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10 Analytical Skills Necessary for a Successful Career in Data Science

The growing field of data science is becoming more competitive as talented professionals enter the industry in search of long-term, fulfilling careers. Because of this increased competition, it’s essential for candidates to distinguish themselves by completing the necessary training top employers are seeking. It also involves understanding the more nuanced attributes of a successful data scientist, which include the ability to think analytically.

What are analytical skills, and why are they essential for a career in data science? Let’s take a look.

What Are the Analytical Skills?

When employers want to hire professionals with strong analytical 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 these skills, which include the ability to observe and collect information and then use 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 data science is to improve processes in a way that enhances productivity and ultimately leads to greater success.

The analytical process looks something like this:

  • Identify the problem
  • Gather information from relevant sources
  • Develop a deeper understanding of the issue
  • Formulate new ideas and solutions
  • Implement solutions
  • Analyze, test, and review solutions for effectiveness
  • Use trial and error if necessary to develop additional solutions

We use analytical skills more often than we may realize, primarily because they also can be used in everyday applications. Whenever we observe, interpret, and formulate new ideas, we are using analytical skills.

Why Are Analytical Skills Important?

Many employers favor candidates who can demonstrate these skills because they are more likely to develop actionable solutions to common business issues. This is especially important in data science, which demands analytical thinking at multiple levels. A proven track record of success through analytical thinking can make candidates stand out from the pack.

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 impact of strong analytical skills in this field, take a look at the following key functions:

  • 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
  • Review multiple sets of data to determine which are the most impactful
  • Validate collected data for accuracy and uniformity to determine its value
  • Analyze data to solve problems and achieve objectives
  • Discover new solutions in collected data sets
  • Communicate insights and potential solutions to clients and stakeholders

10 Analytical Skills for a Data Science Career

While employers often look for hard skills relevant to data science roles—like a candidate’s education or hands-on experience—analytical skills are a bit different. They are more of a soft skill set because they are more intuitive and thus more difficult to demonstrate. Because of this, employers often ask candidates to complete core competency exams, which provide scenarios that allow them to demonstrate their analytical abilities.

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

Critical Thinking

Critical thinkers are capable of looking at a problem or scenario and formulating an understanding of how and why the problem exists. The ability to understand the full scope of the problem is key for developing effective solutions.


Data science involves analyzing and interpreting numbers. A curious nature and a desire to dig deeper to solve problems are the keys.


The ability to uncover data through reputable sources is vital, making strong research capabilities an important trait for data scientists.


Not all problems are straightforward, especially in the world of data science. Many challenges have multiple layers, each with their complex nature. 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 attention to detail to identify and use the most important data sets.


Data science is not only about finding solutions but also being able to see the bigger picture and look ahead to understand how a current issue may affect the company one, five, or even ten years from now. Data scientists can take today’s insights and project them into possible outcomes far into the future.

Ability to Communicate the Big Picture

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.


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.

Problem Solving

The ability to look at an issue from multiple angles to formulate solutions is vital for data science professionals.

Decision Making

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


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