Team Working Skills

Code

DATA2007

School

Computer Science

Level

2

Credits

N/A

Semesters

Full Year UK

Summary

This teaching block is intended to provide some up-front learning in relation to successful working software development teams.

The module will cover:

The nature of teams and building successful teams.
Software development methodology – especially agile development and how to run an agile project in practice.

The teaching block will also provide ongoing supervision support for each software development project group to ensure that the approach to each team’s software development teamwork is developmental, and improving through the project.

Target Students

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

Assessment

• 100% Coursework: Completion of the teaching block

Assessed by end of designated period

Educational Aims

Learning Outcomes

Team Goals

To learn approaches to working successfully in software development teams.  
 

To understand how to establish software specifications and deliver code against those specifications.
 

Reflect on issues arising within a team environment and learn how to resolve them. In doing so learn from the group project experience. 

Plan, manage and track a significant data science software activity.
 

Identify a suitable data science problem, questions and corresponding software which relate to organisational goals.

KSBs

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:
4.    Statistical and mathematical models and methods. 
5.    Advanced and predictive analytics, machine learning and artificial intelligence techniques, simulations, optimisation, and automation. 
6.    Applications such as computer vision and Natural Language Processing.
7.    An awareness of the computing and organisational resource constraints and trade-offs involved in selecting models, algorithms and tools.
8.    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.
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.
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.

B1. An inquisitive approach: the curiosity to explore new questions, opportunities, data, and techniques; tenacity to improve methods and maximise insights; and relentless creativity in their approach to solutions.
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

• Adam Walker