Want to learn the basics of large-scale data processing? Need to make predictive models but don’t know the right tools? This course will introduce you to open source tools you can use for parallel, distributed and scalable machine learning.
Learn how you can predict customer demand and preferences by using the data that is all around you. In a digital world, data has gone ‘big’ – ushering in the age of the zettabyte. This subject shows you how big data equals business opportunity. Find out what ‘big data’ means and where it comes from – including ordinary transactions and social interactions. See how smart businesses use data to target their offerings and get ahead of market trends. Consider how marketing data can be based on false assumptions such as the ‘last click myth’.
Consider the promises and threats of big data for organisations and individuals, such as the capacity of data to track a customer along the pathway to purchase; and the issues of democracy and privacy that arise when data is collected and used.
What will you learn?
- Define big data and outline ways in which it is remapping the future of marketing:
- Define the measurement units of big data
- Recognise different types of data
- Provide examples of where big data is created
- Identify the basic attributes of big data:
- Categorise data according to its level of refinement
- Provide examples of data analytics that achieve refinement
- Outline positive and negative social impacts of data proliferation
- Outline business challenges and opportunities in managing and using big data:
- Distinguish between brand-centric and customer-centric uses of data
- Identify the key stakeholders within organisations in data management
- Provide examples of targeted data acquisition for marketing benefit
- Outline ways in which effective marketing can exploit big data
- Define media attribution and outline its importance to marketing strategy
- List some common tools in the marketing toolkit, and outline their purposes
- Provide examples of marketing strategies that can capture trackable data in order to improve the quality of attribution.