Introduction to Big Data and Analytics in Marketing

This introductory level online course will provide participants with a basic understanding of the use of big data and analytics when applied to the marketing domain. An emphasis on industry examples will allow learners to engage with contemporary best practice. Participants will be introduced to professional practice concepts such as customer analytics, which allows for a more efficient targeting of different types of existing and potential customers (‘micro segmentation’). Topics covered will include automated or optimized pricing based on machine learning, and the use of artificial intelligence in digital social media.

The course will be taught through online sessions consisting of lectures and practical workshops. In addition to attending weekly teaching sessions, learners will be expected to engage with supplementary materials such as video recordings, audio podcasts and introductory readings.

This course will be taught by Dr Sukhpreet Singh who has more than 20 years of experience in management research and teaching. He holds Teaching Fellowships at the University of Edinburgh and Lancaster University Management School while serving as external advisor to several UK universities. Dr Singh also holds a judicial office as a Tribunal Member at the Employment Tribunals Scotland. For full profile information, see here.

This course is open to anyone looking for a basic understanding of the use of big data and analytics when applied to the marketing domain. It is of an introductory nature and introduces approaches to big data analytics and how these are manifested in marketing practice - this course does not teach students how to code. No prior University experience is required to take this course.

1. Demonstrate a critical understanding of big data and its uses in marketing;
2. Identify and apply knowledge of analytics to practical contexts within marketing;
3. Reflect on current professional practice in this area.

This is an 8 week course, comprising 2 hours contact time per week - weekly teaching sessions Tuesdays from 18.00-20.00.

Please note that all sessions will be live synchronous using the Collaborate platform within the University's VLE (Learn). ​​​​​Sessions are not recorded.

40 learning hours (16 contact teaching hours, 24 hours of independent study).

Course fees for 23/24 are £350 but funded places are available for people employed or unemployed in Scotland (residency requirements apply).

Funding

Through the Scottish Funding Council (SFC) Upskilling Fund, a limited number of fully-funded places are available on Data Upskilling Short Courses at The University of Edinburgh.

Eligibility

Funded places are available to those who meet SFC fee waiver criteria:

“Courses/provision is open to all Scottish-domiciled/’home fee’ students, which is consistent with SFC’s policy for core funded student places. Students from the rest of the UK (rUK) are not normally considered eligible for SFC funding. If however a university is working with a Scottish/UK employer which has a physical presence or operating in Scotland, rUK employees of that employer would be eligible.”

If you are from outside Scotland, you need to have settled status in the UK and meet other residency criteria:

• be ordinarily resident in the United Kingdom, the Channel Islands or the Isle of Man for the three years immediately before course start date, and
• have ‘settled status’ in the UK (as set out in the Immigration Act 1971) at the course start date, and
• be ordinarily resident in Scotland at the course start date.
• You can find out more about residency criteria on the SAAS website or in this summary.

Funding eligibility will be assessed at the point of each application for each course; you may be asked to provide further information if you do not meet the general residence conditions. You can check your likely fee status here: https://www.ed.ac.uk/tuition-fees/fee-status/work-out Please email us at upskilling@ed.ac.uk if you would like to discuss your funding eligibility before applying.

Please note that full-time students (including full-time PhD students) are not eligible for funding.

You will receive a certificate of completion.