INTRODUCTION
Agile Development with Scrum module introduces learners to agile development with scrum. Scrum is the most widely used agile framework at the moment, and has been applied to software development across a whole range of sectors, from web development to computer games. It can be used to manage and control contemporary and complex software development using a range of iterative techniques. This module therefore looks at scrum in detail from theory and foundations to practical applications and drawbacks. The module starts with explaining the agile movement with an overview of agility, principles, methods and techniques. It explains the well-known contemporary agile processes.it explains the application of the Scrum framework that is, origins, why scrum works, sprints, activities, meetings, team, backlogs, scalability. It explains the process of combining scrum with other systems development approaches. Explains the business analysis and modelling technical solutions using suitable modelling techniques and ends with explaining the usage of software development tools used by agile teams.
LEARNING OUTCOMES
At the end of this module, learners should be able to:
– Explain what scrum is and highlight its theoretical and practical forms.
– Examine the agile movement with an overview of agility, principles, methods and techniques.
– Explain the well-known contemporary agile processes.
– Gain experience in working in an agile scrum team environment and follow the scrum method to resolve a problem from its conception through to its implementation.
– Demonstrate the ability to analyse, design and develop creative solutions and systems for contemporary problems.
– Know through experience, the practical challenges associated with working as a member of an agile scrum software development team.
COURSE CONTENT
UNIT 1: The Agile movement: Overview of agility, principles, methods and
techniques.
UNIT 2: Introduction to most well – known contemporary agile processes.
UNIT 3: Deep understanding and application of the Scrum framework: origins,
why Scrum works, sprints, activities, meetings, team, backlogs, scalability
etc.
UNIT 4: Combining Scrum with other systems development approaches.
UNIT 5: Business analysis and modelling technical solutions by using suitable
modelling techniques.
UNIT 6: Introduction and use of software development tools used by agile
teams.
ASSESSMENT
Prescribed Textbooks
Rob Cole and Edward Scotcher (2015). Agile Project Management: A Practical
Guide to Using Agile, Scrum and Kanban. Harlow: Pearson Education
Andrew Stellman Jennifer Greene (2016). Head First Agile – A Brain-Friendly
Guide. Sebastopol: O’Reilly Media
Recommended Textbooks
Andrew Stellman and Jennifer Greene (2015). Learning Agile: Understanding
Scrum, XP, Lean and Kanban. Sebastopol: O’Reilly Media
INTRODUCTION
Web Programming module introduces learners to the second part of web programming with a focus on Java. The module starts with explaining the concepts of JavaScript Full Stack Development. It explains free open-source JavaScript based stacks can be used to build single page, multi-page as well nas complex dynamic web applications. The module ends with explaining new technologies such as mongoDB, express JS, node JS, NPM and a frontend JavaScript framework. Students will gain an introduction to these technologies while developing various small web applications.
LEARNING OUTCOMES
At the end of this module learners should be able to:
– Know and explain the concepts of JavaScript Full Stack Development
– Deploy a simple web server using server-side JavaScript with Node JS, Express JS
– Know and apply knowledge on running the NPM package manager from the command line.
– Know and apply the knowledge in installing a JavaScript frontend framework and code a simple dynamic web interface.
– Know and analyse the differences between relational and nonrelational databases.
COURSE CONTENTS
UNIT 1: Review of JavaScript.
UNIT 2: Introduction to Node JS and server-side JS.
UNIT 3: Introduction to Mongo DB and NoSQL.
UNIT 4: Express JS.
UNIT 5: NPM.
UNIT 6: Introduce a frontend JavaScript framework.
UNIT 7: Client / Server relationship – HTTP.
UNIT 8: Simple CRUD functionality.
ASSESSMENT
Prescribed Textbooks
Steven Blumenthal (2017). JavaScript: Learn JavaScript with Ease. Steven
Blumenthal.
Julian Shapiro (2015). Web Animation using JavaScript. DEVELOP AND DESIGN.
Pearson Education Publishing
Recommended Textbooks
Nicholas C. Zakas (2012). Professional JavaScript for Web Developers.
Indianapolis: John Wiley and Sons
INTRODUCTION
Information Security module introduces learners to information security. The module focuses on information security threats, risks and corresponding countermeasures which, includes confidentiality, integrity and availability in different computer systems, also taking into account privacy, secure design and cryptography and its applications. The module starts by explaining information security issues and explains the concept of secure systems.
LEARNING OUTCOMES
At the end of this module learners should be able to:
– Explain what information security is
– Demonstrate knowledge of information security principles and concepts.
– Demonstrate a critical appreciation of security risk assessment and controls for different systems and organisations.
– Demonstrate an understanding of algorithms and procedures covering cryptography and its applications.
– Know and apply the principles of secure design.
– Demonstrate an appreciation of sociotechnical approaches to cyber
security.
COURSE CONTENTS
UNIT 1: Introduction to Information Security;
UNIT 2: Designing Secure Systems;
ASSESSMENT
Prescribed Textbooks
Chapple M., Stewart M.J and Darril Gibson (2018). CISSP-Certified Information
Systems Security Professional Official Study Guide. Indianapolis: John Wiley and Sons
Whitman M.E. and Mattord H.J. (2018). Principles of Information Security.
Boston: Cengage Learning
Recommended Textbooks
Thomas R. Peltier (2002). Information Security Policies, Procedures, and
Standards Guidelines for Effective Information Security Management. Boca
Raton: CRC Press
INTRODUCTION
Data and Web Analytics module introduces learners to data and web analytics. The module starts with explaining clickstream data collection techniques. It explains the process of Identifying, defining and interpreting commonly used web analytic metrics and KPIs. The module explains the concept of descriptive analysis, distributions, significance tests, hypothesis testing and correlation analysis. It further explains the concept of data quality, data cleaning and data integration. It explains the process of data presentation using graphics and tables. Explains the process of implementing different statistical tests in R and interpreting the results. It explains the concept of predictive analytics techniques and models. Explains the process of Implementing regression analysis and multivariate analysis then ends with explaining the process of implementing time series analysis.
LEARNING OUTCOMES
At the end of this modules learners should be able to:
– Know and explain what clickstream data collection techniques are.
– Demonstrate an understanding of the methods and algorithms used in
data and web analytics to identify the challenges and select appropriate solutions.
– Analyse and manipulate clickstream data to extract statistics and
features and provide analytic insights.
– Critically evaluate, select, and employ appropriate tools, technologies
and data models to provide answers to analytic questions.
COURSE CONTENTS
UNIT 1: Clickstream data collection techniques.
UNIT 2: Identify, define, and interpret commonly used web analytic metrics and KPIs
UNIT 3: Descriptive analysis, distributions, significance tests, hypothesis testing and correlation analysis.
UNIT 4: Data quality, data cleaning and data integration
UNIT 5: Data presentation using graphics and tables.
UNIT 6: Implementation of different statistical tests in R and interpret the results.
UNIT 7: Predictive analytics techniques and models.
UNIT 8: Implementation of regression analysis and multivariate analysis.
UNIT 9: Implementation of Time series analysis.
ASSESSMENT
Prescribed Textbooks
Arshdeep Bahga & Vijay Madisetti (2019). Big Data Science and Analytics.
Arshdeep Bahga & Vijay Madisetti
BERNARD MARR (2015). BIG DATA USING SMART BIG DATA, ANALYTICS AND
METRICS TO MAKE BETTER DECISIONS AND IMPROVE PERFORMANCE. West
Sussex: John Wiley and Sons
Recommended Textbooks
David Dietrich etal (2015). Data Science & Big Data Analytics Discovering,
Analyzing, Visualizing and Presenting Data. Indianapolis: John Wiley and Son