Complete Road Map for Data Science & ML for Begineers

Why Data Science? (Decide the Goal First?)So before jumping into the complete Roadmap of Data Science one should have a clear goal in his/her mind that why he/she wants to learn Data Science? Is it for the phrase “The Sexiest Job of the 21st Century“? Is it for your college academic projects? or is it for your long-term career? or do you want to switch your career to the data scientist world? So first make a clear goal. Why do you want to learn Data Science? For example, if you want to learn Data Science for your college Academic projects then it’s enough to just learn the beginner things in Data Science. Similarly, if you want to build your long-term career then you should learn professional or advanced things also. You have to cover all the prerequisite things in detail. So it’s on your hand and it’s your decision why you want to learn Data Science.How to Learn Data Science?Usually, data scientists come from various educational and work experience backgrounds, most should be proficient in, or in an ideal case be masters in four key areas.Domain KnowledgeMath SkillsComputer ScienceCommunication SkillDomain KnowledgeMost people thinking that domain knowledge is not important in data science, but it is very important. Let’s take an example: If you want to be a data scientist in the banking sector, and you have much more information about the banking sector like stock trading, know about finance, etc. so this is going to be very beneficial for you and the bank itself will give more preference to these type of applicants more than a normal applicant.Math SkillsLinear Algebra, Multivariable Calculus & Optimization Technique, these three things are very important as they help us in understanding various machine learning algorithms that play an important role in Data Science. Similarly, understanding Statistics is very significant as this is a part of Data analysis. Probability is also significant to statistics and it is considered a prerequisite for mastering machine learning.