A data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. They spend a lot of time in the process of collecting, cleaning, and mugging data, because data is never clean. This process requires persistence, statistics, and software engineering skills—skills that are also necessary for understanding biases in the data, and for debugging logging output from code.
Candidates wishing to become data scientists must secure 50% marks in aggregate at 10+2 level or equivalent with mathematics as one of their core subjects. At graduation level, the first step is to have a good understanding of statistics, if possible take up a crash course in statistics. While an advanced degree in computer science is usually preferred, it is not a must. Also, getting hands-on knowledge of programming skills in R, Scala and Python or Learn Visualization tools such as Tableau, Microsoft Power BI. Understanding of Hadoop and Apache Spark is a must. It is advised to learn the fundamentals of machine learning algorithms.
- Identifying the data-analytics problems that offer the greatest opportunities to the organization.
- Determining the correct data sets and variables.
- Collecting large sets of structured and unstructured data from disparate sources.
- Cleaning and validating the data to ensure accuracy, completeness and uniformity.
- Devising and applying models and algorithms to mine the stores of big data
- Analysing the data to identify patterns and trends.
- Interpreting the data to discover solutions and opportunities.
- Communicating findings to stakeholders using visualization and other means.
A natural curiosity is important, as it is creative and critical thinking. As a data scientist domain expertise and soft skills are equally crucial as technical skills. Besides technical skills, deep industry knowledge and problem-solving skills are definitely a must-have. You cannot find insights in real-life datasets if you are not asking the right questions. Finding patterns and insights is dependent on variables which are assigned by data scientists.
- A bachelor's degree is often enough to begin entry-level work.
- Analysts can have a direct impact on a company's operations.
- Analysts often work closely with top-level managers and executives.
- This field has applications in many areas such as economics, engineering, political science etc.
- Job opportunities are typically better for analysts with a master's degree.
- Continuing education is expected throughout your career.
- This can be a high-pressure job with tight deadlines.
- Travel for business and conferences is often necessary.
Avik SarkarHe currently heads the Data Analytics Cell at NITI Aayog (National Institution for Transforming India) as Officer on Special Duty (OSD) and believes that the challenges with analytics in governance are quite different from that of other industries. He is in charge of developing a roadmap for use of data/analytics for Governance and Policymaking along with programs. He has an experience of over 15 years across different aspects of data analytics, statistical modelling, data and text mining across companies like IBM, Accenture, Nokia, NASA, Persistent Systems etc.
- Kiran R is currently the Director, Data Sciences at VMware and has experience in driving impact in both businesses to business and business to customer organizations. He currently drives advanced analytics and data sciences support at VMware across verticals. Prior to VMware, he headed analytics & data sciences for Sales, Marketing & Customer at Flipkart, affiliates analytics at Amazon and for the e-business & search teams at Dell. Kiran has 3 filed US patents.