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This field comprises two sub-fields: the theory of algorithms, which involves the design and analysis of computational procedures; and complexity theory, which involves efforts to prove that no efficient algorithms exist in certain cases, and which investigates the classification system for computational tasks. Time, memory, randomness and parallelism are typical measures of computational effort.
Theoretical computer science is a natural bridge between mathematics and computer science, and both fields have benefited from the connection. The field is very active, with exciting breakthroughs and intriguing challenges. The P =? NP problem is one of the seven of the Clay Millennium Problems. The recent polynomial time primality algorithm received a Clay Math research award.
MIT has been the leading center for theoretical computer science for several decades. A strong group of EECS Department faculty also works in this field and runs joint activities with the Mathematics faculty through CSAIL. The RSA cryptosystem and Akamai Technologies are two important success stories that were developed by Mathematics and EECS Department faculty.
Our group investigates active areas such as quantum computation, approximation algorithms, algorithms in number theory, distributed computing and complexity theory.
*Only a partial list of graduate students
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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 ?
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.
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.
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.
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Theory of Computing (ToC) is an online journal dedicated to the widest dissemination, free of charge, of research papers in theoretical computer science.
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The Department of Computer Science of The University of Chicago provides the main server, technical support, and archiving. The articles will also be posted on arXiv , a virtual guarantee of indefinite archiving and periodic adaptation to new software and storage media. Arrangements for long-term archiving at other major sites are in progress.
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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.
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.
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.
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.
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.
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.
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Model for designing gamified experiences mediated by a virtual teaching and learning environment.
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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Steps | Description |
---|---|
Technological–Pedagogical Gamification Model for a Virtual Teaching and Learning Environment (MGTP). | |
Validate the content and reliability of the MGTP through the measurement instrument. | |
Content Validity Coefficient (CVC) by Hernandez-Nieto [ ] | |
Selection of experts in EVEA Pedagogy and Gamification through the biogram and Knowledge Quotient. | |
The instrument consisted of four sections: . Analysis of Subsystem I . Analysis of Subsystem II . Analysis of Subsystem III . Opinion on the Adequacy of the MGTP | |
Communication process and methodological follow-up of the expert judgment developed. | |
E-mail sent to the experts for the development of the validation process by expert judgment. | |
Communication process and methodological follow-up of the expert judgment developed. | |
Procedure to calculate the CVC for each of the items and the general instrument and interpretation of the CVC, based on the scale of values established by Hernandez-Nieto [ ]. | |
Verification of the concordance of the items for elimination, modification or approval. Review of considerations on comments or improvements suggested by experts. | |
Procedure to calculate the reliability of the results obtained from the pilot test by means of a Cronbach’s Alpha Coefficient and a Test of Two Halves. | |
Once the application and analysis of the results obtained have been completed, the experts are informed of the entire process. |
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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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