The Harvard Business Review called ‘Data Scientists’ as the ‘Sexiest Job of the 21st Century’. Undoubtedly, it has become the fascination of thousands of youngsters. Moreover, the induction of automation in several industries has boosted the need for Data Scientists, to develop machine learning techniques using mathematics, statistics, and computer science and information technology.
The pathway to becoming a Data Scientist is time-bound, reliant on your prior knowledge and your network. Experience plays a significant role acquiring the post of a Data Scientist. Above all, if you aspire to become a Data Science Expert, you should be a constant learner. Probability, statistics, and linear algebra are the central themes about mathematics that you should teach yourself. Gain ample amount of knowledge about statistical learning or machine learning, and these essential mathematical fundamentals will aid as a base for your entire learning mechanism.
Nate Silver, an American statistician states ‘We must become more comfortable with probability and uncertainty.’ Mathematically, the probability is the amount of the prospect that an event will occur. A lot of data science is based on endeavoring to measure the likelihood of events. It is used in calculating the probability of the reach of advertisements, the growth of customer base, etc.
Statistics deals with examining and interpreting data. Once you're done with learning sufficient about probability, you can get your hands on statistics. It enables you to get through with assortment, investigation, explanation, demonstration, and organization of data. In becoming a Data Scientist, you will have to apply the statistical approach in solving numerous scientific, industrial and social complications. Statistics help you plan your survey process for efficient data collection and also enables you to generate accurate reports.
Linear Algebra is extensively used in machine learning. Understanding Linear algebra is crucial to comprehend how algorithms function. Linear Algebra covers the study of vector spacing and linear mapping. Hence, it is imperative for you to learn basic and complex fundamentals of Linear Algebra.
Python and R are the major programming languages used in the Data Science community. Julia and Matlab are also used in very few sections. Python is a broad-spectrum programming language with lots of possibilities. It helps in developing Graphical user interfaces, Web frameworks, Multimedia and Databases. It is also used in Networking and Test Frameworks. The development of automation using Python semantics has proved to be a great invention. This programming language is also used in Text and Image processing. A lot of machine learning system can be developed by Python and hence learning and mastering it gives you added advantage in your trail towards becoming a Data Scientist.
It is advisable to take up courses that can aid in developing your skills in mathematics and programming, the key essentials in the Data Science Industry. On mastering your abilities and gaining sufficient knowledge, you can start your hunt for the role of a Data Scientist.