Introduction: Data Science
Data Science is the field of study combining specific field expertise, programming skills, and knowledge of statistics and mathematics to draw meaningful understandings from the data. The practitioners of Data Science apply Machine Learning to the text, numbers, images, audio, video, and more to produce Artificial Intelligence systems in performing the tasks that generally require human intelligence. Many companies are realizing the importance of Data Science, Artificial Intelligence, Machine Learning, etc. regardless of their industry or size. To remain competitive in the age of Big Data companies need to develop and execute data science capabilities efficiently.
Data Science is an experimental way of focusing on a forward-looking approach by analyzing the historical and current data to predict future outcomes to make informed decisions. The relevant topics covered in Data Science are Statistics, Linear Algebra, Programming, Machine Learning, etc.
The best books written by great authors for getting a good understanding and knowing how to implement them in real-time are listed below:
1. Introduction to Statistical Learning (ISLR)
The book Introduction to Statistical Learning does a great job of adding a statistical viewpoint to your knowledge by covering some of the algorithms as Hands-On Machine Learning, with a more statistical bend. It also goes much deeper into the world of regression models as well as provides R code for practical application. Some of the topics included in this book are linear regression, resampling methods, classifications, etc. where the real-world examples are used to explain those methods.
The book was written to be an “accessible overview of the field of statistical learning” and gets the job done. To do so, the book focuses more on intuitive explanations as opposed to math.
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2. Mathematics for Machine Learning
This book introduces all the relevant Mathematical concepts needed to understand and use Machine Learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics.
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3. Linear Algebra by Gilbert Strang
I believe that one of the key benefits of taking linear algebra is to get exposed to abstract mathematical thinking and proofs.
Linear Algebra video lectures by Gilbert Strang: