To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks 
 
 
 



        

            
            

                
Publisher
                
O'Reilly Media (10 Jun. 2022)
            
            
            

                
Paperback
                
350 pages
            
            
            

                
Dimensions
                
17.78 x 1.91 x 22.86 cm
            
            
            

                
Language
                
English
            
            
            

                
ISBN-10
                
1098102932
            
            
            

                
ISBN-13
                
978-1098102937
            
            
        

         





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