Introduction to Data Science

Code

DATA1001

School

Computer Science

Level

1

Credits

N/A

Semesters

Autumn UK, Spring UK

Summary

Indicative content: 

• Attitude to learning 
• Professionalism and Ethics 
• The history and context of Data Science 
• Data Processing Pipeline 
• Introduction to Data 

Target Students

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

Classes

One block release day - 10 hours 2 x one hour remote tutorials 2 x one hour independent learning

Assessment

• 100% Assessment: Completion of teaching block

Assessed by end of designated period

Educational Aims

Learning Outcomes

• Understanding of the context and history of data science

• Understanding of the purpose of data science and the professional data scientist

• An understanding of the nature of data and the different types of data

• A high-level understanding of ethics and professionalism especially with respect to the use of data

• Can assess and make progress towards achieving the data scientist apprenticeship KSBs

KSBs:

K1. The context of Data Science and the Data Science community in relation to computer science, statistics and software engineering. How differing schools of thought in these disciplines have driven new approaches to data systems.
K2. How Data Science operates within the context of data governance, data security, and communications. How Data Science can be applied to improve an organisation’s processes, operations and outputs. How data and analysis may exhibit biases and prejudice. How ethics and compliance affect Data Science work, and the impact of international regulations (including the General Data Protection Regulation.)
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.

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.
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.
S7. Develop and maintain collaborative relationships at strategic and operational levels, using methods of organisational empathy (human, organisation and technical) and build relationships through active listening and trust development.
S8. Use project delivery techniques and tools appropriate to their Data Science project and organisation. Plan, organise and manage resources to successfully run a small Data Science project, achieve organisational goals and enable effective change.

B2. Empathy and positive engagement to enable working and collaborating in multi-disciplinary teams, championing and highlighting ethics and diversity in data work.
B3. Adaptability and dynamism when responding to varied tasks and organisational timescales, and pragmatism in the face of real-world scenarios.
B4. Consideration of problems in the context of organisation goals.
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.
B6. A commitment to keeping up to date with current thinking and maintaining personal development. Including collaborating with the data science community.

Conveners

• Dr Simon Kent
• Dr Ria Symonds