Probability Models and Time Series

Code

DATA2002

School

Computer Science

Level

2

Credits

N/A

Semesters

Full Year UK

Summary

In this teaching block, the ideas of probability introduced in the first year are extended to a multivariate setting. It will provide an introduction to stochastic processes (i.e. random processes that evolve with time) and time series analysis (i.e. series of observations evolving in time and observed at discrete points in time). There will be a particular focus on discrete-time Markov chains and forecasting methods which are fundamental to the wider study of techniques required in the analysis of probabilistic and statistical models.

Target Students

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

Classes

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

Assessment

• 100% Assessment: Completion of the teaching block

Assessed by end of designated period

Educational Aims

Learning Outcomes

• Demonstrate and apply a deeper understanding of continuous random variables and their applications in the field of data science.
• Understand and apply basic properties of discrete-time Markov chains.
• Carry out initial data analysis of time-series data and be able to identify and remove simple trend and seasonality.

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 - 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

• Dr Daniel Cavey