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Essential Statistics for Non-STEM Data Analysts
Essential Statistics for Non-STEM Data Analysts

Essential Statistics for Non-STEM Data Analysts: Get to grips with the statistics and math knowledge needed to enter the world of data science with Python

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Key benefits

  • Work your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisions
  • Understand how various data science algorithms function
  • Build a solid foundation in statistics for data science and machine learning using Python-based examples

Description

Statistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks. The book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. You’ll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, you’ll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. Once you’ve uncovered the working mechanism of data science algorithms, you’ll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, you’ll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning. By the end of this Essential Statistics for Non-STEM Data Analysts book, you’ll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals.

Who is this book for?

This book is an entry-level guide for data science enthusiasts, data analysts, and anyone starting out in the field of data science and looking to learn the essential statistical concepts with the help of simple explanations and examples. If you’re a developer or student with a non-mathematical background, you’ll find this book useful. Working knowledge of the Python programming language is required.

What you will learn

  • Find out how to grab and load data into an analysis environment
  • Perform descriptive analysis to extract meaningful summaries from data
  • Discover probability, parameter estimation, hypothesis tests, and experiment design best practices
  • Get to grips with resampling and bootstrapping in Python
  • Delve into statistical tests with variance analysis, time series analysis, and A/B test examples
  • Understand the statistics behind popular machine learning algorithms
  • Answer questions on statistics for data scientist interviews

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 12, 2020
Length: 392 pages
Edition : 1st
Language : English
ISBN-13 : 9781838984847
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Languages :
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Product Details

Publication date : Nov 12, 2020
Length: 392 pages
Edition : 1st
Language : English
ISBN-13 : 9781838984847
Category :
Languages :
Concepts :

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Frequently bought together


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Practical Discrete Mathematics
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Essential Statistics for Non-STEM Data Analysts
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Imran Sarker Feb 15, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book provides a lot of knowledge related to data analysis to its readers. It doesn’t matter whatyour background is when you read this book you start walking on the path to becoming a data analyst.Being an Analyst, it’s important to be familiar with Statistics in order to come up with results and turnyour idea into reality.Although, with the time being, new ways are being introduced but getting familiar with thebackground of everything is important. The author has distributed all the topics well so people fromany background a non-stem or stem can go through each chapter and learn as much as they can. Iwould definitely recommend this book to all who want to manage the data and find ways to deal with the data.
Amazon Verified review Amazon
Book_Reviews Mar 10, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great:* A very practical-oriented intro to the major statistic concepts and applications throughout are provided in python code and popular libraries used by professionals.* This is the best for those who have learned to code in Python, are interested in becoming a Data Analyst, or who have just graduated from a Data Science bootcamp!* Particularly useful if you are trying to bring more statistical methods into your current work.Needs Improvement:* initial chapter order in Section 1 didn't feel the most intuitive, but because you'll want to read this 3x times to fully grasp all the concepts anyway, it shouldn't be a problem.
Amazon Verified review Amazon
Amazon Customer Jan 05, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I came to this book with the perspective of a non-STEM professional who is transitioning into a Data Science career - so it seemed like it could be an ideal resource for someone with my profile, and that is indeed the case!Previously, I had worked through online courses to learn some Python and some statistics, and had also used the books ‘Python the Hard Way’ and ‘Statistics for Dummies’ quite extensively. The challenge with both of those books is that, in order to become a working data analyst or data scientist, you really need to learn both topics hand-in-hand: it's best to learn statistics while also learning to code in Python. That's exactly what you get in THIS book - working through the essential mathematics/statistics concepts, and learning to code them as you go.These are tough subjects to learn and it's not easy for anyone who doesn't have previous experience in coding - as the author says early on in the book, for a few chapters it's best to just "code along" with the assignments even if you don't fully understand. As you become more familiar with Python, the syntax will start to make sense.Additional notes:- If you’re apprehensive about starting your data analysis/data science journey, this book includes a very clear explanation of how beginners can start coding (using Google Colabs or Jupyter Notebook).- There’s a full section on machine learning - this is what many people consider the most fun part of data science. Spend a lot of time and effort on the earlier sections of the book, and you’ll be fully ready to jump into the machine learning chapters.- Don’t miss the great chapter towards the end of the book, titled “A Collection of Best Practices” - it covers some of the ethical challenges of working with data, ranging from the publication of misleading graphs to the controversial use of facial recognition technology.
Amazon Verified review Amazon
SB Dec 15, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As someone who has transitioned to Data Science from biology I can say that this book has been tremendous so far. I highly recommend it to anyone transitioning into Data Science from a different field!
Amazon Verified review Amazon
Rongyu Lin May 22, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It contains the most important knowledge that data analysts use in the daily work. Well organized and easy to understand. I would recommend this book to all my friends.
Amazon Verified review Amazon
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