Top Analytical Skills Necessary For A Successful Career In Data Science 2021


Data finds its importance everywhere, and data analytics is used in every sector. In every organization, meaningful insights are drawn pertinent to the growth of the company. Analytical skills are required to collect, process, and understand the trends & insights hidden in massive data sets.  

  1. What Are the Relevant Analytical Skills?
  2. Why Are Analytical Skills Important?
  3. How Analytical Skills Impact Your Data Science Career
  4. Analytical Skills for a Data Science Career

1.What Are the Relevant Analytical Skills?

Let’s understand what are analytical skills. They are described as the ability to gather and analyze data, solve the problem and make decisions. Analytical thinking is a combination of logical and creative thinking. The 3 main components of analytical thinking are logical reasoning, decision making, and problem-solving. The steps involved in an analytical approach are 

  • Identify the issue or the problem.
  • Break down the problems into sub-parts or key components
  • Organize the data related to the problem
  • Recognize the cause and effect of the underlying issue.
  • Integrate all the pieces 
  • Analyze the complexity of the problem
  • Draw proper conclusion

Some of the analytical skills required to follow the above approach are: 

  • Communication: This involves listening, surveying, reporting, and presentation skills.
  • Creativity: An analytical person needs to be able to brainstorm effectively, structure the thoughts in the right sequence, collaborate with the right set of people, and strategize or plan to implement ideas.
  • Critical Thinking: Various aspects of critical thinking are process management, auditing, improvising, computing, classifying, and calculating.
  • Data Analysis: This includes SWOT analysis, cost-benefit analysis, policy analysis.
  • Research: It is the main domain to design new solutions. It includes data collection, investigation, metrics, and data entry.            

2.Why Are Analytical Skills Important?

Analytical skills are important: 

  • To articulate and visualize the problem that needs resolution.   
  • To gather the information that is relevant to the problem.
  • To explore facts and all the possible options systematically.
  • To find a suitable solution that is sustainable and affordable. 

3.How Analytical Skills Impact Your Data Science Career

The primary responsibility of a data scientist is to capture, structure, and organize data. Strong analytical skills help in 

  • Strategizing and planning for the future: Data Science, Machine learning, and artificial intelligence are essentially preparing our workforce to be future-ready.
  • Deciphering the facts and figures: There is a lot of information available these days, analytical ability help convert that information into knowledge. 
  • Addressing the problem efficiently: Analytical skills help to be productive, achieve maximum output with minimum input, and synchronize the output with the objective.
  • Innovating effective solutions: Analytical thinking also helps one think out of the box, develop new solutions, and communicate the same effectively to the stakeholders. 

4.Analytical Skills for a Data Science Career

In addition to the technical skills like a strong foundation in mathematics, statistics, fundamentals of programming language, another important skill for a data scientist is to be able to analyze data. It takes a strong analytical ability to make sense of the vast amounts of data, see trends and present it in a meaningful form. For a data science career, here is a list of the most relevant analytical skills:

  • Balanced decision making: A data scientist needs to make sound data-driven choices after studying the options and weighing the pros and cons.
  • Critical and creative thinking: This is the ability to ask the right questions, conceptualize the project, and then come up with innovative solutions or a meaningful outcome. 
  • Theoretical and practical problem solving: An expert in data sciences may examine a problem from various perspectives to come up with the best solution. Understand the techniques to organize and structure data to provide efficient and reliable results.
  • Intuitiveness: The ability to explore the data, see cause and consequences, look at alternate possibilities may add value and help build better models.
  • Industry knowledge and business sense: It is important to have business acumen. To channel their technical skills productively, data analysts need to have a know-how of the elements that make up a successful business model. To discern the problems and the potential challenges that need solving for the business to sustain.
  • Research: Competent research skills are a necessary quality for data scientists since they need to find data from reliable sources.
  • Data analysis and visualization: This is the ability to perform a SWOT analysis of the problem, spot patterns, similarities, and trends in text-based data that might go unnoticed. 
  • Effective Communication: Data visualization and presentation skills go hand in hand. Data analysts need to clearly and fluently translate their technical findings to non-technical teams, to enable the business to make decisions with quantified insights. 


There is a constant demand for data science professionals across the globe. To have a successful career in data science, it is imperative to have an analytical mindset required at each step of the job. It helps you to come up with knowledgeable, practical solutions to common business problems. Analytical skills can be categorized as soft skills that are harder to measure because they are more intuitive. To acquire this soft skill, one needs to be observant, learn & read, gain knowledge, ask questions, explore how things work, and practice problem-solving. 

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 


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