Is the idea of being a data scientist sounds appealing to you? It is absolutely perfect if it does. There is a reason why “Data Scientist” is being referred to as the sexiest job of the 21st century.
The important question though is how to become one? The information collected from different channels and resources suggest that it requires comprehensive knowledge of a number of fields like software development, data mining, databases, statistics, machine learning and data visualization to become a successful data scientist. But this shouldn’t make you worry at all.
I, personally, think that it isn’t necessary to learn too much too soon. Prima facie you should develop the understanding of how to read data science job descriptions. This enables you to apply for the jobs best-suited according to your skills and get the ball rolling. From there you can work towards acquiring specific data skill sets and attain the desired job.
There are certain imperatives which you should take into consideration before getting into the data science profession.
Irrespective of the industry you’re interviewing there are certain fundamental tools which you would be expected to have knowledge of. These would entail having the knowledge of programming languages like R or Python and DB querying language like SQL.
Knowledge of Statistics
It is quite important for an aspiring data scientist to have an understanding of the basic concepts of statistics. Sometimes it has been observed that over-enthusiastic aspirants have failed to provide definitions of simple functions like p-value. This is not a favorable situation for someone who wishes to be a data scientist. An understanding of statistical tests, distributions, maximum likelihood estimators etc. is considered important.
Not only does one needs to know about the concepts but should also have the wisdom to apply the right concepts at the right time. When it comes to data science, statistics become highly important. More so in the case of companies whose products are not data-driven; in such cases, stakeholders look for support from you to help them in making decisions and designing experiments.
Machine Learning becomes imperative in case you work with huge chunks of data or your company has a data-driven product. The requirement would be to get acquainted with the machine learning lingo like k-nearest neighbors, random forests and ensemble methods. The implementation of these techniques is usually done with the help of programming languages like R or Python which simply means that you don’t necessarily need to understand the algorithms. Again the emphasis is on using the right technique at the right time.
Data Visualization & Communication
Visualizing and communicating data are vital for new enterprises where decisions driven by data are the new thing. This activity also holds a high relevance in companies where data scientists are required to assist others in making data-driven decisions.
When it comes to data – communication means sharing your findings and working with techniques with technical as well as the non-technical audience. When trying to get acquainted with visualization tools like ggplot or d3.js. visualization is of a lot of help. It is not just about, knowing the necessary tools, but also the logic behind the principles of visual encoding of data and communicating information.
If you choose to join a small company where you are one of the first data scientists then it is imperative that you come from a strong engineering background. In such organizations, it will be considered your duty to take care of data logging and also handle the potential developments of data-driven products.
Think Like a Data Scientist
Organizations are always on a lookout for data-driven professionals. This is why when someone appears for an interview for a data scientist’s role they must be certain about facing a question based on a high-level problem. This is where the select and eliminate approach comes to the rescue and you can easily identify the right way of tackling the issue.
There are certain standard questions to which you must have answers to. These can be – How to talk to engineers or product managers? Which methods would you use? When do approximations make sense? If you know the answers to these then half the battle are won.
Though data science is a relatively newer field it still has a lot to offer and professionals who are skilled will have numerous opportunities awaiting them. These six are some of the primary skills that one must know or acquire before stepping into this field. Cognixia offers a great training program in Data Science which educates and prepares you for the real-time jobs in this field. For further information, you can write to us.