The Most in Demand Skills for Data Scientists

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• Since 2013, the demand has gone up by 344%.
• If we talk about this increase in the past year, there was a 29% upsurge.
• The searches carried out by applicants for the position of data scientists has increased by only 14%.

Now, if you closely look at the given data, you’ll observe a difference in demand and supply. Although the demand is increasingly going up, the supply not so much.

In fact, Brandon Purcell, an analyst from Forrester Research believes that the demand for data scientists will only surge. Since organizations continuously need to analyze data for user personalization, large enterprises are looking forward to hiring talented professionals. With the advent of machine learning and artificial intelligence applications in almost every field imaginable, an incremental need for data scientists has been triggered.

Hence, in this article, we will discuss the most in-demand skills for a data scientist today. Let’s dive in and explore some general and technical skills.

General Skills

It is amazing how general skills often become as important as technical skills. Take communication for example. This one factor is essential in almost all the job roles today. Talking specifically about data scientists, these professionals should possess the capability of communicating the insights and actionable information.

Here are some general skills that data scientists should have:

1. Communication

Think about it – you need to analyze data, convert it into insights, and convey these findings to non-technical teams such as sales, etc. These teams then use this information for making enhanced business decisions.If a data scientist is not able to communicate the findings to a non-technical team in simple terms, then all this information would be worthless. Hence, communication is one of the most important general skills.

2. Critical Thinking

Being a data scientist, you should be able to understand business requirements in order to extract critical aspects necessary for providing a solution. The ability to analyze a situation from various point of views is the key to finding the right information from a given data set.

3. Risk Analysis Every

data scientist should understand the risk factors in a business so that they can finally mitigate these risks with their insights. It is necessary to align your process improvement efforts to eliminating company risks. The resulting model will lead to higher customer satisfaction along with advanced services.

3. Problem Solving

If you look at it, then data scientists in reality are problem solvers. They are supposed to pull the strings from all ends to know how the problem can be solved before actually pulling out the tools. However, still, many experts believe that though most job seekers have technical knowledge, they lack problem-solving skills. These professionals have a hard time explaining why a certain model worked. Developing a deep and intuitive understanding of data is the key to gaining problem-solving skills.

Technical Skills

The role of a data scientist is essential for organizations, which also indicates that these contributors need to have proper technical skills. Here are some common skills data scientists are expected to have:

1. R Programming

Did you know that almost half of the data scientists prefer utilizing R for statistical requirements?

R is the most preferred tool as it is specifically made for data scientists. It is deeply rooted in the statistical world, making R very popular and a desirable tool for every data person.

2. Python

Python is as essential as R. One survey revealed that 40% of data scientists use Python as the main programming language. Owing to its flexibility and versatile structure, Python can be utilized through and through in data science. Whether you want to import SQL or make datasets, everything is easily achieved.

3. Hadoop

While Hadoop is not the first requirement of every job role, it makes for a good selling point. When the data in your system is so much that you need to transfer it to different servers, Hadoop comes to your rescue. It further streamlines data filtration, exploration, summarization, and sampling.

In a survey including data scientists, Hadoop was ranked as the second most essential skill.

3. SQL

While NoSQL has gained popularity in data sciences recently, it won’t hurt to have knowledge of SQL. In fact, many organizations expect data scientists to understand complex SQL queries. Using this knowledge, you can transform database structures and perform analytical functions.

4. AI And Machine Learning

Artificial intelligence and machine learning technologies including deep learning, reinforcement learning, neural networks, etc. are the pillars of data science. Unfortunately, most data scientists are not well-versed in this area. Having knowledge of the same can instantly give you an edge. If you are working with huge sets of data, it is favorable to have knowledge of how to make future predictions.

Conclusion

Considering all the above skills and studies, it is imperative for individuals to figure out if they really need to enter the field. If so, invest time in both technical and general skills equally. All your knowledge would be for nothing if you don’t know how you are going to solve a problem or why a certain model can give you a solution.

Just learn to tell a story with data and everything will fall into place.