What exactly does a Data scientist do?
Data scientists are deep thinkers, often curious to find out the depth of any data provided to them. One would always be asking new questions and making new discoveries. When provided with big data, apart from the computational aspects, these professionals instantly start using their business acumen and skills to solve data science problems and help their organization get the greatest return on investments.
Education and Programming Skills
A good educational background is highly needed to be a professional in this field. Research shows that at least 88% of professionals in the field of data science would have a Master’s degree and around 46% are even PhD holders. To begin with, a data scientist’s qualifications include a bachelor’s in computer science, engineering and/or statistics. Special skill sets on how to use Hadoop or Big Data querying is definitely an added plus especially when one needs to get a master’s in data science and/or PhD in a related field of study. Working knowledge of programming is essential to set foot in the world of Data Science professionals. Knowledge of analytical tools like ‘R’ is preferred as it is one of the easiest ways of solving statistical problems.
Technical Skills needed to be a Data Scientist
Python Coding: The most common coding language for programmers is also a requirement for data scientists. Other languages include Java, Perl, or C/C++. Python is sought after because of the versatility that it provides and SQL tables that can be easily imported to code. Statistics modelling in python is also made relatively easy since it allows users to create datasets. One can find the different types of dataset that is needed on Google.
Apache Hadoop: An added advantage would be having a knowledge of Apache Hadoop. In fact, a study carried out by CrowdFlower stated that data scientists with at least a working knowledge of Apache Hadoop are preferred with a rating of 49%. In a situation where data is in large volume, and one needs to send it to various different servers, Hadoop comes in really handy. One can use Hadoop to convey data quickly along with data exploration, data summarization, filtration and even sampling.
SQL Database/Coding: SQL or Structured Query Language helps programmers extract data/information from big data in a structured way. It even helps in analytical functions and transforms database structures. Since SQL is used to deal with large volumes of data, the knowledge of SQL is a requisite for data scientists.
Machine Learning and AI: The data science learning path includes having a good knowledge of machine learning and AI (artificial intelligence). Machine learning techniques such as supervised machine learning, logistic regression, decision trees etc. are sure to set a data scientist apart from the others. Other advanced machine learning techniques include Outlier detection, Survival analysis, Reinforcement learning and Computer vision to name a few.
Non-Technical Skills needed to be a Data Scientist
Intellectual Curiosity: If one needs to go a long way in the field of data science one needs to be innately curious. Continuous research, prolific reading and an ability to ask plenty of questions would make one a good data scientist. One also needs to carry out data wrangling because going through large volumes of data is the primary task of a data analyst.
Business Acumen: As a data scientist one is basically helping businesses to further their business by steering them in the right direction. Hence a solid understanding of the industry is needed to be able to help an organization solve their problem and leverage data in the best possible way.
Communication Skills and Teamwork: Data Scientists must be able to clearly and fluently translate their technical findings in layman’s terms to be able to keep members of other teams on the same page. Working within the team is also necessary, as work is co-dependent on other teams as well and hence data scientists must be able to put their thoughts into clear words and be able to collaborate easily and efficiently.