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Two analysts from LinkedIn coined the term ‘data scientist’ in the year 2008. They were just trying to describe what they do, i.e. derive business value from the massive data generated by their website. In the process, they ended up naming the job title that would see incredible demand in the years to come and even be termed as ‘Sexiest job of the 21st century.’
Now, organizations that consider ‘data’ as a valuable asset are looking for these data experts or ‘scientists’ to lead them into the future.
So, what does it take to be a great data scientist?………A variety of skill sets!
Brief look at the core skills of a data scientist.
The process of data science includes 3 stages.
Let us take a closer look at the role of a data scientist in each of these stages.
The first step of data mining is to capture the right data. So, to be a data scientist, it is very essential to be familiar with tools and technologies, especially the open source ones like Hadoop, Java, Python, C++, and database technologies like SQL, NoSQL, HBase and so on.
Data differs according to the business. Therefore, understanding the business data needs expertise, which comes only by working in a particular data domain.
For example: Data gathered from the medical field will be entirely different from the data of a retail clothing store.
Organizations are gathering enormous amount of data through various resources. The data captured in this fashion is unstructured and needs to be organized before analysis. Therefore, a data scientist has to be proficient in modeling the unstructured data.
The essential skill of a data scientist is to know how to use the statistical tools like R, Excel, SAS and so on. These tools are required to grind the captured data and analyze it.
Computer science knowledge alone is not sufficient to be a data scientist. The data scientist profile requires someone who can understand large-scale machine learning algorithms and programming, while being a proficient statistician. This needs expertise in other scientific and mathematical disciplines apart from computer languages.
You may be able to mine and model the gathered data, but are you able to visualize it?
If you want to be a successful data scientist, you should be able to work with some data visualization tools to represent data analyses visually. Some of these include R, Flare, HighCharts, AmCharts, D3.js, Processing, and Google Visualization API etc.
But this is not the end! If you are really keen to become a data scientist, you should also have the following skills:
Looking at the above skill sets it is clear that being a Data Scientist is not just about knowing everything about data. It is a job profile with an amalgamation of data skills, math skills, business skills and communication skills. With all these skills together, a Data Scientist can be rightfully called as the Rock star of the IT field.
Check list to become an awesome and efficient data scientist:
We covered the skills that is required to become a data scientist. There is a huge difference to just becoming a data scientist and become an awesome and efficient data scientist. The following skills along with the above mentioned skills, sets you apart from being a normal or even a mediocre data scientist.
Edureka has a specially curated Data Science course which helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes. You’ll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning as well. New batches for this course are starting soon!!