Data science is a unification of statistics, mathematics, computer science, domain knowledge, information science and data analysis. It uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structural and unstructured data. 

Data science was originally included in my Multimedia Studies and Creative Technologies concentration, but I ultimately moved it over into Information Management and Career Development because I was drawing more of an information management angle from what I was learning.

Mining data, cleaning it and drawing conclusions from it can be a very daunting task because you can end up with lots of big data and there are ethical considerations as well because you might be handling someone’s personal data, so data science requires a great deal of information governance adherence. 

For me personally, data science is a hugely useful tool for discovering opportunities and improving your marketplace performance (as you do with Marketing Analytics), so I was also seeing data science as more of a career development tool.  

Ultimately, I consider my studies in data science to be open-ended because it is still a growing field and I can only really start to dig deeper into it when I have got substantialy pratical with exploiting it.

19 Courses

Introduction to Big Data

Platform: Coursera

Institution: University of California, San Diego

Started: 15/09/2015

Finished: 19/09/2015

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Data Science Foundations: Fundamentals

Platform: LinkedIn

Institution: LinkedIn Learning

Started: 08/02/2018

Finished: 15/02/2018

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Big Data for Better Performance

Platform: Open2Study

Institution: Open2Study

Started: 15/09/2014

Finished: 15/10/2014

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Basic Science: Understanding Numbers

Platform: FutureLearn

Institution: The Open University

Started: 17/08/2018

Finished: 20/08/2018

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Numeracy Skills for Employability and the Workplace

Platform: FutureLearn

Institution: Loughborough University

Started: 25/07/2015

Finished: 

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A Crash Course in Data Science

Platform: Coursera

Institution: John Hopkins University

Started: 06/09/2017

Finished: 06/09/2017

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Building a Data Science Team

Platform: Coursera

Institution: John Hopkins University

Started: 06/09/2017

Finished: 06/09/2017

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Managing Data Analysis

Platform: Coursera

Institution: John Hopkins University

Started: 06/09/2017

Finished: 06/09/2017

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Data Science in Real life

Platform: Coursera

Institution: John Hopkins University

Started: 06/09/2017

Finished: 06/09/2017

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Data Science and Analytics Career Paths and Certifications: First Steps

Platform: LinkedIn

Institution: LinkedIn Learning

Started: 07/02/2018

Finished: 07/02/2018

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Learning Data Science: Understanding the Basics

Platform: LinkedIn

Institution: LinkedIn Learning

Started: 15/03/2018

Finished: 15/03/2018

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Learning Data Science: Ask Great Questions

Platform: LinkedIn

Institution: LinkedIn Learning

Started: 24/03/2018

Finished: 25/03/2018

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Learning Data Science: Manage Your Team

Platform: LinkedIn

Institution: LinkedIn Learning

Started: 27/03/2018

Finished: 27/03/2018

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Learning Data Science: Using Agile Methodology

Platform: LinkedIn

Institution: LinkedIn Learning

Started: 01/04/2018

Finished: 01/04/2018

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Learning Data Science: Tell Stories With Data

Platform: LinkedIn

Institution: LinkedIn Learning

Started: 17/04/2018

Finished: 17/04/2018

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What is Data Science?

Platform: Coursera

Institution: IBM Big Data University

Started: 16/03/2019

Finished: 02/07/2019

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Open Source tools for Data Science

Platform: Coursera

Institution: IBM Big Data University

Started: 01/06/2019

Finished: 01/09/2020

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Data Science Methodology

Platform: Coursera

Institution: IBM Big Data University

Started: 29/06/2019

Finished: 01/09/2020

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Python for Data Science and AI

Platform: Coursera

Institution: IBM Big Data University

Started: 03/07/2019

Finished: 01/09/2020

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If ‘sexy’ means having rare qualities that are much in demand, data scientists are already there. They are difficult and expensive to hire and, given the very competitive market for their services, difficult to retain. There simply aren’t a lot of people with their combination of scientific background and computational and analytical skills.
Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies.
If companies sit out this trend’s early days for lack of talent, they risk falling behind as competitors and channel partners gain nearly unassailable advantages. Think of big data as an epic wave gathering now, starting to crest. If you want to catch it, you need people who can surf.

Thomas H. Davenport, Data Scientist: The Sexiest Job of the 21st Century, Harvard Business Review, hbr.org, October 2012