Software Portfolio Assessment

Code

ZDAT1001

School

Computer Science

Level

1

Credits

40

Semesters

Full Year UK

Summary

This assessment comprises a progressive series of data science problems which require learners to implement software solutions.  It provides an assessment for learning opportunity for learners to draw on the teaching and formative activities in the teaching blocks and demonstrate their knowledge and develop their skills in delivery of data science solutions.

The portfolio will comprise three parts:

Basic data science problem – requiring learners to demonstrate the implementation a simple data science solution.

Progressive data science problem – requiring learners to demonstrate a wider range of programming skills on a more difficult data science solution requiring more creativity from learner.

Deeper (preferably work-based) data science problem - for the final part, learners will be encouraged to seek a relevant problem related to their role or employer.  The learner will be expected to draw from on-the-job context (organisational, business questions, proprietary data)

The portfolio assessments across this programme provide an opportunity for apprentices to understand how to start structuring a portfolio of evidence to demonstrates the acquisition and application of knowledge, skills and behaviours in line with the apprenticeship standard. One of the conditions for passing through the gateway and on to the end point assessment is a portfolio of evidence which can be used as the basis for the professional discussion in the end point assessment. This therefore helps the apprentices in understanding the expectations and practice in the preparation of an assessed portfolio before reaching the gateway review.  

Target Students

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

Assessment

• 100% Portfolio: Data Science Problem Portfolio

Assessed by end of designated period

Learning Outcomes

Can use appropriate tools to write simple programs which solve a problem, especially a data science problem.
 

Understands and can use basic procedural control structures and programming operators to implement simple algorithms.
 

Can store and manipulate different types of data (for example numerical, textual, lists, sets, maps).
 

Can produce simple visualisations of data using using simple programming constructs.
 

Understands the need for software testing and can demonstrate basic software testing principles in practice.
 

Understand and logically evaluate program requirements and specifications. 
 

Understand the complex ideas of programming solutions and relate them to particular problems. 
 

Design and analyse simple algorithms and data structures.  
 

Read, clean and analyse different kinds of data from different sources to produce data science solutions.
 

Apply appropriate software development methods and tools. 
 

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:  
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.  
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.  
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

Conveners

• Adam Walker