Data is the most important thing for a company to run successfully. Companies plan strategies according to the data they have collected and analyzed. They need a person to collect all useful information in one place and plan a strategy. This is where the data scientists come in. As data plays a huge part in making company plans, there is a demand for data scientists.
Aspirants can pursue Data science courses online to learn the basics of Data science or pursue full-time Data science courses after the 12th. BSc Data Science, BCA Data Science at the bachelor’s level, and MTech Data Science, MCA Data Science, and MSC Data Science are top data science courses along with various data science certifications, from Unacademy, Upgrade, edX, Simplilearn, and IBM. Coursera offers data science degrees online from top universities.
A data scientist’s chief responsibility is data analysis, a process that begins with data collection and ends with business decisions made on the basis of the data scientist’s final data analytics results. A data scientist's role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.
Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role is becoming increasingly important as businesses rely more heavily on data analytics to drive
decision-making and lean on automation and machine learning as core components of their IT strategies.
Skills that are required are:
Statistics:
When you start learning to write sentences, you must be familiar with grammar to build the right sentences similarly statistics is an essential concept before you can produce high-quality models. Machine Learning starts out as statistics and then advances. Statistics is a MUST concept to become a data scientist.
Programming:
Knowing a programming language is essential in order to become a data scientist. Programming allows you to take vaste amounts of data and process them quickly in a meaningful way. Right now some of the most popular languages for data science analytics include Python or R
.
R is specially designed for data science to deal with big data. It is generally preferred for data science to gain in-depth knowledge of analytical tools.
Product understanding:
Understanding products will help you perform quantitative analysis. It will also help you predict system behavior, establish metrics and improve debugging skills.
Teamwork:
Much like communication, teamwork is vital to a successful data science career. It requires being selfless, embracing feedback, and sharing your knowledge with your team.
Curiosity:
Not only will curiosity keep you driven to continue your learning in the long run, but it will also help you know what questions to ask when you are diving into a new set of data. Your first answer is rarely the right one. If you keep diving deeper you may find things that surprise you or change your whole understanding of the problem.