Information Management and Career Development
Module 7
Data Science
20 Courses
Introduction to Big Data Platform: Coursera Institution: University of California, San Diego Started: 15/09/2015 Finished: 19/09/2015 Data Science Foundations: Fundamentals Platform: LinkedIn Institution: LinkedIn Learning Started: 08/02/2018 Finished: 15/02/2018 Go to course on Class Central Big Data for Better Performance Platform: Open2Study Institution: Open2Study Started: 15/09/2014 Finished: 15/10/2014 Basic Science: Understanding Numbers Platform: FutureLearn Institution: The Open University Started: 17/08/2018 Finished: 20/08/2018 Numeracy Skills for Employability and the Workplace Platform: FutureLearn Institution: Loughborough University Started: 25/07/2015 Finished: A Crash Course in Data Science Platform: Coursera Institution: John Hopkins University Started: 06/09/2017 Finished: 06/09/2017 Building a Data Science Team Platform: Coursera Institution: John Hopkins University Started: 06/09/2017 Finished: 06/09/2017 Managing Data Analysis Platform: Coursera Institution: John Hopkins University Started: 06/09/2017 Finished: 06/09/2017 Data Science in Real life Platform: Coursera Institution: John Hopkins University Started: 06/09/2017 Finished: 06/09/2017 Data Science and Analytics Career Paths and Certifications: First Steps Platform: LinkedIn Institution: LinkedIn Learning Started: 07/02/2018 Finished: 07/02/2018 Learning Data Science: Understanding the Basics Platform: LinkedIn Institution: LinkedIn Learning Started: 15/03/2018 Finished: 15/03/2018 Learning Data Science: Ask Great Questions Platform: LinkedIn Institution: LinkedIn Learning Started: 24/03/2018 Finished: 25/03/2018 Learning Data Science: Manage Your Team Platform: LinkedIn Institution: LinkedIn Learning Started: 27/03/2018 Finished: 27/03/2018 Learning Data Science: Using Agile Methodology Platform: LinkedIn Institution: LinkedIn Learning Started: 01/04/2018 Finished: 01/04/2018 Learning Data Science: Tell Stories With Data Platform: LinkedIn Institution: LinkedIn Learning Started: 17/04/2018 Finished: 17/04/2018 What is Data Science? Platform: Coursera Institution: IBM Big Data University Started: 16/03/2019 Finished: 02/07/2019 Open Source tools for Data Science Platform: Coursera Institution: IBM Big Data University Started: 01/06/2019 Finished: 01/09/2020 Data Science Methodology Platform: Coursera Institution: IBM Big Data University Started: 29/06/2019 Finished: 01/09/2020 Python for Data Science and AI Platform: Coursera Institution: IBM Big Data University Started: 03/07/2019 Finished: 01/09/2020 Data Scientist Career Path Platform: Codecademy Institution: Codecademy Started: 19/10/2021 Finished:
1 Book
I Used to Know That: Maths Author: Chris Waring Publisher: Michael O’Mara Books Limited Published: 2014 Started: 03/07/2019 Finished: 09/06/2021
“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
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.