Skills Required To Become Data Scientists

Leveraging the application of big data, whether it is to improve the process of product development, improve customer retention or work through the data to find new business possibilities organizations are more relying on the expertise of data scientists to sustain, grow and beat their competition. Consequently, as the market for data scientists increases, the system presents an exciting career path for students and existing professional.

Both Technical and Non-Technical skills required in Data Science:

Technical Skills:

Data Scientists usually have a Ph.D. or Master's Degree in statistics, computer science or engineering. This gives a strong foundation to connect with the technical points that form the core of the practice in the field of data science.

Programming Skills: Must have the knowledge of programming languages like Python, Perl, C/C++, SQL and Java with Python being the most common coding language required in data science roles. Python is a super powerful language and the number one language just because of Machine Learning. If we talk about big companies like Google, Facebook or startup their engineers have a thorough knowledge about Python. If some want to implement ML anyhow into their product must have a great understanding of this language to grow up. Programming languages help you clean massage and organize an unstructured set of data.

Knowledge of SAS and Analytical Tools: The knowledge of analytical tools is what will help you extract the valuable insights out of the cleaned, massaged, and organized data set. SAS, Hadoop, Spark, Hive, Pig, and R are the most popular data analytical tools that data scientists use. Certifications can further help to establish the expertise in the use of these analytical tools.

Manage Unstructured Data: When talking about the skill of being able to work with unstructured data, we are specifically emphasizing the ability of a data scientist to understand and manage data that is coming unstructured from different channels. So, if a data scientist is working on a marketing project to help the marketing team provide insightful research, the professional should be well adept at handling social media as well.

Non-Technical Skills:

Strong Business Acumen: If a data scientist does not have business acumen and the know-how of the elements that make up a successful business model, all those technical skills cannot be channeled productively. You won't be able to discern the problems and potential challenges that need solving for the business to sustain and grow. You won't really be able to help your organization explore new business opportunities.

Strong Communication Skills: You are a data scientist and understand data better than anyone else. However, for you to be successful in your role, and for your organization to benefit from your services, you should be able to successfully communicate your understanding with someone who is a non-technical user of data. You need to have strong communication skills as a data scientist.

Great Data Intuition: This is perhaps one of the most significant non-technical skills that a data scientist needs. Great data intuition means perceiving patterns where none are observable on the surface and knowing the presence of where the value lies in the unexplored pile of data bits. This makes data scientists more efficient in their work. This is a skill which comes with experience and boot camps are a great way of polishing it.

Source: HOB

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