5 Must-Have Skills You Need to Become a Data Scientist
It’s not enough to say that today’s age is that of technology. With the advent of technology what we now have access to is data. Hence its right to say that we live in a data age. Along with scientists that invent technology, there is a dire need for those who can mine the data and discover insights from it. Whether its Machine Learning, Internet of Things, Deep Learning, Big Data or traditional large-scale data analysis, there is always a growing demand for those who understand the “analytics of things” in the data realm. Let us take a look at the key skills needed for being a data scientist –
1) Understanding of Statistics
Hypothesis Testing, Probability, Descriptive and Inferential Statistics are the basic building blocks for data science. What is needed is to have an intuitive understanding of business statistics. This entails interpreting statistical output in business friendly context. Can you explain significance using a p-value to a layman? Can you explain central limit theorem to someone is completely new to statistics? It’s not so much about being a statistician. It’s about using the basics of statistics as a foundation to business analytics.
2) Statistical Programming
When I started working in Analytics many years ago, the most well known and used Statistical language was SAS. But as it is with technology, we can no longer say that we are good only in one language. More and more recruiters of data scientists are looking at candidates who are conversant with at least SAS and R. It is a great bonus if you know Python as well.
At a beginner level, a good understanding of these tools is a must. Companies do not want tools to be a barrier for building analytical solutions. It’s not so much about being a programmer but it’s about being comfortable with various programming environments to be able to slice and dice the data as and when required. If one can demonstrate the ability to adapt to the growing changes in technology by being able to learn new statistical languages, it surely will be an advantage to those seeking data scientist opportunities.
3) Statistical Techniques and Algorithms
An exposure to algorithms like Linear Regression, Logistic Regression, Time Series Forecasting, Clustering, Decision Tree and some understanding of Neural Networks, Machine Learning, and their Business Application would be helpful. It’s an advantage to have hands on exposure on at least few of the statistical techniques and a good understanding on the current happenings in the analytics industry such as deep learning or NLP.
4) Business Knowledge
This is not so critical as a beginner, but the more experienced you become in the analytics world, being a domain expert will add value. It’s worth investing time to understand various norms, trends, terminologies in the domain of your interest.
If you like to be a distinguished data scientist, being a good communicator is of paramount importance. To be able to clearly and concisely present analytical solutions, translate statistical output into actionable recommendations, to be able to manage team perception, communication skills are a must!
Most importantly – Stay Current! Watch the next wave and keep yourself updated with the latest through online courses, sites like datasciencecentral.com, latest news, articles, and blogs. What is the future of analytics and where do you fit in? How can you grow in the world where there is so much to offer to a data scientist? Start thinking now!