The 12 Analytical Skills Necessary for a Successful Career in Data Science

Although the field of data science continues to grow and provides many lucrative job opportunities, it’s also becoming increasingly competitive as more talented professionals enter the industry in search of long-term careers. Because of this increased competition, it’s critical for candidates to distinguish themselves by completing the necessary training top employers want in their new hires. It also involves would-be candidates understanding the more nuanced attributes of a successful data scientist, such as thinking analytically. 

This competitive environment leads us to ask, what are the ideal data scientist analytical skills, and why are they essential for a career in data science? Let’s explore this topic in-depth. 

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What Are the Relevant Analytical Skills? 

When employers want to hire professionals with strong data analytics skills, they look for candidates who can look at complex problems from multiple angles and formulate the most effective and efficient solutions through observation, logic, and research. While they are not innate, analytical skills can be taught and nurtured over time, often through the simple process of trial and error.

Anyone can learn data analytics skills, including observing and collecting information and then using that information and gleaned insights to deeply analyze an issue and develop a critical-thinking process that leads to innovative solutions. The overall goal of analytical thinking in the context of data science is to improve processes in a way that enhances productivity and ultimately leads to more tremendous success. 

Here is one way to approach the analytical process:

  1. Identify the problem.
  2. Gather the necessary information from all relevant sources.
  3. Develop a deeper understanding of the issue.
  4. Come up with new ideas and solutions.
  5. Implement new solutions.
  6. Analyze, test, and review the effectiveness of the newly implemented solutions.
  7. Based on the results, use trial and error to develop any additional necessary solutions. 

Here is another data analysis process with fewer steps but embracing much of the same philosophy:

  1.   Define your questions.
  2.   Set up clear measurement priorities, breaking them down into what must be measured and how to measure it.
  3.   Collect new data.
  4.   Analyze new data.
  5.   Interpret the results of the new data.

We use analytical skills more often than we may realize and most often don’t even give it a second thought. That’s because professionals can use these skills in everyday applications, not just data science. Whenever we observe, interpret, and formulate new ideas, we are using analytical skills. 

Why Are Analytical Skills Important? 

Many employers prefer candidates who can demonstrate these skills because they are more likely to develop informed, actionable solutions to everyday business issues. This characteristic is especially important in data science, a field that demands analytical thinking at multiple levels. Candidates with a proven track record of success through analytical thinking can make them stand out from the rest of the pack.

What Does Future Data Scientist Demand Look Like?

According to a Business-Higher Education Forum report, in conjunction with PricewaterhouseCoopers (PwC), almost 70 percent of business leaders in the United States in 2021 will choose applicants with data skills.

There will be a continually growing demand for data scientists and data engineers across a wide range of industries in 2021. We’re living in the data age, so aspiring professionals who want a secure career should set their sights on data science.

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How Analytical Skills Impact Your Data Science Career 

A data scientist's primary role involves collecting, structuring, and organizing data for implementation. To better understand the demand and the impact of strong data analytics skills in this field, let’s review some of the critical functions of a data scientist:

  • Collect large sets of data, both structured and unstructured, from multiple sources.
  • Assess data and analytically based problems to determine the best outcomes for clients and stakeholders.
  • Review multiple sets of data to determine which ones will make the most impact.
  • Validate the collected data for accuracy and uniformity to determine its value.
  • Analyze collected data for the purposes of problem-solving and achieving objectives.
  • Discover new solutions within the collected data sets.
  • Communicate insights and potential solutions to clients and stakeholders.

12 Analytical Skills for a Data Science Career

While employers often look for hard skills relevant to data science roles, such as a candidate's education or hands-on experience, analytical skills are a bit less tangible. Analytical skills are more of a soft skill set because they are more intuitive and more difficult to demonstrate or quantify. Because of analytical skills' nebulous nature, employers often ask candidates to complete core competency exams, which provide scenarios that let them demonstrate their analytical abilities.

To better understand what these employers are looking for when considering candidates, here is a list of a dozen of the most important data analytics skills for any data science job.

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Critical Thinking

Critical thinkers can look at a problem or scenario and formulate an understanding of how and why the problem exists. The ability to understand the full scope of the problem is the first important step towards developing effective solutions.

Curiosity

Why are things the way they are? What makes things tick? A curious nature and a desire to dig deeper to solve problems are essential tools for a data analyst. Data science involves analyzing and interpreting numbers, and an active curiosity goes a long way in this field.

Research

You can’t make sound data-based decisions until you actually find the data. Thus, the ability to uncover data through reputable sources is vital, making strong research capabilities an essential trait for data scientists.

Tenacity

Not all problems are straightforward, especially in the world of data science. Many challenges have multiple layers, each with its complex nature. Sometimes, this complexity and lack of a simple linear track can cause people to give up. Strong analytical skills mean seeing beyond one issue at a time to develop a more cohesive view of a problem's complexity.

Attention to Detail

When handling large sets of data, it’s easy to overlook details that might be essential to a project's outcome. Successful data scientists analyze mountains of information and use their ability to pay attention to detail to identify and use the most important data sets.

Foresight

Data science is about finding solutions and seeing the bigger picture and looking ahead to understand how a current issue may affect the company one, five, or even 10 years from now. Data scientists can take today’s insights and project them into possible outcomes far into the future.

Good Communication

Effective communication is a fine art, and it’s one that successful data scientists refine throughout their careers. Because they often communicate essential information to executives and stakeholders, they must be confident about the data they present and relay it in terms that the layman can understand. You can create the best actionable solutions anyone has ever seen in the history of IT. Still, unless you can relay that information to the people in charge, those solutions won’t go anywhere.

The Ability to Communicate the Big Picture

Call this one a subset of “good communication.” Data scientists have the unique ability to take information that might seem irrelevant and spin it into a story that communicates how it will make an overall impact, even in areas where one would typically not think of.

Problem Solving

An expert data sciences professional can look at an issue from multiple angles in order to formulate the best solution.

Decision Making

Data scientists make decisions every day, many of them instantly. This requires robust and confident decision making capabilities.

Business Sense

Data scientists use their acquired information to create actionable solutions for businesses. Therefore, they need to understand the nature of the field and specific business they’re working in to come up with solutions that fit best.

Writing Efficient, Maintainable Code

Data scientists work with data, which means they work with systems that process and analyze data. A good data scientist needs to understand how to use programming languages so they can create algorithms or programs to parse data and APIs to collect and prepare data.

Getting Acquainted with Data Science 

A career in data science begins with introductory courses that provide the initial necessary knowledge and insight. This introduction includes both technical training and education to enhance data analytics skills. This Data Analytics for beginners from Simplilearn is the ideal first step for those interested in entering the field, regardless of education and experience. The introductory course offers insights into the application of analytics in a range of businesses and an opportunity to improve data analytics skills through hands-on applications.

Are you looking forward to build a career in analytics? Then get certified with the Data Science Bootcamp Program. Click to enroll now!

Do You Want to Become a Data Scientist?

Are you interested in launching your career in data science? Enroll in Simplilearn’s Data Scientist Course or the Data Analyst Course today to take your skills to the next level. Co-developed with IBM, these programs enable you to learn everything you need to know to get certified in one of the hottest careers of the 21st century.

Don’t delay; visit Simplilearn today, and get started on a new, challenging, and rewarding career!

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