In a previous post (see it here) I mentioned that evaluation is not a framework problem, rather it is a data problem. I ended that post with a question, does learning and development need data science? If I had answered that question closer to the time I wrote the post, my answer would have been no, but my confident answer to that question is yes. I said yes because I now have a better understanding of what data science is.
At the moment there is a lot of hype about data science in the press and public. Apart from the fact that it is being touted as the 'sexiest job of the 21st century' (which i don't agree with anyway), what you read about data science tends to be linked with big data, machine learning and predictive analysis. But data science is much more than that. It is a very huge field with various applications and big data, machine learning and predictive analysis are just subsets of data science which may or may not be needed in certain domains.
A good question to answer at this point is, what is data science? There are myriads of definitions but my answer is, it is the science of using data to ask and answer questions and also devise solutions. Data science is different from traditional business analytics in that scientific approaches such as experimentation, more in depth statistical and mathematical analysis may be used to work with data.
Generally data science follows a process which includes asking a question > obtaining supporting data > preparing the data (cleaning and putting it in a form ready for analysis) > exploring the data (commonly known as exploratory data analysis for the purpose of understanding the data) > analysing the data > arriving at conclusions > communicating insights from the conclusions.. Also data science is said to consist of three main domains which are:
- understanding of statistical and mathematical models which can be used to work with data.
- computing and programming knowledge such as the use of tools like R, Python, Tableau, Hadoop, Spark, SQL and even Excel.
- domain knowledge for example learning and development or broader human resources
Now how can all this be applied to learning and development? I will answer that question in my next post on this topic.
3 Responses
Hello bolaowoade!
Hello bolaowoade!
I am very curious about this topic as I study a combination of both, recently consulted a company and am currently in search for an internship in that area..
Hi Mariowille,
Hi Mariowille,
Sorry for my really late reply. I stoped blogging on Traininzone for a while. I am interested in knowing how you are studying for it. Are you doing a certificated programme or just self-study? And how are you combining it with learning and development?
This is a reply two years
This is a reply two years later, but only because i missed your reply and I do apologise. I am not studying to be a full blown data scientist as i don’t believe you need that for L&D. What i do mostly is take courses from MOOC platforms such as Edx, FutureLearn and Coursera and also read books. My aim is to be able to to identify what data is needed for any learning and development intervention and then be able to analyse it to arrive at the necessary conclusions. For example if the goal is to evaluate an intervention, then i would first want to know what the performance objectives are and what data needs to be collected to measure the performance objectives and how to collect the data. Then we can go ahead and analyse the data to see if the intervention met the performance objectives or not.