10 Mandatory Skills to Become a Data Scientist

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Last updated on
11th Mar, 2021
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30th Apr, 2020
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10 Mandatory Skills to Become a Data Scientist

The data science industry is growing at an alarming pace, generating a revenue of $3.03 billion in India alone. Even a 10% increase in data accessibility is said to result in over $65 million additional net income for the typical Fortune 1000 companies worldwide. The data scientist has been ranked the best job in the US for the 4th year in a row, with an average salary of $108,000; and the demand for more data scientists only seems to be growing. 

Who is a Data scientist? 

A data scientist is precisely someone who collects all the massive data that is available online, organizes the unstructured formats into bite-sized readable content, and analyses this to extract vital information about customer trends, thinking patterns, and behavior. This information is then used to create business goals or agendas that are aligned to the end-user/customer’s needs.  

This outlines that a data scientist is someone with sound technical knowledge, interpersonal skills, strong business acumen, and most importantly, a passionate data enthusiast. Listed below are some mandatory skills that an aspiring data scientist must develop. 

10 Mandatory Skills to Become a Data Scientist 

Technical Skills  

  • Programming, Packages, and Software 

Since the first task of data scientists is to gather all the information or raw data and transform this into actionable insights, they need to have advanced knowledge in coding and statistical data processing. Some of the common programming languages used by data scientists are Python, R, SQL, NoSQL, Java, Scala, Hadoop, and many more.  

  • Machine Learning and Deep Learning 

Machine Learning and Deep Learning are subsets of Artificial Intelligence (AI). Data science largely overlaps the growing field of AI, as data scientists use their potentials to clean, prepare, and extract data to run several AI applications. While machine learning enables supervised, unsupervised, and reinforced learning, deep learning helps in making datasets study and learn from existing information. A good example is the facial recognition feature in photos, doodling games like quick draw, and more. 

  • NLP, Cloud Computing and others 

Natural Language Processing (NLP), a branch of AI that uses the language used by human beings, processes it and learns to respond accordingly. Several apps and voice-assisted devices like Alexa and Siri are already using this remarkable feature. As data scientists use large amounts of data stored on clouds, familiarity with cloud computing software like AWS, Azure, and Google cloud will be beneficial. Learning frameworks like DevOps can help data scientists streamline their work, along with several other such upcoming technologies. 

  • Database knowledge, management, and visualization 

A collection of information organized to easily access, manage, and update the data is called a database. Data scientists must have a strong database knowledge and use its different types to their advantage. Some examples include relational databases like SQL, distributed database, cloud database, and many more. Once this is expertise is established, analyzing the data, database management, and data visualization are also important skills.  

  • Domain knowledge  

Domain knowledge about the domain in which data is to be analyzed and predictions will be made is important. One can harness the true power and fullest potential of an algorithm and data only by having specific domain language. Instead of waiting to analyze the data, the goals itself can be shaped towards actionable results with the help of domain knowledge.  

Non-technical Skills 

  • Communication skills 

As explained above, once the raw data is processed, it needs to be presented understandably. This does not limit the job to just visually coherent information but also the ability to communicate the insights of these visual representations. The data scientist should be excellent at communicating the results to the marketing team, sales team, business leaders, and other stakeholders. 

  • Team player 

This is related to the previous point. Along with effective communication skills, data scientists need to be good team players, accommodating feedback, and other inputs from business teams. They should also be able to efficiently communicate their requirements to the data engineers, data analysts, and other members of the team. Coordination with their team members can yield faster results and optimal outputs. 

  • Business acumen  

Since the job of the data scientist ultimately boils down to improving/growing the business, they need to be able to think from a business perspective while outlining their data structures. They should have in-depth knowledge of the industry of their business, the existing business problems of their company, and forecasting potential business problems and their solutions. 

  • Critical thinking 

Apart from finding insights, data scientists need to align these results with the business. They need to be able to frame appropriate questions and steps/solutions to solve business problems. This objective ability to analyze data and addressing the problem from multiple angles is crucial in a data scientist. 

  • Intellectual curiosity  

According to Harvard Business Review, data scientists spend 80% of their time discovering and preparing data. For this, they must always be a step ahead and catch up with the latest trends. Constant upskilling and a curiosity to learn new ways to solve existing problems quicker can get data scientists a long way in their careers. 

Taking data-driven decisions 

Data science is indisputably one of the leading industries today. Whether you are from a technical field or a non-technical background, there are several ways to build up the skill to become a data scientist. From online courses to bootcamps, one should always be a step ahead in this competitive field to build up their data work portfolios. Additionally, reading up on the latest technologies and regular experimentation with new trends is the way forward for aspirants.  

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