Comprehensive course analysis
About the course
This course provides a comprehensive survey of the core vocabulary and methods of data science, data analytics, and machine learning.
Data science is a rapidly spreading field that combines statistical analysis, data management, computation, and substantive expertise, with the goal of improving decision-making in business, government, administration, law, and just about every other field.
One of the key challenges for decision-makers and managers is to understand what makes for good data science, and how the evidence from this field should be used in evaluation and decision-making.
The focus of this course is on examples of good and bad data science, with real-world applications from government, business, and law. By the end of the course, students will be familiar with the concepts of data science and will have learned how to evaluate quantitative evidence and how to design new studies using big data and data scientific tools.
- A comprehensive top-level understanding of the core concepts and methods of data science, including data management, data analysis, machine learning, and statistical learning.
- The ability to evaluate evidence from statistical learning and data science, in order to make informed decisions.
- A thorough awareness of the core issues in designing new data scientific studies.
- Practical and applied knowledge of the core material through applications drawn from business, government, and law, including at least one presentation from a practitioner in one or more of these areas.
Day 1: Data and Data Science
- Introduction and overview of data science
- The vocabulary of data, the structure of data and the types of data
- Introduction to the tools used to record, structure, link and retrieve data
- Databases and their role in building a business or organisational infrastructure
Day 2: Data Science Analysis and Data Regulation
- Interpreting Data Science
- Hypothesis tests, Type I and Type II errors, p-values, statistical significance, coefficients, regression analysis, model fit, and common association measures
- Bayesian reasoning and probability
- Data protection and the law
- Data governance and ethical considerations
Day 3: Machine Learning and Artificial Intelligence
- An overview of machine learning and how to interpret it
- Practical guide to machine learning methods and their potential pitfalls and limitations
- Supervised and supervised methods of machine learning
- Business uses of data and AI
- Recent developments in AI such as embeddings, neural networks and deep learning
Day 4: Collecting the interpreting survey data
- How does survey data fit into other types of data
- Translating managerial problems into a survey
- Approaches in survey research
- Implementing survey research
- Items: Statements and scales
- Pitfalls to avoid in survey research
- Good and bad practices, e.g. the net promoter score
Day 5: Social Listening: Deriving actionable insights from social media and textual data
- How new sources of data can be used to derive consumer behaviour insights
- Social listening and machine learning
- Advances in machine learning
- Text mining and sentiment analysis
- Course conclusions
Virtual course structure
Rather than an intensive five-day course, the virtual courses will be delivered over four weeks and you will be able to manage them alongside your working hours.
Week one will be a course orientation where participants can access your pre-course readings and meet fellow classmates, as well as a live welcome session hosted by our world-leading faculty.
Following that, your course will be taught over the course of three weeks with 10 contact hours each week, matching the level of contact time you would get on an on-campus course. Teaching will typically occur on weekdays during regular UK university hours. During this time, you'll have dedicated slots to meet your fellow participants, a virtual event per programme, full technical support by phone, email or live-chat and a 'virtual graduation' ceremony to end the course.
Experience Keywords Commons-based peer production; identity assurance; identity cards; information systems; innovation; plagiarism; policy Research Summary Edgar is the research co-ordinator of the LSE Identity Project and represented the project at the Science and Technology Select Committee ...
Awards Winner of 1999 APSA Best Research Web Site Award (awarded as part of a group) presented by the Computers and Multimedia Section at the 1999 annual meeting; Winner of 1998 APSA Best Research Software Award presented by the Computers and Multimedia Section at the 1998 annual meeting. Expe...
In February 2016 Professor Sabine Benoit (nee Moeller) joined the University of Surrey as a Professor of Marketing. She is a member of the Department of Retail and Marketing at Surrey Business School. Her main research fields are Service- and Retail-Marketing. Her work has been published in lead...
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