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Irrespective of the type of data science company or role you are looking forward to, you should excel in these skills to become a successful data scientist:
- Mathematics — Understanding mathematics is crucial as a data scientist. Mathematics is relevant in all types of companies, but especially those data-driven companies where employers will rely on your help in the decision-making process.
- Multivariable Calculus and Linear Algebra — Understanding these concepts is essential in all those companies where a product is defined by data. During your job interview in any company, you may be asked to solve some calculus or algebra-based equations to check your job readiness on the point.
- Machine Learning — This skill is crucial if your aim is to work for any large company with massive data. In such companies, where the product itself is data-oriented, it’s important to become familiar with machine learning methods first.
- Data wrangling — Often, the processed data becomes confusing or difficult to work with. Because of this, it is essential to have data wrangling skills to deal with data imperfections that may arise.
- Data visualization- Data visualization and communication is very important in various job roles in data science. Being familiar with the tools needed to visualize the data is not enough. You must also know about different principles behind the encoding of data and data transmission.
- Data Intuition- Companies want to hire a data-driven problem solver. At some point during the interview process, you will probably be asked about a specific issue that might arise with data at a higher level. And you may lose your job if you aren’t spontaneous enough at that point. Therefore, you must value data intuition to avoid any such situation in life.
- Proper training- Data science being the multi- faceted and multi- dimensional field that it is, requires extensive skill sets and motivation to succeed in this profession. skills can always be developed through training. Make sure that you undergo training that involves concepts, examples, case studies, projects and assessments that measure and reinforce learning.