, the students will significantly improve their commercial awareness and develop a range of skills, including their commercial awareness and leadership skills.
This module develops the student’s understanding of the financial industry by focusing on delegated portfolio management and alternative investments. The course is made up of two components. The first focuses on delegated portfolio managers, namely the financial institutions that manage assets on behalf of investors. In particular, we discuss the role of institutions such as mutual funds, asset managers, hedge funds, and private equity. Furthermore, we evaluate the risk profiles, fee structures, and investment strategies of these entities. We also analyse how Environmental, Social, and Governance (ESG) concerns affect the decision-making of delegated portfolio managers. The second component of the module focuses on alternative investments such as commodities and real estate assets. The course will discuss the salient features of these assets, their trading, as well as their historical performances. It will also discuss the impact of ESG considerations on these alternative investments.
By the end of this module, students will have a deep understanding of the delegated portfolio management industry. Furthermore, they will expand their knowledge of the asset menu by learning about alternative investments and comparing them to more conventional assets, e.g. stocks and bonds. The course will involve both lectures and seminars. The assessment is conducted via a mixture of a group coursework (30%) and a final exam (70%) at the end of the course. Overall, the students will significantly improve their commercial awareness and communication skills.
During the lectures, you will be exposed to the key ideas as well as the latest academic research and case studies. You will engage with the subject through a range of activities, including live polls, class activities, and class room discussions. The knowledge from the lecture is reinforced by small-group seminars where you will put your knowledge into practice.
You are normally expected to prepare work in advance for seminars and may be expected to present work or give presentations from time to time. On several modules, the seminars take the form of practical sessions in the McKenzie computer suite, where you will use data from leading financial sources, e.g. Bloomberg, Datastream, and Compustat, to test key finance theories. This practical approach enables you to not only learn about theory but also to be able to empirically evaluate its practical relevance. Furthermore, the module leader will provide guidance in the form of suggested readings based on academic journals or newspapers articles.
The degree features E-learning and blended learning methods. You will be able to pre-learn and prepare for each teaching session by accessing the lecture materials on the virtual learning environment (VLE) ahead of each lecture. Furthermore, you will have access to the recording of individual sessions thus giving you the opportunity to revise the material at your own pace and time. The VLE will also be used to share digital material, e.g. videos, podcasts, and articles, highlighting the recent business events that are connected to the key concepts discussed in-class.
You will be assessed through a combination of individual and group-based activities. The methods of assessment include the written examination, the employability portfolio, the (pre-recorded) video presentation, the multiple choice question, the poster presentation, the referee report, the pitchbook, and the visualisation dashboard. The exact method(s) of assessments and weight(s) will vary from one module to another.
We have a distinctive approach to education, the Liverpool Curriculum Framework, which focuses on research-connected teaching, active learning, and authentic assessment to ensure our students graduate as digitally fluent and confident global citizens.
Studying with us means you can tailor your degree to suit you. Here's what is available on this course.
Day-to-day teaching takes place in the Management School, a world leading centre for management and leadership education and research. You will have access to extensive library facilities, industry-leading databases, such as Bloomberg and Wharton Research Data Services, and a state-of-the-art computer room (McKenzie Suite).
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The curriculum is inspired by the growing demand by employers for graduates with a mixture of finance and technology skills. By undertaking this degree, you will have the opportuity to develop your expertise in finance, programming with Python, as well as cutting-edge topics such as machine learning and data visualisation. Moreover, you will develop a range of transferable skills, including flexibility, problem-solving ability, commercial awareness, and communication, which are highly valued by employers.
We’re committed to enhancing employability and supporting you all the way to your future career. Our programmes offer flexibility, choice and opportunities allowing you to pursue your life aspirations. Our innovative Careers and Employability Service will be on hand throughout your time with us to help you prepare for life after graduation.
Students are also encouraged to undertake a Year in Industry as part of the four-year sandwich degree programme. You will be supported in finding and applying for a placement in an organisation which could range from a local small and medium-sized enterprise to a global blue-chip company – the choice in yours!
Students completing the BSc Finance and Data Analytics programme at the University of Liverpool Management School develop the knowledge, skills and abilities needed to successfully pursue careers in a range of industries such as asset management, banking, financial technology (FinTech) firms, large corporations, consultancy firms, and financial services firms. They will also have the possibility to pursue further degrees, including a taught postgraduate degree (e.g. MSc) or a research degree (e.g. PhD under the ESRC 1+3 route).
At Liverpool, our goal is to support you to build your intellectual, social, and cultural capital so that you graduate as a socially-conscious global citizen who is prepared for future success. We achieve this by:
Your tuition fee covers almost everything, but you may have additional study costs to consider, such as books, specialist equipment or field trips.
|UK fees (applies to Channel Islands, Isle of Man and Republic of Ireland)|
|Full-time place, per year||£9,250|
|Year in industry fee||£1,850|
|Year abroad fee||£1,385|
|Year in industry fee||£1,850|
Tuition fees cover the cost of your teaching and assessment, operating facilities such as libraries, IT equipment, and access to academic and personal support. Learn more about tuition fees, funding and student finance.
All students have the opportunity to spend a semester studying overseas during their second year, subject to your year one performance. If you elect to study abroad you will need to cover associated travel and living costs. University travel bursaries and subsistence grants are available.
Find out more about the additional study costs that may apply to this course.
We offer a range of scholarships and bursaries to help cover tuition fees and help with living expenses while at university.
The qualifications and exam results you'll need to apply for this course.
My qualifications are from: United Kingdom.
|GCSE||GCSE Mathematics at grade 6 required.|
|BTEC Level 3 Diploma||
D*D* in a Business related subject plus A-Level at grade B.
|BTEC Level 3 National Extended Diploma||
D*D*D. BTEC qualifications must be in a Business related subject.
35 with no score less than 4
|Irish Leaving Certificate||H1, H1, H2, H2, H2, H3.|
|Scottish Higher/Advanced Higher||
Scottish Advanced Highers accepted at AAB.
|Welsh Baccalaureate Advanced||Accepted at grade A or B alongside two A Levels only.|
|Access||45 credits at Distinction in graded level 3 units in a relevant Diploma.|
Many countries have a different education system to that of the UK, meaning your qualifications may not meet our entry requirements. Completing your Foundation Certificate, such as that offered by the University of Liverpool International College, means you're guaranteed a place on your chosen course.
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