Data Science Analytics

An interdisciplinary field dedicated to leveraging a variety of data for driving competitive advantage and sustainable success.

In this course, you will be guided through an introduction to the main concepts, techniques and innovations, with a practical focus on guidelines for operationalising data science, optimal structuring, and communicating insights.

The primary aim of this course is to give an overview of the data, questions and tools that quantitative researchers, analysts and data scientists work with to tackle increasingly sophisticated questions that stretch the limits of traditional market research techniques.

Cutting-edge advances from the fields of Artificial Intelligence, Machine Learning, and Cognitive Psychology will be covered using interactive demonstrations and through in-depth coverage of new technological solutions to complex data challenges from companies such as Google, Microsoft, Unilever, Sky, and Netflix.

The implications of advances in data science for the market research industry at large, will also be discussed throughout the course.

Attendees will be informed of the latest thinking in the field of algorithmic market research and be able to translate this for their business needs in a practical and cost-effective manner.

Who would benefit

• This course will benefit customer insight and market research agency practitioners, who want to better understand the burgeoning field of data science.

Objectives

• To inform delegates of the main approaches, tools and techniques and recent innovations for turning data into actionable insights.
• To provide delegates with hands-on experience with basic methods of acquiring and cleaning data as well as visualising data for exploratory analyses.
• Outline the process of drawing conclusions about populations in a reliable and robust manner, based on statistical inferences and hypothesis-based research studies.
• To show delegates how concepts and technologies such as R, SAS, Alteryx and Tableau can be utilised for reporting modern data analyses in a reproducible manner, where the aim is to significantly cut-down the time required to run and re-run complex analytics, by intelligently recycling and sharing code with other data scientists, researchers, and insight practitioners.
• Provide a general introduction to algorithms and their increasingly dominant role in the world of business and highlight their role as a bridge between traditional research and more recent technological advances relating to targeting and personalisation.
• To demonstrate the value of customer insight using data science approaches applied to market research data.

Learning outcomes

• Understand the basic principles of data science.
• Appreciate different perspectives on data science.
• Outline the difference between quantitative analysts and data scientists.
• Understand the basics of algorithms and hypothesis-driven analytics.
• Design and run simple experiments, compute multivariate computational statistics, and interpret findings.
• Learn how to critically evaluate data science model outputs.
• Integrate data science approaches with traditional quantitative and qualitative methods.

Level

SPECIALIST: Familiarity with traditional quantitative research methodologies such as surveys and experimental methods as well as some awareness of other data sources (e.g. customer data) will be helpful but is not a pre-requisite.

Testimonials

“Very inspirational, I enjoyed it very much!”

Joanna Targosz, Which?
14 November 2016

“A brief but comprehensive introduction to data science.”

Jennifer Bufton, Sport England
14 November 2016

“Brilliant overview of this evolving area.”

Tom Bowling, MMR Research Worldwide
14 November 2016

“A great insight into 'the future' with practical applications.”

Alish Palmos, Nuero-Insight
14 November 2016

“Fascinating, informative and eye-opening!”

Holly-Ella Coe, Mintel International Group
14 November 2016