In simple words, a Data Scientist is one who practices the art of Data Science. The highly popular term of ‘Data Scientist’ was coined by DJ Patil and Jeff Hammerbacher. Data scientists are those who crack complex data problems with their strong expertise in certain scientific disciplines. They work with several elements related to mathematics, statistics, computer science, etc. this blog aims to explain the technical and non-technical skills that are critical for success in data science.
Data scientists usually have a Ph.D. or Master’s Degree in statistics, computer science or engineering. This gives them a strong foundation to connect with the technical points that form the core of the practice in the field of data science.
You need to 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. Programming languages help you clean, massage, and organize an unstructured set of data.
2. Knowledge of Sas and Other Analytical Tools
The understanding 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 you to establish your expertise in the use of these analytical tools.
3. Adept at Working with 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.
1. A 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 channelled 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.
2. 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.
3. 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 that comes with experience and boot camps are a great way of polishing it.