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Key Skills to Become a Data Science Engineer

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In a 2017 business research article IBM predicted that the need for Data Scientists will increase 28% by 2020, with nearly 3 million job openings for Data Science professionals. According to a Forbes report, Data Scientist has been named the best job in America for three consecutive years, with a median base salary of $110,000 and over 4,524 job openings. According to Glassdoor’s 50 Best Jobs In America For 2018 research, Data Scientist jobs are ranked among the 50 best jobs based on each job’s overall Glassdoor Job Score. The Glassdoor Job Score is determined by weighing three key factors equally: earning potential based on median annual base salary, job satisfaction rating, and the number of job openings. Hence, the need for sharpening Data Scientist skills are at an all-time high.

Organizations across the world look for Data Science professionals with the right mix of demonstrated academic excellence and impressive soft skills. A successful Data Science professional should have eyes of a data visualizer, thinking powers of an analyst, and genius of a master communicator. Data Scientist skills are not always defined by one’s academic degrees, but the right attitude.

This discussion throws light on the key Data Scientist skills in demand, what recruiters want to see in a killer Data Science resume, and how aspiring Data Science engineers can hon their skills to become Data Evangelists in near future.



Technical Data Scientist Skills:

Data Scientist’s chief responsibility is data analysis, a process that begins with data collection and ends with business decisions made based on the data scientist’s final data analytics results. They deal with large volumes of structured and unstructured data, and always finding better tools, languages or approaches to managing data. Though a difficult choice I have handpicked a few hard skills for data scientists.

  • Programming:

Programming is one of the most important technical skills required for Data Scientist. Having a strong programming base helps you put your statistical knowledge to use. Data science as a career is probably the best place to augment your knowledge of statistics.  In addition, you also get to study large data sets, which goes a long way to shaping your future role as a Data Scientist role.

Having a strong command of programming languages like Python, MATLAB, Perl, C/C++, SQL, Scala, and Java is one of the key attributes of a data science professional. Knowledge of Programming languages imbibes in you a habit of sorting meaningful data from vast volumes of an unstructured set of data and later organizing them.

Python Libraries for Data Science

Data scientists are often involved with wiring together network applications, programming for the web, scripting and automating data processing jobs. If you are looking for one programming language to do all these tasks, then Python is the answer. You may go for an advanced online course in Python to gain a competitive advantage over others in Data Science career.

Know more about Python libraries.

Want to know, which one works better for you, Python or R, read our earlier post on R vs. Python, Which One is the Best for Data Analysis.

  • Quantitative Analysis:

A data scientist spends most of his time studying the behaviour of complex systems(s) by analyzing the data that it produces. Quantitative Analysis lies at the core of Data Science skills, which helps you model complex structures or analyzing large data sets.

As a seasoned data analyst, you will have to go beyond common models like churn models or customer lifetime value models and learn about complicated models like supply + demand modeling, economically-optimal ways to match providers and suppliers, and methods to model the growth channels of a company to better quantify which growth avenues are the most valuable. Valued as an important Data Scientist skill, Quantitative Analysis improves your ability to run the experimental analysis, scale your data strategy and help you implement machine learning.

  • Machine-Learning:

If you have a background in Statistics or Economics, Machine Learning of all Data Science skills would appeal you the most. Machine learning is a set of algorithms that train on a data set to make predictions or take actions to optimize some systems. For instance, supervised classification algorithms are used to classify potential clients into good or bad prospects, for loan purposes, based on historical data. machine learning also encompasses deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Machine learning counted among the core Data Scientist skills requires you to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

With a keen interest in machine learning, you may help create prototypes to test assumptions, select and create features, and identify areas of strength and opportunity in existing machine learning systems. You will derive immense satisfaction as a data science professional to find hidden themes in large collections of documents using topic modelling or use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth.

  • Product Intuition:

Product Intuition is one of the basic premises for a data scientist to perform quantitative analysis on the system. Product knowledge as a Data Scientist skill set implies understanding the complex system that generates all the data that data scientists analyze. Product Intuition will enable you to understand the product so well, that you can generate hypotheses about ways the system can behave if alterations are made.

As a data scientist, you will be expected to know about the product in and out to create product metrics so that you can make an accurate estimate of what is to be measured and predict the probable fluctuations. As a data scientist, you should have prior knowledge of any change in metrics. It comes from a thorough knowledge of the product and product behaviour. Start studying products with an intuitive approach today if you aspire for a Data Scientist role in near future.

Having a good product intuition is an essential requirement for debugging. As a Data Scientist skill set, product intuition aids faster sanity checks and back-of-the-envelope calculations that can help more quickly identify things that might have gone wrong.

Soft Skills Required for Data Scientist:

Technical Data Scientist skill sets like programming and quantitative analysis are essential for filling up a Data Scientist role, but soft skills are no less important. Successful data science professionals not only execute complicated database queries but also share an excellent rapport with all business stakeholders, data users, and producers in the organization.

  • Communication:

Communication is one of the key Data Science skills. A Data Scientist role involves working in close liaison with engineers, designers, product managers, operations, and business stakeholders. Clear and concise communication, conveyed through excellent presentation skills is often the biggest strength of ace data scientists.

Story-telling is often an interesting way to represent your findings to the mass. Master the art of weaving a beautiful story of the data journey, to demonstrate your expertise in data science. Being an expert in your domain is not enough, the ability to share your knowledge in a convincing manner is what matters. Being a prolific communicator helps a data scientist play twin role of a trainer and an innovator.

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  • Teamwork:

Being a team player is one of the best Data Science skills. A successful data scientist grows with his team. Getting feedback and implementing it in the right manner with desired results is the primary responsibility of a data scientist. A data scientist’s work also involves back-and-forth iteration and feedback with others to reach an impactful solution. These can only be achieved by a data science who understands the importance of working in collaboration, sharing knowledge, and standing tall in times of crisis.

A data scientist who shares useful statistical techniques, frameworks, libraries, languages, and tools with his team members, is a boon for his team and the organization. An organization grows only when its data scientists share their knowledge, methods, and results with each other.

  • Intellectual Curiosity:

A successful career in data science requires much more than certified degrees. A Data science professional is distinguished from his peers by his intellectual curiosity. Being an expert in product knowledge or staying updated about industry changes, are a few necessary conditions, but what really matters is your ability to question data types and discover data in newer ways. An inquisitive mind and willingness to explore the unknown are two of the important Data Scientist skills.

An average mind simply looks for solutions to common issues, whereas an expert comes up with questions that affect all but never asked. The intellectual capacity of a data scientist propels him to find newer ways to explore data sets with interesting insights. The capacity to absorb knowledge and share data insights marks the potential of a data scientist.  Read discussions, watch video interviews of well-known data scientists in India and abroad to know how data scientists think and act.

  • Business Acumen:

Having a strong business acumen is one of the essential skills required for Data Scientist. Understanding key industry trends, customers’ pain points, and competitors are what defines the business acumen of a data scientist. If you are, for example, a data science professional working for healthcare, you should be cognizant of the use of Big Data, security, and compliance in maintaining electronic records. The Data Scientist role in healthcare is to drive practices that improve health services and uncover newer tools and techniques. One should also have a clear understanding of business and be able to relate to changing customer tastes, product cycles and profitability goals are critical to finding truly innovative solutions.


Be a Data Scientist today and explore the abundant career options in data science. You might be a programmer, a mathematics graduate, or simply a bachelor of Computer Science. Taking up a good Data Science or Data Analytics course teaches you the key Data Science skills and prepares you for the Data Scientist, Data Scientist role (that you aspire for) in near future.

You may also enroll for a Digital Vidya’s Data Analytics Course for more lucrative career options in Data Science.  Industry-relevant curriculum, pragmatic market-ready approach, Hands-on Capstone Project are some of the best reasons for choosing Digital Vidya.

A self-starter technical communicator, capable of working in an entrepreneurial environment producing all kinds of technical content including system manuals, product release notes, product user guides, tutorials, software installation guides, technical proposals, and white papers. Plus, an avid blogger and Social Media Marketing Enthusiast.

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    • 2 months ago

      TTI   /   Reply

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