Visualisation Techniques

Code

DATA2005

School

Computer Science

Level

2

Credits

N/A

Semesters

Full Year UK

Summary

Data Visualisation is the process of extracting knowledge from complex data and presenting it to a user in a manner that this appropriate to their needs to make it easier to pull insights from. This module provides an understanding of data visualisation concepts, terminology, methods, and its importance in data processing. You will learn about using a range of visual elements and software to see and understand trends outliers and patterns in data. You will also learn a productive way to convey stories form your data highlighting useful information.

This module enhances human perception and cognition to make sense of data in a way to effectively communicate conclusions drawn for the data to a wider audience. You will also learn about the challenges associated with visualising large or ambiguous or time-based datasets. You will also learn about psychological theories that help explain how humans process information and consider their relevance to the design of effective visualisations. Using example images, we will critique them, building up knowledge about what works, and what does not.

We will also explore visualisation techniques linked to textual analysis.

Learners will be encouraged to use visualisation tools used within their organisation to present findings in their role.

Target Students

Only available to those studying towards the Data Scientist Degree apprenticeship programme.

Classes

15 hours of weekly distance learning mathematical exercises with supporting video material and drop-in remote support sessions. One x 5-hour block release day.

Assessment

• 100% Assessment: Attendance on the teaching block

Assessed by end of designated period

Educational Aims

Learning Outcomes

• Understand the key concepts of data visualisation using relevant software.
• Can use appropriate visualisation tools and techniques to present data of different types appropriately to different audiences.
• Effectively communicate and ideas relating to the software process and data science solutions.

KSBs

K3. How data can be used systematically, through an awareness of key platforms for data and analysis in an organisation, including:

1. Data processing and storage, including on-premise and cloud technologies.
2. Database systems including relational, data warehousing & online analytical processing, “NoSQL” and real-time approaches; the pros and cons of each approach.
3. Data-driven decision making and the good use of evidence and analytics in making choices and decisions.

K4. How to design, implement and optimise analytical algorithms – as prototypes and at production scale – using:

1. Statistical and mathematical models and methods.
2. Advanced and predictive analytics, machine learning and artificial intelligence techniques, simulations, optimisation, and automation.
3. Applications such as computer vision and Natural Language Processing.
4. An awareness of the computing and organisational resource constraints and trade-offs involved in selecting models, algorithms and tools.
5. Development standards, including programming practice, testing, source control.

K5. The data landscape: how to critically analyse, interpret and evaluate complex information from diverse datasets:

1. Sources of data including but not exclusive to les, operational systems, databases, web services, open data, government data, news and social media.
2. Data formats, structures and data delivery methods including “unstructured” data.
3. Common patterns in real-world data.

S1. Identify and clarify problems an organisation faces, and reformulate them into Data Science problems. Devise solutions and make decisions in context by seeking feedback from stakeholders. Apply scientific methods through experiment design, measurement, hypothesis testing and delivery of results. Collaborate with colleagues to gather requirements.

S2. Perform data engineering: create and handle datasets for analysis. Use tools and techniques to source, access, explore, prole, pipeline, combine, transform and store data, and apply governance (quality control, security, privacy) to data.

S3. Identify and use an appropriate range of programming languages and tools for data manipulation, analysis, visualisation, and system integration. Select appropriate data structures and algorithms for the problem. Develop reproducible analysis and robust code, working in accordance with software development standards, including security, accessibility, code quality and version control.

S4. Use analysis and models to inform and improve organisational outcomes, building models and validating results with statistical testing: perform statistical analysis, correlation vs causation, feature selection and engineering, machine learning, optimisation, and simulations, using the appropriate techniques for the problem.

S5. Implement data solutions, using relevant software engineering architectures and design patterns. Evaluate Cloud vs. on-premise deployment. Determine the implicit and explicit value of data. Assess value for money and Return on Investment. Scale a system up/out. Evaluate emerging trends and new approaches. Compare the pros and cons of software applications and techniques.

S6. Find, present, communicate and disseminate outputs effectively and with high impact through creative storytelling, tailoring the message for the audience. Use the best medium for each audience, such as technical writing, reporting and dashboards. Visualise data to tell compelling and actionable narratives. Make recommendations to decision makers to contribute towards the achievement of organisation goals.

B3. Adaptability and dynamism when responding to varied tasks and organisational timescales, and pragmatism in the face of real-world scenarios.

B5. An impartial, scientific, hypothesis-driven approach to work, rigorous data analysis methods, and integrity in presenting data and conclusions in a truthful and appropriate manner. 

Conveners

• Dr Nazmul Hussain