---
product_id: 598153487
title: "Modern Statistics: Intuition, Math, Python, R"
price: "KSh15290"
currency: KES
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reviews_count: 8
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---

# Modern Statistics: Intuition, Math, Python, R

**Price:** KSh15290
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- **What is this?** Modern Statistics: Intuition, Math, Python, R
- **How much does it cost?** KSh15290 with free shipping
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## Description

Review: An oasis in a desert of statistics books! - Having thoroughly read the first six chapters, I believe I am in a good position to comment on the book thus far. First, to legitimize my opinions, it is important to mention my background in Science. I am a pharmaceutical chemist, and I have a master's degree in Biochemistry. I recently did an online specialization in data analysis with R through Duke University. Usually what you expect when you begin reading a Statistics book is to start by learning concepts about probability and you will have to be comfortable solving exercises using dice; instead, Cohen’s book starts with even more fundamental topics, critical to understand, comprehend and play around with more advanced concepts. Starting by reviewing subjects like data, visualization, transformations, and, in particular, simulations, allows you to leverage your thinking as a researcher. Examples like those outlined in section 5.6.1 and exercises 4.8, 5.1 and 5.2, to mention just a few, are so insightful. All the chapters so far are full of very valuable information derived from the author's experience, even addressing questions that other books catalog as “out of the scope of this book”. Reading this book, you feel as though you are being guided by a real wise professor, with wisdom derived from his experience in the field of data analysis. But most importantly, from a pedagogical point of view, the book was designed and structured in the same way as higher education should be: the professor, in this case the author’s book, guides us through the concepts, but the reader shoulders the responsibility of learning, in particular, by going thoroughly through the exercises, exercises which were very, very well designed and structured, having a specific pedagogical goal in mind: make the reader discover by himself the reasoning behind every single algorithm, statistical procedure or concept. Sometimes one exercise lays the groundwork for the next one, and after finishing all the end-chapter exercises, you have essentially completed a full workshop on the topic! In this way, one can learn statistical concepts in concrete scenarios utilizing creative simulated experiments rather than by throwing dice. One has to figure out why and how a particular problem should be tackled, and then, one must figure out why and how particular code should be used and implemented. These drills, which I found highly challenging, will be absolutely useful for solving future problems in our role as data analysts. In addition, the book delightfully merges statistical concepts with the underlying mathematical foundations. Caveat: You should try to solve the exercises by yourself. If that is definitively impossible, analyze the code provided by the author. Don’t get intimidated by the code presented! Be patient and seek additional information on the Internet about new R commands you come across, and why the author used them, and how. In my case, I spent many hours trying to decipher the code stated for a single exercise, but it has paid off. In order for you to improve your coding skills even further, I suggest that once you have understood the whole code, try to implement new ways of solving that particular exercise, ways that make more sense to you or that are more intuitive to you. I have discovered that some exercises could be tackled with fewer lines of code and in a more intuitive way. In summary, up to this point in my reading (chapter 6), the author has done exceptional work in regard to structure, content, and explanations. The statistical reasoning one acquires by working on those simulations is phenomenal, not to mention the skills acquired on R coding. I think this is the type of book targeted at researches who want to leverage their (mathematical) comprehension of statistics concepts to be applied as part of their tools to understand the Universe, not for those folks who wants to earn some bucks by introducing data into a statistical software, copy and paste some code, then present the yield as an “analysis”. Thank you Professor Cohen for the effort you put into this book. I will update my review as I continue reading chapters.
Review: Clear, Engaging, and a Great Resource! - I love Dr. Cohen’s clear and engaging tone! Having taken his EEG, stats, and MATLAB courses, I was thrilled to get this book. It’s a great resource with very intuitive explanations of statistical methods. I especially recommend it for those without a neuroscience or experimental background who haven’t collected data before. It helps build an understanding of data such as outliers and emphasizes that there’s no one-size-fits-all approach to analysis. Plus, the wide margins make it perfect for taking notes. Highly recommend! 📖✨

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #138,564 in Books ( See Top 100 in Books ) #2 in Transformations Mathematics #7 in Mathematics Research (Books) #49 in Mathematics Study & Teaching (Books) |
| Customer Reviews | 4.6 out of 5 stars 56 Reviews |

## Images

![Modern Statistics: Intuition, Math, Python, R - Image 1](https://m.media-amazon.com/images/I/6188BjmxWRL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ An oasis in a desert of statistics books!
*by O***O on September 18, 2025*

Having thoroughly read the first six chapters, I believe I am in a good position to comment on the book thus far. First, to legitimize my opinions, it is important to mention my background in Science. I am a pharmaceutical chemist, and I have a master's degree in Biochemistry. I recently did an online specialization in data analysis with R through Duke University. Usually what you expect when you begin reading a Statistics book is to start by learning concepts about probability and you will have to be comfortable solving exercises using dice; instead, Cohen’s book starts with even more fundamental topics, critical to understand, comprehend and play around with more advanced concepts. Starting by reviewing subjects like data, visualization, transformations, and, in particular, simulations, allows you to leverage your thinking as a researcher. Examples like those outlined in section 5.6.1 and exercises 4.8, 5.1 and 5.2, to mention just a few, are so insightful. All the chapters so far are full of very valuable information derived from the author's experience, even addressing questions that other books catalog as “out of the scope of this book”. Reading this book, you feel as though you are being guided by a real wise professor, with wisdom derived from his experience in the field of data analysis. But most importantly, from a pedagogical point of view, the book was designed and structured in the same way as higher education should be: the professor, in this case the author’s book, guides us through the concepts, but the reader shoulders the responsibility of learning, in particular, by going thoroughly through the exercises, exercises which were very, very well designed and structured, having a specific pedagogical goal in mind: make the reader discover by himself the reasoning behind every single algorithm, statistical procedure or concept. Sometimes one exercise lays the groundwork for the next one, and after finishing all the end-chapter exercises, you have essentially completed a full workshop on the topic! In this way, one can learn statistical concepts in concrete scenarios utilizing creative simulated experiments rather than by throwing dice. One has to figure out why and how a particular problem should be tackled, and then, one must figure out why and how particular code should be used and implemented. These drills, which I found highly challenging, will be absolutely useful for solving future problems in our role as data analysts. In addition, the book delightfully merges statistical concepts with the underlying mathematical foundations. Caveat: You should try to solve the exercises by yourself. If that is definitively impossible, analyze the code provided by the author. Don’t get intimidated by the code presented! Be patient and seek additional information on the Internet about new R commands you come across, and why the author used them, and how. In my case, I spent many hours trying to decipher the code stated for a single exercise, but it has paid off. In order for you to improve your coding skills even further, I suggest that once you have understood the whole code, try to implement new ways of solving that particular exercise, ways that make more sense to you or that are more intuitive to you. I have discovered that some exercises could be tackled with fewer lines of code and in a more intuitive way. In summary, up to this point in my reading (chapter 6), the author has done exceptional work in regard to structure, content, and explanations. The statistical reasoning one acquires by working on those simulations is phenomenal, not to mention the skills acquired on R coding. I think this is the type of book targeted at researches who want to leverage their (mathematical) comprehension of statistics concepts to be applied as part of their tools to understand the Universe, not for those folks who wants to earn some bucks by introducing data into a statistical software, copy and paste some code, then present the yield as an “analysis”. Thank you Professor Cohen for the effort you put into this book. I will update my review as I continue reading chapters.

### ⭐⭐⭐⭐⭐ Clear, Engaging, and a Great Resource!
*by M***A on February 5, 2025*

I love Dr. Cohen’s clear and engaging tone! Having taken his EEG, stats, and MATLAB courses, I was thrilled to get this book. It’s a great resource with very intuitive explanations of statistical methods. I especially recommend it for those without a neuroscience or experimental background who haven’t collected data before. It helps build an understanding of data such as outliers and emphasizes that there’s no one-size-fits-all approach to analysis. Plus, the wide margins make it perfect for taking notes. Highly recommend! 📖✨

### ⭐⭐⭐⭐⭐ Perfect text on statistics
*by A***R on November 26, 2024*

This is an amazing text on Statistics written from the field, not like some BS ivory tower professor’s book. I wish I had seen this book before I spent hours trying to figure out inconsistent notations, formula proofs in other books. This book should be a prerequisite for anyone who wants to learn what statistics knowledge is necessary today.

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*Last updated: 2026-02-24*