Unit 3: Emerging Trends in Computer Science

Learning Outcome

• Appreciate the emerging trend of computing that led to the advancement of human computer interaction.
• Identify the fundamental technologies that are deployed to support the emerging computing technologies.
• Identify the risks and threats associated with emerging technology and suggest guidelines to mitigate those risks.

Readings

• Read Section 4, 4.6, 9.6, 9.7, 10.6, 11.7, Chapter 11 of your core text book: Brookshear, J. and Brylow, D. (2018), Computer Science - An Overview. 13th Ed. Harlow: Pearson.
• El-Seoud, S & El-Sofany, H. (2017) Big Data and Cloud Computing: Trends and Challenges. International Journal of Interactive Mobile Technologies 11(34). DOI: 10.3991/ijim.v11i2.6561.
• Neves, P.; Schmerl, B.; Cámara, J. & Bernardino, J. (2016) 'Big Data in Cloud Computing: Features and Issues', In Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD 307-314. DOI: 10.5220/0005846303070314.
• Carsten M. (2017) Security and privacy in the internet of things. Journal of Cyber Policy 2(2): 155-184. DOI: 10.1080/23738871.2017.1366536.
• This article will talk how the Big Data can be used in different domains for forecasting: Hassani, H., Silva, E.S. (2015) Forecasting with Big Data: A Review. Ann. Data. Sci. 2: 5-19.
• This sets the concept of BYOC and BYOD: North Bridge (2013) Future of cloud computing survey reveals business driving cloud adoption in everything as a service era; IT investing heavily to catch up and support consumers graduating from BYOD to BYOC .
• Sultan, H. & Khalique, A. (2017) Internet of Things: Future of Cloud Computing. International Journal of Advanced Research in Computer Science 8(2).
• Sherman, R. (2014) Strong AI strong artificial intelligence and consciousness. Social Psychology of Cyberspace.

Explore AI tools developed by Google

I choose to explore Tensorflow, which is a software application popular for implementing machine learning algorithms particularly neural networks. What it does, is that i takes input as a multi-dimensional array where we can construct a sort of flowchart of operations that would like to perform on that input. It’s used to process the input, through the system of operations and designed flow and comes out as output.

Explore Google Translation

Google translate has a vast database of the word, and phrases with their alternative in different languages that rely on user’s feedback data when suggesting a translation. It also has a library of verified phrases and contributors. It relies on machine learning to compare the database records from user-inputted data, user feedback and labelled data for suggestion a translation and judging if the output is relevant. As well as showing other suggestions for alternative translation.

Reflection

In this module, I have reviewed my knowledge in artificial intelligence (AI) on its capacity to parse and apply knowledge in different sectors and integrate different data sources to create valuable insights that can help in decision making or services output. I have also investigated the internet of things which brings together information from a series of connected devices that allows for the creation of analytics of systems, I also came across biometrics, blockchain, and robotics.

Unit 4: Computing Infrastructure and Network

Learning Outcome

• Differentiate between various networking approaches, namely, internet and intranet.
• Identify various network topologies and appreciate the technologies and protocols that make these networks to work.
• Critically review a topology selection for a network and review the cybersecurity threats.

Readings

• Read Chapter 4 of your core text book: Brookshear, J. and Brylow, D. (2018), Computer Science - An Overview. 13th Ed. Harlow: Pearson.
• Siddiqui, F., Zeadally, S & Salah, K. (2015) Gigabit Wireless Networking with IEEE 802.11ac: Technical Overview and Challenges. Journal of Networks 10(3). DOI: 10.4304/jnw.10.3.164-171.
• This article introduces to the threats and challenges of networks: CISCO (n.d.) What is network security.
• TED (n.d.) Avi Rubin: All your devices can be hacked.
• This is a case example of autonomous car and the article highlights the challenges of such technology: Lu, N., Cheng, N., Zhang, N., Shen, X. & Mark, J.W. (2014) Connected vehicles: Solutions and challenges. IEEE internet of things journal 1(4): 289-299.

Introduction to HTML

The HTML excerises were a good exercise to see how the HTML and HTML5 have evaluated since the first time I learned HTML.

Emerging Technology

Virtual Machine: an instance of a physical computer with the only difference that virtual machines are computing within physical servers, existing only as code. Using virtual machines can be useful to set up a dedicated environment for a program, or a server. Also, during the research and development phase, where it allows us to create an unlimited number of computers with different operating systems using code only and sharing the resources of the same physical computer/server.

Bring Your Own Device (BYOD): This is a policy that’s set by companies to allow employees to use their personal devices such as smartphones, laptops, and tablets for work. Which can help business reduce the fixed costs and maintenance for such while it can have some downside with security risks and privacy, as the device isn’t managed by the company the employee is free to download and use other software and programs that might have access to certain corporate data. Also, other users might be allowed to use their personal computers and might have access to the company files and data.

Parallel computing: Parallel computing refers to the process of breaking down larger problems into smaller, independent, often similar parts that can be executed simultaneously by multiple processors communicating via shared memory, the results of which are combined upon completion as part of an overall algorithm. The primary goal of parallel computing is to increase available computation power for faster application processing and problem-solving.

Hadoop: a collection of tools that allows the storing of big data in a readily accessible and distributed environment. It enables processing the data parallelly, it’s also an open-source framework that is quite popular in the big data industry.

Distributed computing: a field of computer science that studies distributed systems, that systems with components located on different networked computers, which communicate and coordinate their actions by passing messages to one another to achieve a common goal.

Reflections

In this unit, I learned about networking and computer networks which how network infrastructure comprises hardware and software to enables computing and communication between users, services, application and processes using different communication networks security protocols such as Transmission Control Protocol TCP/IP, HTTP, Secure Hypertext Transfer Protocol is the secure version of HTTP, FTP, Secure File Transfer Protocol (SFTP), and Secure Socket Layer (SSL) protocols. These protocols require management protocols to maintain and govern the network through protocols such as ICMP and SNMP.

Unit 5: Software as a Tool - Its Functionality and Development Methodologies

Learning Outcome

• Classify software and review the selection of software as a creative tool in order to solve a problem in your workplace.
• Apply different software development methodology to collaborate, design, develop and test software.
• Identify correct methodology for software validation and correctness.

Readings

• Read Section: 5.3, 10, 10.1, 10.6, 12.4 and Chapter 7 of your core text book: Brookshear, J. and Brylow, D. (2018), Computer Science - An Overview. 13th Ed. Harlow: Pearson.
• Wirth, N. (2008) A Brief History of Software Engineering. IEEE Annals of the History of Computing 30(3): 32-39. DOI:10.1109/MAHC.2008.33.
• Ambler, S. (2012) 2011 IT project success rates survey.
• This article introduces an important software development methodology adapted by many large and small organisations: Ambler, S. (2014) Agile modeling (AM) home page: effective practices for modeling and documentation.
• Asur, S. & Hufnagel, S. (1993)Taxonomy of rapid-prototyping methods and tools. Proceedings The Fourth International Workshop on Rapid System Prototyping, Research Triangle Park. Pp. 42-56. DOI: 10.1109/IWRSP.1993.263196.
• Hughey, D. (2009) The traditional waterfall approach.
• Lewallen, R. (2005). Software Development Life Cycle. Code Better.
• This article introduces the basics of SCRUM methodology: Scrum (2014) Scrum.org.

Formative Activities

Identify a system and a piece of software of your choice, i.e. operating system or an application that you are familiar with. Investigate the features of these pieces of software and then compare their features. Where appropriate, use images to demonstrate and explain the functionality of these pieces of software.

Reflections

This unit has allowed me to learn about the different types of software and digital solutions, as well as it was very helpful to learn flode to build the flow of data processing, I have developed programs before but never tend to actually visualise the process, now I feel more confident to come up with new algorithms and to be able to visualise it for testing, optimisation, and most importantly to be able to share my ideas and process with others.

Unit 7: Data Abstraction

Learning Outcome

• Read Section 6.2, 6.3 and Chapter 8 of your core text book: Brookshear, J. and Brylow, D. (2018), Computer Science - An Overview. 13th Ed. Harlow: Pearson.
• FAQs.org (n.d.) Queues and priority queues.
• NIST (n.d.) Dictionary of algorithms and data structures.
• This website presents the fundamentals of data structure:
• Virginia Tech (n.d.) Graphs.
• Virginia Tech (n.d.) Multidimensional arrays.
• Virginia Tech (n.d.) Ordered list: The implementation view.
• Virginia Tech (n.d.) Queues: The abstract view.
• Virginia Tech (n.d.) Stacks: The abstract view.
• Virginia Tech (n.d.) Trees.

Formative activities

Compare and contrast between linear and non-linear data structure, and Stack and Queue

Reflections

It was good to read and learn about the different data structure, how to analyse algorithms to evaluate the time and space an algorithm may consume for an operation. I feel now more confident when evaluating and comparing an algorithm and how I can translate a real-life problem into a computer machine problem.

Unit 8: An Introduction to Algorithms and Basic Programming Concepts

Learning Outcome

• Apply the algorithm framework for describing and solving a problem.
• Demonstrate a step by step approach to solving a problem using pseudocode.
• Validate the output using different quality assurance matrices.

Readings

• Read Chapters 5 and 12, Appendix D of your core text book: Brookshear, J. and Brylow, D. (2018), Computer Science - An Overview. 13th Ed. Harlow: Pearson.
• Acm (n.d) Collected Algorithms.
• Dionysis, Z. (n.d) A gentle introduction to algorithm complexity analysis.
• Ericson, J (2019) Algorithms.
• An example program that identifies multiples written in Java: Meyer, H.B. (n.d.) Eratosthenes’ sieve.
• Roughgarden, Tim. (n.d.) Design and analysis of algorithms.

Formative activities

Completed the exercises in Codio

Reflections

Learning to write pseudocode was a bit challenging for me, although it doesn’t have a syntax I felt the need to refer to other Pseudocode examples and references. I kind of prefer using Flode chats as I can visualise the code and interactions where I can perhaps find other ways around the solution from the flow.
Learning how to deal with lists, strings, user-defined functions, and recursion in python was like learning how to speak English in different accents. Although I’m still finding it a bit challenging to keep up with the python syntax where I sometimes forget to indent an if-statement or add the colons, I feel I’ll overcome this once I use python in a full project exercise.

Unit 9: Solving Problems Using Python

Learning Outcome

• Write an algorithm.
• Investigate the quality parameters of an algorithm.

Readings

• The reading this week focuses on the high-level programming language features, structure and their types. This unit also demonstrates examples of algorithms using high-level programming languages.
• Read Chapter 6 and Appendix E of your core text book: Brookshear, J. and Brylow, D. (2018), Computer Science - An Overview. 13th Ed. Harlow: Pearson.
  Additional Reading
  Lynda.com (n.d.) Programming Languages Tutorials.
• Mogensen, T. (2010) Basics of compiler design.
• Virginia Tech (n.d.) Programming languages.

Formative activities

Continue with the Learning Python programming exercises

Reflections

I now feel much more confident using python, I’m now able to write a microprogram with the help of nested functions, user inputs, and being able to export it to the user or even with some extra research to return the data in JSON format which can be done easily using the built-in package called JSON.
I’m grateful for this course to allow me to learn and use Python, it’s something I’ve always wanted to learn.

Unit 11: Advanced SQL

Learning Outcome

• Write complex queries to extract the results from a database using SQL.
• Apply aggregate, arithmetic, date and string functions in SQL for summarised reporting.
• Evaluate query results.

Readings

• Hoffman, J. (1997) Introduction to Structured Query Language.

Formative activities

Completed the Advanced SQL lab in Codio and shared my Seminar 6 session on the forum

Advanced SQL lab exercises - Seminar 6 preparation

Reflections

This unit was one of my favourites, as I always wanted to dive deep into the SQL world and the advanced exercises were so helpful to allow me better understand the different SQL commands, joining different tables, and managing databases cautiously. I enjoyed preparing the Seminar 6 document, writing the code explanation has really taught me the ins and outs of SQL syntax.