Core Data Scientist Skills

 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 21^st 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.

• Data Capture
• Data Analysis
• Presentation

Let us take a closer look at the role of a data scientist in each of these stages.

Data Capture

• Programming and Database Skills

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.

• Business Domain and Expertise

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.

• Data Modeling, Warehouse and Unstructured Data Skills

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.

Data Analysis

• Statistical Tool Skills

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.

• Math Skills

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.

Presentation

• Visualization Tool Skills

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:

• Communication Skills:  Statistics and Excel are the tricky ones to deal with. Data Scientists should be able to present the data in a way that it communicates the results to the business users.

• Business Skills: Data scientists will have to play multiple roles. They would need to communicate with diverse people in the organization. Therefore, having strong business skills that include communication, planning, organizing and managing will be of great help. This includes understanding business and application requirements and interpreting the information accordingly.  Also, he should have an overall understanding of the key challenges in the industry and should be aware of the financial ratios for better decision making. Bottom line, a data scientist as to think ‘Business’ as well.

• Problem solving skills: This seems obvious as data science is all about problem solving. An efficient data scientist must take time and look into the problem deeply and come up with a feasible solution to suit the user.

• Prediction Skills:  A data scientist should also be an efficient predictor. He should have broad knowledge of algorithms to select the right one to properly fit the data model. This involves certain amount of creativity to use and represent the data wisely.

• Hacking: I know it sounds scary, but different hacking skills like manipulating text files at the command line, understanding vectorized operations and algorithmic thinking will make you a better data scientist.

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.

• Mathematical skills – Calculas, Matrix operations, Numerical optimization, stochastic methods, etc.
• Statistic skills – Regression models, tress, classifications, diagnostics, applied Statistics, etc.
• Communication – Visualization, presentation and writing.
• Database – Besides CouchDB, knowledge in non-traditional databases like MongoDB and Vertica.
• Programming languages – Pig, Hive, Java, Python, etc.
• Natural language processing and Data Mining.

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!!