In the Part I, Part II and Part III of this post we discussed the importance of statistics and programming skills to be an analytics professional, but we also underpinned the fact that Analytics is not all about statistics only! We also discussed the importance of communication skills and how without communicating the results effectively, it’s difficult to derive value from the analysis. We then discussed the essential soft skills and how to develop them, particularly we discussed on Intellectual Curiosity, Business Acumen and Communication Skills.
In this concluding post on what it needs to be a data scientist & business analytics professional? we would discuss the required hard skills and how you could go about attaining the same.
We broadly categorize the hard skills into programming and machine learning methods. So rather than just the skills, readers may take it as models to know as well.
With the huge amount of data that needs to be analyzed, it’s practically impossible to work on it without the knowledge of statistical software’s. Though we have some applications that offer GUI like the SAS Enterprise Miner, but knowing to code gives you more power in your hands.
SAS has been the industry leader from a statistical software point of you, and is a widely used tool. So knowledge of SAS is a good asset for anyone aspiring to move into analytics field. R and Python are other powerful options, and more importantly they are open source applications so doesn’t cost anything to use them. Hence a combination of SAS + R or SAS + Python should arm an individual quite well from analytics point of view.
Another quite important and powerful tool is the SQL, as most data would be maintained in a database knowledge of SQL is needed to extract and play around with the same data. The statistical packages discussed above too offer ways to write SQL queries within them.
Individuals particularly interested in the big data may also need the knowledge of Hadoop platform, but we shall spare it for now.
Machine Learning Methods:
It’s difficult to put down everything over here; hence we would discuss the most commonly used methods across domains. We start with the knowledge of different regression methodologies particularly the multivariate linear regression and the logistic regression, to non-linear methods like decision trees, random forests, clustering and the artificial neural networks. From our experience, among st these you would see using clustering, decision trees and one of the most powerful modern machine learning methods, the random forests quite often. It is essential to have a deep understanding of these models and knowing on when to use which one and interpreting and communicating the results easily.
Developing these hard skills should be easy compared to developing the soft skills. There are plenty of options ranging from advanced degrees to certification courses by training institutes to massive open online courses (MOOCs). Our training page has listed down some of the interesting training programs with an Analytics Bodhi rating for each of these courses. Apart from these, our informative blogs and the “Learn the Tool” section would offer you a free resource for learning.
In this concluding article on key skills to become a data scientist & business analytics professional, we discussed the hard skills that you should equip with to enter into the analytics industry. We discussed about the importance of SAS, R, Python and SQL. We then discussed the important machine learning methods and some resources to learn them. From here, the journey is going to be an exciting one with many informative and interesting posts to come on several topics. We are as excited as you are about this journey.