Algorithms, Data Structures and ProgrammingObject Oriented principles OO Design and implementation Design patterns Components Design of data-structures and algorithms, abstract data types Influence of data-structure and algorithms on performance.Fundamentals Of ProgrammingProgramming with high-level languages, e.g., Python, JavaScriptVariables, control structures, functions, libraries/modules, scriptingsearching/sorting, lists/arrays/dictsRelational databases: design and implementationWeb Applications: developing secure server-side applications using HTML, CSS and server-side languagesSystems design & system architectureData analysis and statistics: descriptive, hypothesis testing, basic visualisationUser Experience (UX) and User Interfaces (UI)Computational ThinkingReflective writingStudy skillsBasic HTML & CSS ProgrammingData VisualisationEncoding theoryVisualisation history & theoryCurrent trends in visualisationUse of appropriate software tools and libraries for data analysis Python: Pandas, Scipy, Numpy, Matplotlib, Seaborn, Altair, BokehJavaScript: D3, Plotly, HighchartsRetrieving and storing data (JSON, csv) using JavaScript and PythonVisualisation developmentSoftware EngineeringSoftware development lifecycle Requirements analysis/specification/management Design (UML) Quality software implementation (ISO 25000) Project management ((Prince2, BMBOK, ISO 21500, Prince 2 Agile) Risk Management (ISO 31000) Team working theory Legal and Ethical Issues (Data Protection Act, Computer Misuse Act, Intellectual Property Law, Information Ethics, BCS Code of Conduct) Computer architecture, operating systems, networks.Topics, Research and Skills in ComputingPurpose, goals and process of academic researchComputer Science researchProject proposalProject management and organizationResearch strategies, data generation methods and data analysisLiterature review: strategies and methods for effective literature searchResearch ethics

• Worked alongside team members and leaders to identify analytical requirements and collect information to meet customer and project demands. Applied analysis skills, leveraging insights, developing and deploying data models and evaluating and improving existing models to create solutions. Provided solutions and strategies to address business challenges and help businesses remain competitive using market trend data. Adapted workflow to meet need business needs. • Reviewed business areas to strengthen analytics, enable strategic planning and support decision-making processes with solid data.• Gathered analytics data using different sources. Set up structures for producing reports.