A massive explosion of data across various industries made everyone notice and think how to drive value of this huge information and that's where the expertise of a data scientist is called for. According to McKinsey report, it predicts that by 2018, there will be a 50 percent gap in the supply of data scientists versus demand.

Majority of data scientists are expected to have advanced degrees and training in statistics, math, and computer science. It is an add-on when their experience extends to data visualisation, data mining, and information management. To be a successful Data Scientist for the future one needs to develop certain skills and abilities and the summary of which is as below

1. Programming

   Being able to code is the most important skill to master by a data scientist. The ability to program serves the data scientist in several ways. The top three being: deriving increased value from statistics, examining and explaining large datasets and most importantly to create tools to enhance and add more meaning to data science. A data scientist will be required to know a statistical programming language, like R or Python, and a database querying language like SQL. Statistics play a crucial role in data-driven companies where the role of a data scientist becomes important in decision making, designing or evaluating experiments.

2. Quantitative Analysis

   Understanding and analysing the data forms the crux, where the data are derived both naturally or via experiments. A data scientist works along with a statistician to run massive experiments and such experiment analysis can proceed in a way that causes a bad result. Sometimes, data can be difficult to work with or rather messy. Therefore, it is vital to straighten out and derive maximum value out of it

3. Machine Learning

   While we know how statistics and programming form the base, another fundamental point that cannot be missed here is machine learning. Companies such as Ola, Swiggy, Amazon rely heavily on machine learning methods to increase business and reach new customers. A data scientist need not necessarily be an expert in algorithms, however, can help in creation of prototypes, features identifiable areas of strength and opportunity in existing machine learning systems.

4. Product and Data Intuition

   A large part of data scientist's ability lies in performing a quantitative analysis. Grasping the essence of the product and its working requires understanding how the system generates all the data that a data scientist analyses on a day to day basis. In-depth and working knowledge of a product helps the data scientist to generate hypotheses, determining the primary and secondary metrics that a company, can be use to set keep a track of its performance and lastly, perform sanity checks to identify loopholes and fix them on time. A data scientist is required to be a go-to person to solve any data related problem.

5. Non-Technical Skills

   All data solving problems and answers aren't of much use when the communication isn't right. Thus, a data scientist need to hone skills such as Data Visualisation, Communication, Teamwork, Personal Effectiveness and Time management. Learn to keep communication clear and concise, create presentations and graphs to send across some effective data insights. Coordinate and collaborate with team members to share knowledge, methods, and results for a better business. Lastly, no work can be done right in isolation, to ensure best results timely feedbacks are useful and this happens when you reach out and bond with people of your organisation.

Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway said Geoffrey Moore. This summarises why data science is gaining preference as a career choice and the value that data scientists bring to a company.