Foundations in Maths for Data Science

Code

DATA1002

School

Computer Science

Level

1

Credits

N/A

Semesters

Autumn UK

Summary

This course provides a basic course in algebra and calculus required for data science. It introduces key elements of definition, manipulation, graphical representation of functions, as well as quadratic equations, simultaneous linear equations, differentiation and integration.

Target Students

Level 6 Data Scientist apprenticeship students only.

Classes

27 hours of weekly distance learning mathematical exercises with supporting video material and drop-in remote support sessions. Two x 6-hour block release days.

Assessment

• 100% Assessment: Completion of teaching block

Assessed by end of designated period

Educational Aims

Learning Outcomes

• Demonstrate algebraic facility with algebraic topics including linear, quadratic, exponential, logarithmic and trigonometric functions.

• Produce and interpret graphs of basic functions of these types.

• Compute derivatives and definite and indefinite integrals of basic functions of these types.

• Understand and be able to apply the basics of vector algebra.

KSBs

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

• Data processing and storage, including on-premise and cloud technologies.
• Database systems including relational, data warehousing & online analytical processing "NoSQL" and real-time approaches, the pros and cons of each approach.
• 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 - using:

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

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.

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

• Mr Alan Barker