5 Major Skills under a Data Scientist’s Belt
Nothing gets better than a data scientist when it comes to technical jobs. It is like the icing on the top of the cream. No wonder you want to be a part of it but what does it take? And what are the stakes? Well, before we go on pondering the skill sets and job roles, let us just be on the same page regarding our understanding of a data scientist.
Data scientist is a loaded compound, it contains the word scientist in it and it is not a word to be flung about at will. A lot of work and plenty of years go into it. Not every job title saying a data scientist is actually referring to data scientist in the truest sense of the world. So, we will not be that strict either. However we will treat this as a process of becoming rather than already being a data scientist. Getting on the points:
Statistics has got to be your forte
Being a data scientist definitely involves a statistical approach to data. A lot of successful data scientists are in fact students of statistics.
There are certain statistical skills which are exceptionally useful for a data science professionals.
Some crucial methods you need to know are statistical modelling, Bayesian inference, logistic regression, decision tree, clustering, experiment design, etc.
If you already know these from academic experience you have an edge; if you do not then a comprehensive data science course can fix it for you.
Data science, like any discipline of work, has evolved and through its evolution it has necessitated the acquirement of machine learning know how for the data science professional. It is required to automate certain processes. The models can help a data scientist to ceive through large amounts of data with increased precision and accuracy.
It can come really handy when you are attempting a predictive or prescriptive analysis of available data. Or when you are choosing the right kind of data from a humongous data influx.
Knowing the codes
That is to say the programming languages are a vital part of a data scientist’s game. Your prowess over R, Python or some other relevant language can make a lot of difference. This can be useful in building analytical models to designing machine learning algorithms.
Learn the languages that have a lot of promise for the future, it will go a long way for you.
This might sound less important, but it is not. It means the representation of your research in a telling manner, so as to drive the viewer to action.
Data visualization can be learnt separately or it can be part of some data science courses. The result of your work is dependent on data visualization.
Problem solving skills
This is rather abstract in nature and one of the most important parts of a data scientist’s job role.
Identifying problems and assessing the probable solutions to it is all a data scientist does. You need to have terrific domain knowledge and a strong business acumen to translate your skills in real business. This skill develops with your experience and through case studies and practice.
Data science is a multidisciplinary field, there is more to it than what is addressed here. But these can give you the much needed groundwork.