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Introduction to Data Science for Digital Marketers

Published: 29 October, 2019

The role of data science in digital marketing: how do you transition from marketer to data scientist?

We are firmly set in a digital economy and wherever we look there’s more tech.  Marketing departments in financial institutions are buying into multiple systems that deliver a multitude of analytics.  But what do we do with all this data?

Most marketers are using rudimentary tools and skills to analyse and slice and dice data but there is a quiet revolution – the data scientist.

Enlightened asset managers and hedge funds are now looking to recruit these skills so how do marketers transition to fulfil this new requirement?

ProFundCom will be scheduling a series of training courses to introduce data science to digital marketers. This event is built for financial services marketing professionals who either want to work with the newly recruited data science teams and understand their terminology or, look at building your own skill set.

The topics covered will be:

Understanding Data Science

Plain and simple, first things first. You cannot become a data scientist unless you understand what data science really is, and an introductory course that provides you an overview of this discipline is the first step you should take. Core concepts include why and how data science is so important for business and how it can be applied. You must be able to understand what regression analysis is, and how the process of mining a data set works, as well as what tools and algorithms you are going to use on a daily basis to master this discipline.

Statistics and the Bayesian Approach

Some degree of knowledge in statistics is a necessity in data science. Although statistics is a really broad field, a data analyst requires a grasp of at least some concepts in statistics and probability theory to provide practical insights to businesses and organisations.

You need to combine theory with practice by learning core concepts such as distribution, hypothesis testing and regression, as well as the fundamental Bayesian probability theory. Most machine learning modules are, in fact, built on Bayesian probability models. The Bayesian approach is an intuitive one that moves from probability to the analysis of data and allows for better accounting of uncertainty as well as providing actionable statements of assumptions that can be used in practice.

Machine Learning

Machine learning is the science that allows computers to act outside the boundaries of the scripts they’re programmed to run. It’s a pervasive science that has a lot of applications in the real world, and data mining is one of them. But to approach machine learning you need to possess all the skills mentioned above. Machine learning algorithms need to be programmed with Python, and statistical approaches are the most effective ones to “teach” a machine how to become smarter.

The whole field of machine learning is extremely vast, and includes various subtopics such as supervised and unsupervised learning, model evaluation and deep learning. Although you do not necessarily need to dive as deep as learning how to program the most advanced neural networks, the more you know about the many applications of machine learning in data science, the better.

Please contact us to register your interest.

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