What is a data scientist? 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.
A data scientist’s main objective is to organize and analyze large amounts of data, often using software specifically designed for the task. The final results of a data scientist’s analysis needs to be easy enough for all invested stakeholders to understand — especially those working outside of IT.
The following skills, not in the order of priority, all contribute to a great data scientist, regardless of the level of experiences. You don't need to be a master in all of these but you should understand the importance and role of these in your work, and of course be really good at a few of them.
Programming Skills: More specifically a statistical programming language, like R or Python, and a database querying language like SQL.
Statistics: A good understanding of statistics is vital as a data scientist. You should be familiar with statistical tests, distributions, maximum likelihood estimators, etc.
Machine Learning: IWe are capturing and accumulating massive amounts of data and this data can not be useful without the use of Machine Learning.
Multivariable Calculus & Linear Algebra: small improvements in predictive performance or algorithm optimization can make a huge difference in success of products. multivariable calculus or linear algebra questions, since they form the basis of of Machine Learning and statistics so your knowledge in this area will help form a better understanding of the root of specific outcomes or behaviors in data.
Data Visualization & Communication: Visualizing and communicating data is extremely important. And when it comes to communicating, this means describing your findings, or the way techniques work to audiences, both technical and non-technical. It is helpful to be familiar with data visualization tools like matplotlib, ggplot, or d3.js.