As an analyst, have you ever wondered ‘why do I need (or want) data science skills’?
Attendees of the Sept 25th, 2014 DAA South Central Ontario chapter meeting were intrigued and excited to learn how data science skills could elevate their analytics capabilities and career in a world still hyped up about ‘Big Data’ (sort of) and ‘Data Science’, which is ready to peak according to the Gartner Hype Scale.
There seems to be a lot of interest these days in ‘Data Scientists’, whose job it is to make sense of big data – which means they solve complex data problems through combining techniques from statistics, mathematics, and computer science. This could include aggregating and cleaning multiple sources of data, performing statistical regression analysis, building custom data visualization tools, and much more. But your job title doesn’t have to be ‘Data Scientist’ for you to benefit from some or all of these skills.
If you’re now thinking that learning data science sounds a little ‘crazy’, you’re not alone. The event’s theme was a panel discussion called ‘How to Add Data Science Skills to Your Analytics Arsenal Without Going Insane’, lead by Sharon Flynn (an experienced digital analytics practitioner at TVO, who is new to data science practice) and Christopher Berry (an experienced data scientist currently in residence at 500px).
Sharon and Chris didn’t waste any time, kicking the evening off by sharing their personal experiences that lead them down the path of data science, explaining how/why data science skills can elevate your performance, advance your career and yes, even make you popular in the office.
At this point you may still be thinking:
- “I only work with small data sets – not big data”
- “I’m already an experienced digital analyst and I’m a ‘wizard’ at Excel.”
- “Learning data science skills sounds complicated and will take up a lot of time that I do not have.”
When asked what led her to begin taking data science courses, Sharon revealed that she had become frustrated spending significant amounts of time wrestling with data in Excel and not attaining the answers to the questions she needed to answer.“I have learned that working with lots of little data sets leads to big data. I realized that I needed a proactive mindset.”
Sharon then discovered a brand new and exciting world – data science and vowed never to go back to Excel. She has completed 4 out of the 10 courses for the John Hopkins Data Science certificate, and as a result of doing exceptionally well, she is now a TA for the program. She is currently taking a break from the John Hopkins stream so that she can fortify her statistics capability (through Duke). She does have plans to finish the John Hopkins Data Science program.
While Sharon wishes she had taken these courses earlier in her career, it was the problems that she experienced that drove the need for her to seek out this new skill set. Had Sharon not experienced problems, she would not appreciate this new capability.
During the discussion, Chris expressed his gratitude for all the hype that big data brought, specifically awareness of data science, the need for new tools and how it opens doors for policy changes and new opportunities, “Now that more people know that there is a different level of insight, capability has to be there.”
Here are few reasons for arming your analytics toolbox with data science skills:
- To find better ways of bringing data together to solve your organization’s problems.
- Excel is a great tool… but there is a point where even power users cannot accomplish anything further with the data.
- Organizations are seeking data science tools and experts to drive them.
- Data science skills could open doors to new opportunities or help you to keep your current job.
- Analysts with multiple skill sets are valuable.
- To answer the questions you are asked, not just the questions you have.
- Remember that data is a tool to help decision-making.
- By focusing on finding the right moments where data can answer questions you will create value for decision-makers (and raise their interest in you).
So how do you begin to figure out which data science tools to start with?
- Determine the key problems, inefficiencies and questions that seem to require a lot of data processing to answer.
- Do an inventory of the data that you have.
- Take an honest inventory of your skills gap.
- Figure out the problems in your workplace where data science skills/tools can help.
- Do not be afraid to loop in your manager – they can provide useful support
- Ask ‘why does the data not come together?’.
- Assess if there a tool that can bring them together.
- Review the courses available and find a starting point.
Specific courses and additional resources discussed and recommend by the panel included:
- Coursera Johns Hopkins Data Scientist’s Toolbox
- Coursera Johns Hopkins R Programming Course
- Coursera Duke University Statistics Course
- Download R: http://cran.r-project.org/
- R integration with Google Analytics:
- R integration with SiteCatalyst: RSiteCatalyst
- R integration with data visualization: sjPlot
Feedback from the panel on the John Hopkins Data Science Specialization courses, were that they are digestible, structured to move you forward and that you progress quickly at the basics.
While Coursera courses are free to take, you can earn a Specialization Certificate in Data Science by completing all 9 John Hopkins University Data Science courses and the Capstone Project for $49 per course and $49 for the project:
We hope we have sparked your interest in data science skills and more importantly, your curiosity about how data science skills can help you and your organization better use data for decision-making. We encourage you to take a look at your current problems and the tools you’ve been using to address them to determine if there is a better way to deliver real value to your organization.
We’d love to hear what you think… and if you have taken any of the course, please share your experience!