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Categories within Computer Science

• cs.AI - Artificial Intelligence ( new , recent , current month ) Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
• cs.AR - Hardware Architecture ( new , recent , current month ) Covers systems organization and hardware architecture. Roughly includes material in ACM Subject Classes C.0, C.1, and C.5.
• cs.CC - Computational Complexity ( new , recent , current month ) Covers models of computation, complexity classes, structural complexity, complexity tradeoffs, upper and lower bounds. Roughly includes material in ACM Subject Classes F.1 (computation by abstract devices), F.2.3 (tradeoffs among complexity measures), and F.4.3 (formal languages), although some material in formal languages may be more appropriate for Logic in Computer Science. Some material in F.2.1 and F.2.2, may also be appropriate here, but is more likely to have Data Structures and Algorithms as the primary subject area.
• cs.CE - Computational Engineering, Finance, and Science ( new , recent , current month ) Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
• cs.CG - Computational Geometry ( new , recent , current month ) Roughly includes material in ACM Subject Classes I.3.5 and F.2.2.
• cs.CL - Computation and Language ( new , recent , current month ) Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
• cs.CR - Cryptography and Security ( new , recent , current month ) Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
• cs.CV - Computer Vision and Pattern Recognition ( new , recent , current month ) Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
• cs.CY - Computers and Society ( new , recent , current month ) Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
• cs.DB - Databases ( new , recent , current month ) Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
• cs.DC - Distributed, Parallel, and Cluster Computing ( new , recent , current month ) Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
• cs.DL - Digital Libraries ( new , recent , current month ) Covers all aspects of the digital library design and document and text creation. Note that there will be some overlap with Information Retrieval (which is a separate subject area). Roughly includes material in ACM Subject Classes H.3.5, H.3.6, H.3.7, I.7.
• cs.DM - Discrete Mathematics ( new , recent , current month ) Covers combinatorics, graph theory, applications of probability. Roughly includes material in ACM Subject Classes G.2 and G.3.
• cs.DS - Data Structures and Algorithms ( new , recent , current month ) Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
• cs.ET - Emerging Technologies ( new , recent , current month ) Covers approaches to information processing (computing, communication, sensing) and bio-chemical analysis based on alternatives to silicon CMOS-based technologies, such as nanoscale electronic, photonic, spin-based, superconducting, mechanical, bio-chemical and quantum technologies (this list is not exclusive). Topics of interest include (1) building blocks for emerging technologies, their scalability and adoption in larger systems, including integration with traditional technologies, (2) modeling, design and optimization of novel devices and systems, (3) models of computation, algorithm design and programming for emerging technologies.
• cs.FL - Formal Languages and Automata Theory ( new , recent , current month ) Covers automata theory, formal language theory, grammars, and combinatorics on words. This roughly corresponds to ACM Subject Classes F.1.1, and F.4.3. Papers dealing with computational complexity should go to cs.CC; papers dealing with logic should go to cs.LO.
• cs.GL - General Literature ( new , recent , current month ) Covers introductory material, survey material, predictions of future trends, biographies, and miscellaneous computer-science related material. Roughly includes all of ACM Subject Class A, except it does not include conference proceedings (which will be listed in the appropriate subject area).
• cs.GR - Graphics ( new , recent , current month ) Covers all aspects of computer graphics. Roughly includes material in all of ACM Subject Class I.3, except that I.3.5 is is likely to have Computational Geometry as the primary subject area.
• cs.GT - Computer Science and Game Theory ( new , recent , current month ) Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
• cs.HC - Human-Computer Interaction ( new , recent , current month ) Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
• cs.IR - Information Retrieval ( new , recent , current month ) Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
• cs.IT - Information Theory ( new , recent , current month ) Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
• cs.LG - Machine Learning ( new , recent , current month ) Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
• cs.LO - Logic in Computer Science ( new , recent , current month ) Covers all aspects of logic in computer science, including finite model theory, logics of programs, modal logic, and program verification. Programming language semantics should have Programming Languages as the primary subject area. Roughly includes material in ACM Subject Classes D.2.4, F.3.1, F.4.0, F.4.1, and F.4.2; some material in F.4.3 (formal languages) may also be appropriate here, although Computational Complexity is typically the more appropriate subject area.
• cs.MA - Multiagent Systems ( new , recent , current month ) Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
• cs.MM - Multimedia ( new , recent , current month ) Roughly includes material in ACM Subject Class H.5.1.
• cs.MS - Mathematical Software ( new , recent , current month ) Roughly includes material in ACM Subject Class G.4.
• cs.NA - Numerical Analysis ( new , recent , current month ) cs.NA is an alias for math.NA. Roughly includes material in ACM Subject Class G.1.
• cs.NE - Neural and Evolutionary Computing ( new , recent , current month ) Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
• cs.NI - Networking and Internet Architecture ( new , recent , current month ) Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
• cs.OH - Other Computer Science ( new , recent , current month ) This is the classification to use for documents that do not fit anywhere else.
• cs.OS - Operating Systems ( new , recent , current month ) Roughly includes material in ACM Subject Classes D.4.1, D.4.2., D.4.3, D.4.4, D.4.5, D.4.7, and D.4.9.
• cs.PF - Performance ( new , recent , current month ) Covers performance measurement and evaluation, queueing, and simulation. Roughly includes material in ACM Subject Classes D.4.8 and K.6.2.
• cs.PL - Programming Languages ( new , recent , current month ) Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
• cs.RO - Robotics ( new , recent , current month ) Roughly includes material in ACM Subject Class I.2.9.
• cs.SC - Symbolic Computation ( new , recent , current month ) Roughly includes material in ACM Subject Class I.1.
• cs.SD - Sound ( new , recent , current month ) Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
• cs.SE - Software Engineering ( new , recent , current month ) Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
• cs.SI - Social and Information Networks ( new , recent , current month ) Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
• cs.SY - Systems and Control ( new , recent , current month ) cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.

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How to efficiently read the theoretical computer science research papers?

I am a computer science graduate student works in theory. The general advice is to go through the paper and then in the second or third round try to understand the detail. Read the abstract, introduction and then main results(just the results). I go through research paper multiple times, each time keep record of confused things. Many time I have to spend more than a week to understand even the bigger picture. Once I have bigger picture, then it is easy for me to understand the paper quickly.

Reading is taking quite long time. I am even about to graduate but it takes me couple of weeks to read a single paper. I have to many times present research papers in front of senior researchers. So I prepare, but as I mentioned that reading is taking too much time.

Is there any better way to speedup the reading? Efficiently means getting the overall idea with less number of passes.

Question : How to efficiently read the theoretical computer science research papers ?

• soft-question
• 2 $\begingroup$ I may not be of good advice since I am also a slow reader but I found it useful to skip the intro first (or to only skim it lightly just to have a rough idea of the contributions) so that you can directly go to the "meat" of the paper. Also before I was reading the definitions to really get a good understanding of what the authors are using. Now I read the statements of the theorems and look the needed definitions. You save time because out of context definitions may be hard to parse. And maybe not needed if they are only needed in a proof that you do not really care about. $\endgroup$ –  holf Commented Oct 21, 2019 at 17:04

3 Answers 3

How long it takes one to read a paper typically depends on what one wants to get out of it. I'll outline three levels of granularity.

Coarsest level: Relevant or not? When building up a list of potentially relevant literature, we often start with a handful of important papers, and then play the game -- who cites them? Whom do they cite? Iterating this game naively would cause an exponential explosion, so we prune the search tree. Briefly scan each candidate and classify it as relevant to my topic or not. This shouldn't take more than a few minutes.

User level: I've decided the paper is relevant, but does it contain a particular result that I need? Say, some variant of Chernoff bounds for non-iid random variables? Unbounded ones? This can take a bit longer, since not all results are stated in the form I'm looking for -- but shouldn't take more than an hour.

Expert level: The paper is relevant and has useful results; now I want to get an in-depth understanding and mastery of the technique. After all, this is the area I want to innovate in! This can take arbitrarily long, since it may require going down various rabbit holes of carefully reading previous results the present one relies on, and so on. No time limit here -- decide how badly you need this and settle in for the long haul.

Update, by popular demand. OK, the paper in question falls into category (3) and you're willing to devote serious time and effort into it. My first suggestion is to read the paper at the User Level, as specified in (2) above. Try first to understand what the main results are claiming , not how to prove them. Once you understand the claims, try to flesh out the proof outline. What basic strategy is being used? Is it a construction, an existence proof, etc -- try to "decompose" it into the various techniques you know. If it's an important paper, you might discover that it invents novel techniques -- make good note of these!

Sometimes short and seemingly elementary proofs are notoriously difficult to understand in an intuitive sense -- one such paper, I believe, is Haussler's "Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension". It's easy enough to follow the proofs step by step, but most beginner readers will still not be able to say much about the "big picture", even after the 2nd or 3rd reading. In such a case, it might be useful to seek out an expert (perhaps the author himself) to try to understand how he came up with the proof.

• $\begingroup$ Thanks for the answer. I am interested in third one. The one point I have to disagree is "no time limit". There should be some time limit, the purpose of my reading to do some research, to come up with new results. $\endgroup$ –  user55044 Commented Oct 23, 2019 at 5:13
• 1 $\begingroup$ @ Aryeh I don't know, how much badly I want, but let's say I want to perform research on particular research paper then is there any time limit? $\endgroup$ –  user55044 Commented Oct 24, 2019 at 9:43
• 3 $\begingroup$ Obviously, I can't set one for you. Some papers can take a year or more to understand. Does your adviser/funding situation allow that? Do you have the patience and stamina? These are all personal, individual choices. $\endgroup$ –  Aryeh Commented Oct 24, 2019 at 10:33
• 1 $\begingroup$ and what about efficient reading of papers? $\endgroup$ –  user55044 Commented Oct 26, 2019 at 15:05
• 1 $\begingroup$ I added an update, though it's very difficult to give general advice on a question where case-specific pointers will be most useful. $\endgroup$ –  Aryeh Commented Oct 26, 2019 at 18:45

My personal experience is that complicated proofs are usually discovered gradually: Someone first came up with a relatively simple proof of some result. Then, someone took that simple proof and added another idea to prove a stronger result. Next, someone added another idea or two to prove an even stronger (or different result), etc. Eventually, we end up with a very complicated proof of a very general and strong result.

Now, if someone tries to read the very complicated proof that was obtained at the end of that process, they will have a very difficult time: they are basically trying to digest the culmination of many years of research in one shot.

My personal strategy in such situations is to read the papers in chronological order. I try to read the paper that came first in this line of research - usually, that paper would be rather simple. Then, I try to understand the next paper, etc. Each time I read a paper in the line, I try to understand what was the new idea that the paper added. By the time I reach the last papers in the chain, what previously seemed to be a very complicated proof that I can barely understand turns out to be "what I already read + a simple idea or two".

This strategy can be very time consuming at first. However, within a single area of research, there are relatively few such "lines of research". Thus, after you implement this strategy a few times, you cover most of those lines. By that time, you will be able to read most of the papers in the area rather easily, since you will usually be familiar with the ideas they build on.

More generally, the reason that experienced researchers can read papers very quickly is that they usually already have good understanding of the area to which the paper belongs. When you have such understanding of the area, reading a new paper usually boils down to answering the question "what is the new simple idea that the paper adds to the area".

So, to sum up, if you want to be able to read papers quickly, first invest some time in gaining mastery of your research area. Once this is done, reading more papers should be rather easy.

• 1 $\begingroup$ But sometimes the early arguments were unnecessarily complicated and convoluted, and later insight yielded crisper, more streamlined proofs. If, for example, a proof appears in a textbook, it's usually much better to read that than go to the early literature. $\endgroup$ –  Aryeh Commented Jul 13, 2021 at 13:04
• $\begingroup$ I completely agree. My answer does not work for all cases, and each paper should be considered individually. Still, I think my answer is a good start. $\endgroup$ –  Or Meir Commented Jul 14, 2021 at 12:09

I agree with @holf about skipping parts of the paper. Which parts you skip is a different matter. If I know nothing in the field, I skip the intro, related work, and conclusion. If I know a bit in the field, do the opposite: I skip the meat and read the intro and conclusion only.

In general, however, I would go even further: don't read the papers in the first place at all. Instead, first try to talk to the authors (if possible) and only then read the papers, if needed. My experience has been that the quality of an average paper is so low that a noninformed person is unlikely to understand what is going on. If you go for reading first, you weep and suffer exactly the way you described it and may drown in typos.

• $\begingroup$ My experience is different. When I read a paper in a good journal, it is usually well-written and contains only few typos. Also, I think it is better to read the paper before contacting the author - it shows a much more serious attitude. $\endgroup$ –  Erel Segal-Halevi Commented Sep 12, 2020 at 23:19
• $\begingroup$ @ErelSegal-Halevi, I think that depends on the field, e.g. read the graph minor series, all of them are in a very good journal, almost no typo, not even with logical mistakes, but even authors admit that they are not well written. If we talk about conference papers, half of them are catastrophic (goodness of arxiv is that encourages people to write cleaner). But agreed, one should have a basic understanding of the paper before contacting the authors. $\endgroup$ –  Saeed Commented Jul 14, 2021 at 21:10
• $\begingroup$ @ErelSegal-Halevi I disagree. Most authors, especially if the paper is older, would not answer your questions (say, because their e-mail address is outdated, or because they are no longer in the field, or because they consider you unimportant, …). So writing the authors would be a loss of time. In fact, not only e-mails, even papers are no longer being read on average. They are being written. $\endgroup$ –  MdAyq Commented Jul 11, 2023 at 19:08
• $\begingroup$ @Saeed That's what the authors want you to do (have a basic understanding…) so that you will cite them. After having read a whole bookshelf of papers, each at least once, I no longer feel the need to be that polite. $\endgroup$ –  MdAyq Commented Jul 11, 2023 at 19:10

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Don’t let finances stand in your way: £1,200 Scholarships available now.

BSc Computer Science

Push the boundaries of what's possible with our BSc Computer Science degree. Master the skills to shape the future of technology and lead the digital revolution.

Any day you choose.

4 or 6 years.

Tuition fees

£20,805 in total.

Entry requirements

2 subjects at A level.

Online, distance-learning.

In today's rapidly growing digital landscape *, pursuing a BSc Computer Science degree means immersing yourself in an exciting and highly competitive field. As industries evolve to embrace AI and digital transformation becomes pivotal, opportunities are diverse and in demand, with potential career paths in areas such as software engineering, design, space, future materials, and AI .

Our programme covers an extensive range of topics from mathematics and computer architecture to programming with Python, web application development, database modelling and more. Experience a flexible virtual learning environment with around-the-clock access to study materials and dedicated support from personal tutors, ensuring that you're well-prepared to excel academically and professionally.

*Royal Academy Engineering News (2023) 

Entry requirements for BSc Computer Science

Every application is different. If you’re unsure whether you meet the entry requirements or have any questions, speak to a Study Advisor today. 

Typical offer

Previous undergraduate study: 2 subjects at A level or equivalent and GCSE Maths and English, Grade C or Grade 4 or above.

Other qualifications: we also consider a variety of qualifications, including A levels with EPQ, International Baccalaureate, BTECs, T-levels, Cambridge Pre-U subjects, Irish Leaving Certificate, Scottish Highers, Welsh Baccalaureate and other international qualifications. Overseas qualifications may be accepted and subject to evidence of equivalency typically verified through ECCTIS (UK ENIC).

Contextual offers: we strive to support you in succeeding, whatever your background, and we encourage you to apply to study with us even if you don’t meet the academic requirements. We view all applications holistically and will consider your CV, work experience, background, and other experience when assessing your ability to succeed in your chosen undergraduate degree courses or programme.

Recognition of prior learning: are you thinking of transferring institutions, or have you studied before? Our recognition of prior learning process unlocks the value of your past learning and experience.

Mature students: you may be eligible to enrol through our mature student process, which requires you to submit a CV and a short supporting statement of 500 to 600 words. This should explain why you want to further study in this programme and how it will support your career plans.

English language requirements

If you’re a non-native English speaker, you’ll be asked to provide proof of your English language proficiency in one of the following forms:

IELTS: level 6.0 or above with no element below 5.5 (an online English test is offered (SPEEXX) if IELTS is unavailable).

TOEFL iBT®: a minimum score of 80 overall.

Trinity College London Integrated Skills in English (ISE): ISE III at minimum pass (please note that Trinity College ISE academic results are only valid for two years) .

Duolingo English test: minimum 95 points.

Cambridge Certificate: minimum B grade overall.

Alternatively, you may be accepted if you have previously studied English at an appropriate level and attended a recognised institution.

Fees and funding for BSc Computer Science

For UK students , the total fees will be the same – whether you choose to study full or part-time – and there are no fees for the application process. To explore all fees and funding options for your BSc Computer Science degree, check out our UK fees and funding page or international fees and funding page .

The prices shown below do not include any scholarships.

Student loans

You may be eligible for a government student loan .

£1,200 LIBF Scholarship

As an LIBF student, you’ll have the opportunity to apply for an LIBF Scholarship . The LIBF Scholarship is an award of £1,200 towards your tuition fees, spread over the duration of your course and reducing your total fees. You are eligible to apply if your household income is less than £3,500 per month.

Undergraduate Hardship Fund

If you experience financial difficulty as an undergraduate student, you can apply for support from our Hardship Fund . Hardship funds are made available to UK students twice in the academic year. You’ll be informed when applications are open, and the Scholarship, Bursaries, and Prizes Committee will allocate funds.

Fee reductions for self-funding students

You can get 2% off your total fees if you pay annually in advance or 10% off if you pay upfront in full for an undergraduate course.

Skills you'll master with this Computer Science BSc

Elevate your future with our BSc Computer Science course, meticulously designed to sharpen your expertise and distinguish you in the jobs market. You'll develop a unique blend of technical, analytical, and transferable skills, ensuring a well-rounded and robust knowledge base.

Technical skills

1. Programming with Python : master an essential programming language for modern software development.

2. Database systems : gain expertise in handling vast data through database modelling and management.

3. Web development : develop dynamic, responsive web applications centred around user experience.

Analytical skills

1. Advanced mathematics: apply mathematical principles to solve complex computer science problems. 

2. Algorithms and data structures: understand and implement the blocks of efficient programming.

3. Software engineering: learn and apply methodologies for large-scale software development and management.

Soft skills

1. Critical thinking: evaluate complex problems to develop innovative, practical solutions.

2. Communication skills : present complex technical information clearly to diverse audiences.

3. Team collaboration : work efficiently within multidisciplinary teams on projects and assignments.

Why study BSc Computer Science at LIBF

Choosing our Computer Science undergraduate degree at LIBF places you on a path to becoming a leading tech expert. Our programme, designed by industry experts, gives you both foundational and advanced key skills in computer science. Enjoy access to outstanding resources and insights, and learn from experienced professionals to ensure you are well-prepared for a successful career.

Learn from industry experts

Gain cutting-edge insights from courses crafted and delivered by industry leaders. Apply real-world knowledge to drive impactful results from day one.

Secure your dream career

Stand out in the job market with our future-focused curriculum and comprehensive support system. Graduate ready to conquer top roles and lead with confidence.

Experience next-level flexibility

Enjoy unparalleled freedom to learn on your schedule. Study anytime, anywhere, full or part-time, and take your exams 24/7, 365 days a year.

Get over 140 years of expertise

Benefit from our rich legacy of over 140 years in business, management, and tech education. Trust in our degrees, forged in London, Silver-Rated by TEF 2023.

Studying with LIBF has been amazing. The website and course modules are easy to navigate, and I feel fully supported. The flexibility makes it easy for me to combine studies and work.

Muhammed Saif U

BSc (Hons) Computer Science

How you'll study as a BSc Computer Science student at LIBF

Course delivery and resources

Our online courses offer flexible and engaging teaching methods designed to inspire and empower you:

Expert-led learning: gain unparalleled insights from courses crafted and delivered by industry trailblazers and leading professionals.

Online learning platform: navigate a user-friendly, immersive platform that provides 24/7 access to interactive and engaging study materials.

Live interaction: connect with faculty and other students through dynamic live Q&A sessions and workshops that bring learning to life.

Rich learning resources: access a wealth of resources, including a vast library of ebooks, academic journals, videos, podcasts, and cutting-edge multimedia content.

Complimentary iPad: if you’re based in the UK, we’ll give you an iPad that contains everything you need.

Study support system

Our robust support system empowers you with the guidance and resources you need to excel:

Personal tutors: experience tailored, one-on-one academic guidance from dedicated experts committed to your success.

Study coaches: master study strategies, time management, and tackle academic challenges with personalised support.

Group career coaching: boost your employability and career readiness through dynamic, skills-focused sessions.

AI study assistant, Syntea*: benefit from 24/7, round-the-clock support for unlocking deep knowledge of your subject, and getting faculty-validated answers to your questions*.

*Syntea is available for all modules with a course book.

BSc Computer Science programme structure

Our bachelor's degree in Computer Science offers a mix of theoretical knowledge and practical skills, setting you up for success in the digital world. You'll study mandatory core computer science modules and elective optional modules that allow you to customise your learning according to your career aspirations. Each module is designed to give you specialised skills and insights into real-world applications, preparing you for diverse opportunities in the tech industry.

Core modules

The core modules lay the foundation of our BSc Computer Science programme. You'll cover essential topics such as Introduction to Computer Science , Mathematics , and Computer Networks and Distributed Systems . These modules aim to give you a robust understanding of computer science principles and practical skills.

Introduction to computer science

Module overview:

This module introduces you to the basics of computer science, including algorithms, data structures, propositional logic, and the fundamentals of computer science hardware systems. You'll also explore the architecture of the internet and professional conduct in computing.

Gained skills:

You'll acquire skills in information representation, basic algorithms, and data structures. You'll also become proficient in propositional logic, Boolean algebra, and understanding computing infrastructures.

Real-world application:

Use your knowledge to understand and build efficient computing systems. For example, algorithms can be applied to optimise database queries, and propositional logic can create more effective conditional statements in programming.

Assessment:

You'll take a 90-minute exam with closed and open questions.

Mathematics I

Strengthen your foundational understanding of differentiation, integration, and vector calculus, essential for developing advanced algorithms. Dive into tensors and matrix equations to support data-intensive computations.

Apply rigorous mathematical techniques to analyse data sets and build predictive models. Learn how to deal with complex mathematical challenges in data and computer science projects.

Use advanced mathematical skills to develop and refine algorithms for data-processing tasks in finance, such as risk assessment models, or optimise algorithms for engineering simulations. Apply these techniques to enhance computational models for large-scale analysis projects.

You'll take a 90-minute exam consisting of both closed (e.g., multiple choice) and open questions.

Collaborative work

Module overview :

Focus on self-directed and collaborative learning, covering professional networking, communication, and conflict resolution methods.

Gained skills :

You'll enhance your abilities in professional networking and diverse forms of communication. You'll also learn to handle conflicts effectively while recognising the value of social diversity.

Real-world application :

Apply these skills in team environments, which are essential for roles such as project management and collaborative software development. For instance, effectively manage a team developing a new software application by incorporating diverse perspectives and mitigating conflicts.

Assessment :

You'll complete an oral assignment and a reflection paper, each contributing 50% to your final mark.

Computer architecture and operating systems 

Dive into the basic concepts of computer architecture and different types of hardware and operating systems.

You'll be able to describe the functions of operating systems and simple assembly programs while comparing various types of hardware.

Use this knowledge to optimise computer systems for specific applications, such as enhancing server performance in a data centre or developing more efficient operating systems for embedded computing devices.

Database modelling and database systems

Learn about relational data models, SQL, and NoSQL databases. You'll explore data schemas, data queries, and data storage.

You'll become proficient in designing, creating, and modifying SQL queries and data schemas and have a firm grasp of NoSQL concepts.

Apply these skills to design efficient company database systems, enabling quick and easy data retrieval and processing. For example, develop a customer relationship management (CRM) system that efficiently stores and retrieves client data using SQL databases.

Requirements engineering

Understand enterprise modelling and techniques for determining IT system requirements. You'll also cover documentation, testing, and managing IT system requirements.

You'll acquire skills in using and evaluating different techniques for documenting and managing IT system requirements.

Ensure that IT systems effectively meet business needs, a fundamental responsibility for roles in business analysis and IT project management. For instance, develop a comprehensive set of requirements for a new software application that aligns with user needs and business goals.

Computer networks and distributed systems

Explore network architectures, protocols, security, and the principles of distributed systems.

You'll learn to design and implement network protocols, analyse network problems, and understand distributed systems.

Develop secure and efficient network solutions, which are crucial for network administration and cybersecurity roles. For example, a multinational company can create a secure VPN protocol to ensure safe remote access for employees.

Introduction to programming with Python

Learn Python programming, including data types, structures, control flow, functions, and modules.

You'll develop abilities in writing, debugging, and testing Python programs. You'll also learn to manipulate and analyse data using Python.

Use Python to develop software solutions and automate tasks. This skill applies to various tech roles, such as data analysis and web development. For example, use Python scripts to create a data pipeline to streamline a company's data processing workflow.

Introduction to academic work 

Gain insights into academic research methodologies, structured academic writing, and the evaluating academic sources.

You'll learn to conduct academic research, write structured papers, present findings effectively, and critically evaluate sources.

Apply these skills in research and development roles, providing solid academic underpinning for projects. For instance, use research methodologies learnt to prepare comprehensive reports on emerging technologies for your company.

You'll complete an advanced workbook consisting of 6 assignments, each 150-300 words.

Algorithms, data structures, and programming languages

Explore the principles of designing and analysing algorithms, implementing data structures, and comparing programming languages.

You'll gain the ability to design efficient algorithms, implement data structures, and choose suitable programming languages for different tasks.

Utilise your skill set to solve computational problems effectively, like optimising sorting algorithms in a software application to improve performance.

Theoretical computer science and mathematical logic

Understand the theoretical foundations of computer science and mathematical logic, and analyse computational complexity.

You'll apply mathematical logic to solve computational problems and explore formal languages and automata theory.

Use your technical skills for sophisticated problem-solving in cryptography, where algorithms must comply with computational limits.

Web application development

Learn how to design and develop web applications using HTML, CSS, JavaScript, and web development frameworks.

You'll be skilled in creating responsive and secure web applications using modern web development techniques.

Develop dynamic web applications for businesses, such as an e-commerce platform with secure payment processing.

Advanced research methods

Develop your research skills with a focus on methodologies, case studies, and data collection strategies critical for data science research. Learn to conduct high-quality evaluation research and craft informed research proposals.

Enhance your capacity to design, collect, analyse, and interpret qualitative and quantitative data. Improve your skill in evaluating research quality and applying appropriate scientific methods to complex data problems.

Conduct insightful and actionable research on data to improve business performance and strategy. For example, advanced research methods can be used to guide data-driven product development, or empirical methods can be applied to assess organisational data strategies.

You'll complete a 3,000-word written assignment.

Project: build a data mart in SQL

Apply theoretical knowledge about database methods by designing and implementing a working data-mart solution.

You'll learn to design, architect, and implement a data-mart solution while critically evaluating design choices.

Use your skills to develop data marts that support business intelligence systems, helping companies to make data-driven decisions. 

Project: software engineering

Work on a practical scenario of industrial software development, understanding risks and using strategies to minimise them.

You'll develop professional skills in complex software projects and learn to mitigate risks in large software initiatives.

Effectively manage large software projects, such as developing a new enterprise resource planning (ERP) system, ensuring on-time delivery within budget.

Computer science and society

Analyse the impact of computer science on society, focusing on ethical considerations and the role of technology in the information society.

You'll gain insights into technological advancements' social and ethical implications.

These ethical considerations can be applied in tech roles, such as developing guidelines for ethical AI systems to ensure they're unbiased and fair.

Current topics in computer science

Deepen your understanding of a specific computer science topic through rigorous academic research and discussion with computer scientists.

You'll enhance your research, analytical, and scientific writing skills, focusing on a specialised computer science topic.

Use these skills to stay updated on emerging technologies, providing valuable insights in roles like technology consultancy.

You'll complete a research seminar paper on a selected topic within computer science.

Bachelor's thesis

Conduct independent research in a specific area of computer science, presenting and defending your findings in a final-year thesis.

You'll gain practical skills and develop your ability to conduct independent research and present findings coherently.

Showcase your expertise through a significant third-year research project, such as developing and presenting an innovative data analysis method for a scientific conference.

You'll complete a 7,500-10,000 word bachelor thesis.

Elective modules

Personalise your studies. Elective modules give you the flexibility to delve into specialised topics within computer science, like Artificial Intelligence, Cyber Security, and Business Intelligence. These optional modules align with your career goals and provide in-depth knowledge and practical skills.

You can choose to study two modules from Elective A in your first year. In your second year, you'll select two modules from Elective B and two from Elective C in your final year.

Change management

Dive into the principles and frameworks for managing organisational change effectively. Focusing on various models and their applications, you'll learn to evaluate organisational needs, implement change management theories, and handle resistance to change.

Agile project management

Master Agile methodologies focusing on roles, activities, and artefacts associated with practices such as Scrum. You'll learn to manage complex projects using Agile principles, adapt project strategies to evolving complexities, and uphold ethical standards in decision-making.

Introduction to process management

Understand business process management basics, covering enterprise modelling, process evaluation, and the challenges associated with process changes. You will learn to organise and evaluate business processes, utilise reference models, and effectively manage process changes.

Object-oriented and functional programming with Python

Explore advanced programming concepts using Python, focusing on object-oriented and functional programming paradigms. You will master Python for software development, applying object-oriented and functional programming techniques to build robust and maintainable systems.

Data science software engineering

Learn software engineering principles tailored for data science, including project management, testing, and model deployment. You will gain expertise in managing data science projects from development to deployment using Agile and software testing strategies.

Self-arranged internship I (*)

Gain practical experience through a self-arranged internship, applying theoretical knowledge in a business setting. Develop practical communication, teamwork, and networking skills while understanding organisational structures and processes.

Self-arranged internship II (*)

Build on your practical experience with a second self-arranged internship, focusing on deepening your understanding of a specific industry or role. Refine your professional skills and gain deeper insights into industry practices, enhancing your readiness for post-graduation employment.

*You'll be responsible for arranging a suitable internship in your field. Once placed, please complete an application to the internship module via myCampus to ensure that your internship can be recognised and credited towards your course requirements.

Software Engineering

Techniques for agile software development Explore the principles of Agile, focusing on testing, delivery, and deployment. Understand methodologies like SCRUM and their impact on project teams.

Project: agile software engineering Put your advanced data analysis skills into practice with a comprehensive project. Create a professional report that mirrors real-world data science work environments.

IT operations and project management

IT service management Delve into IT services that ensure smooth system operations, with a focus on ITIL practices.

Project: IT service management Apply your knowledge to analyse and solve IT service management challenges in a project. Offer practical solutions tailored to specific organisational needs.

Cyber security

Data protection and cyber security Learn key IT security concepts, applications, and standard techniques..

Cryptography Understand cryptographic methods, including hash functions and encryption techniques, to secure data.

Big data and business intelligence

Big data and business intelligence Discover the challenges and technologies associated with large-scale data, including storage formats and infrastructure demands.

Cloud computing Gain insights into cloud computing, including its technologies, analytics capabilities, and the latest advancements.

Artificial intelligence

Artificial intelligence Study the evolution of AI, from its history to modern systems, and explore its successes and future developments.

Project: AI Design and develop an AI system, applying your knowledge to meet specific requirements and constraints.

Seminar: software engineering Enhance your research skills by investigating a current software engineering topic. Identify key points, draw connections, and present your findings in a comprehensive written paper.

Project: software engineering Apply your software development expertise to solve a real-world problem. Create a mobile or web app, microservices, or embedded software, using your creativity to deliver impactful solutions.

IT project management Learn core concepts of IT project management, including project organisation, cost management, and problem-solving within IT projects.

IT architecture management Develop future IT blueprints, exploring service strategies and technologies for IT service providers.

Technical and operational IT security concepts Explore key IT security issues like confidentiality, integrity, and availability. Develop skills in network evaluation, protection profile creation, and Big Data interpretation.

Project: configuration and application of SIEM systems: Implement a Security Incident Event Management tool in an enterprise environment, converting technical data into actionable insights.

Business intelligence (BI) Learn about data provision, information generation, and analysis. Build skills in data warehousing and optimise business processes using BI techniques.

Project: business intelligence Apply your BI knowledge to design and prototype BI applications, tailored to specific business needs.

Self-driving vehicles Investigate the safety standards and IT security of autonomous vehicles. Explore areas like sensor fusion, feature detection, localisation, and motion planning.

Seminar: current topics and trends in self-driving technology Delve into the latest advancements in autonomous vehicles, exploring technical, philosophical, and societal impacts. Present your findings in a research essay.

Achieve your career aspirations with a BSc in Computer Science

The demand for computer science professionals is surging. According to Forbes 2024 Tech Industry Statistics , 377,500 new computer and IT positions will open annually between 2022 and 2032. This growth is fueled by the widespread adoption of big data, artificial intelligence, and cloud computing, with over 75% of companies planning to integrate these technologies within the next 5 years ( World Economic Forum's Future of Jobs Report 2023 ).

As a graduate of BSc Computer Science, you're poised to step into a world filled with dynamic career opportunities.

Career roles and salary expectations

Computer science professionals enjoy attractive salaries, reflecting the high skill demand.

IT project manager – £44,842 : manage and maintain applications and systems software, driving innovations across various sectors.

Data scientist – £54,240 : analyse and interpret complex data to aid decision-making and drive business intelligence.

Senior software developer – £66,709 : design computer systems and maintain applications and systems software, driving innovation with programming languages.

Cloud architect – £71,217 : craft scalable, secure cloud solutions that optimise performance, cost, and reliability across diverse environments.

Cyber security architect – £82,033 : protect computer systems from cyber threats through proactive design measures and monitoring.

*Salary information from uk.indeed.com and correct as of August 2024.

Industry sectors

Technology: roles in software development, tech startups, and IT services, driving innovation and digital transformation.

Finance: positions in fintech, risk management, and financial software development, shaping the future of financial services.

Healthcare: careers in health informatics, medical software solutions, and telemedicine, improving patient care through technology.

Retail: opportunities in e-commerce, data analytics, and online services, enhancing the consumer shopping experience.

Consulting: work in business strategy, technology consulting, and process optimisation, helping businesses achieve their goals.

Get your online degree digital prospectus

You’ll find everything you need to know about studying an online degree with us in our digital prospectus. To receive your personalised prospectus, please fill out the form below with a valid email address.

Once you've submitted the form, keep an eye on your inbox for your prospectus to arrive via email.

FAQs about studying for a bachelor's in Computer Science

What is a bachelor's degree in computer science.

A BSc degree in Computer Science focuses on the theoretical foundations of information and computation. It covers key areas such as programming, algorithms, data structures, database systems, and web development.

What can I do with a BSc in Computer Science?

You can explore numerous career prospects in diverse and dynamic fields with a BSc in Computer Science. Graduates commonly move into roles such as software developer, IT project manager, cyber security analyst, systems administrator, and data analyst.

This degree also serves as a solid foundation for advancing to further study, with degree programmes including LIBF's MSc programmes in Artificial Intelligence, Data Science, and Cyber Security.

Is Computer Science a BA or BSc?

Computer science is typically offered as a higher-education BSc (Bachelor of Science) degree. A Computer Science BSc provides rigorous scientific and technical training in computing principles and practices.

Is BSc Computer Science degree valuable?

Yes, a bachelor's degree in Computer Science is highly valuable. It offers you in-demand skills needed to thrive in various tech-driven industries. The comprehensive knowledge and practical experience gained from courses such as data modelling, programming, and web development ensure that graduates are well-prepared to meet industry demands.

What jobs can you get with a BSc Computer Science?

With a BSc Computer Science degree, your possibilities are vast and exciting. You could build innovative computer graphics as a developer, lead tech initiatives as an IT project manager, or protect vital information as a cyber security analyst. Dive into data as a data scientist or create intelligent systems as an AI specialist. Today's computer scientists span multiple industries and offer diverse, rewarding career paths fuelled by the fast-paced world of tech innovation.

How many subjects are in the BSc Computer Science?

The Computer Science BSc comprises numerous subjects across different modules, including compulsory and elective topics. Over 3 years, you will study various areas, such as computer architecture, databases, algorithms and web development, ensuring well-rounded learning outcomes.

Unlock exclusive advice on how to achieve your future goals with LIBF, and discover how our degrees are designed to make education accessible no matter your lifestyle or commitments.

Contact us about our online degrees

If you have any queries about how to apply for our online degrees, or anything else, please contact our Study Advisors.

Our office is open Monday to Friday from 8.00am to 6.00pm UK time.

Get inspired: study guides and career paths in Computer Science

Computer science degree: study guide

How to become a software developer: a career roadmap

What is an IT consultant and how to become one

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JSmol Viewer

Model for designing gamified experiences mediated by a virtual teaching and learning environment.

1. Introduction

2. background and related work in evea, 3. methodology, 4. mgtp model, 5. expert judgment as a first validation of the model, 5.1. application of expert judgment, 5.2. results and discussion, 6. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Vera-Mora, G.; Sanz, C.V.; Coma-Roselló, T.; Baldassarri, S. Model for Designing Gamified Experiences Mediated by a Virtual Teaching and Learning Environment. Educ. Sci. 2024 , 14 , 907. https://doi.org/10.3390/educsci14080907

Vera-Mora G, Sanz CV, Coma-Roselló T, Baldassarri S. Model for Designing Gamified Experiences Mediated by a Virtual Teaching and Learning Environment. Education Sciences . 2024; 14(8):907. https://doi.org/10.3390/educsci14080907

Vera-Mora, Glenda, Cecilia V. Sanz, Teresa Coma-Roselló, and Sandra Baldassarri. 2024. "Model for Designing Gamified Experiences Mediated by a Virtual Teaching and Learning Environment" Education Sciences 14, no. 8: 907. https://doi.org/10.3390/educsci14080907

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