Databases

Code

DATA2003

School

Computer Science

Level

2

Credits

N/A

Semesters

Full Year UK

Summary

To give learners broad overview of concepts, practical skills and applications in the databases area, including:

• Core ideas of what a database is and how it is structured, focusing on relational databases
• How databases are designed, including entity-relationship models, designing a high-quality data model and evaluating the quality of a database schema
• How information is inserted, modified, processed and extracted from a database, including an introduction to SQL
• Alternative models of databases such as NoSQL and graph databases
• How to choose the appropriate tools and data representations for a particular application
• How databases handle different kinds of data, including media data, text, and “unstructured” data
• Basic ideas of where databases are located physically, including local servers and cloud storage; concurrency, real-time and update issues; and how data is coordinated in large organisations, including ideas of data warehousing and OLAP
• Ideas of database systems at different scales and the technologies needed to handle data at various scales, including basic ideas of “big data”
• Legal and ethical concerns such as privacy, rights to access data, freedom of information
• Case studies, including both examples of good practice and examples of problems and failures
   

Target Students

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

Classes

12 weeks x 2 hours of online learning 1 x 5 hour block release

Assessment

• 100% Assessment: Completion of the teaching block

Assessed by end of designated period

Educational Aims

Learning Outcomes

To work confidently with a wide range of data in databases and database languages so as to be able to tackle substantial data problems.
 

Be able to choose appropriate technologies and models for the use of databases in practical applications and to evaluate the strengths and weaknesses of existing systems.
 

Understand the role of ethical, privacy, bias and legal concerns and to embed this into practice.
 

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. 

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

• Adam Walker