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Principles of Data Science: Learn the techniques and math you need to start making sense of your data: Mathematical techniques and theory to succeed in data-driven industries
Key FeaturesEnhance your knowledge of coding with data science theory for practical insight into data science and analysisMore than just a math class, learn how to perform real-world data science tasks with R and PythonCreate actionable insights and transform raw data into tangible valueBook DescriptionNeed to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas.
With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
What you will learnGet to know the five most important steps of data scienceUse your data intelligently and learn how to handle it with careBridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable resultsBuild and evaluate baseline machine learning modelsExplore the most effective metrics to determine the success of your machine learning modelsCreate data visualizations that communicate actionable insightsRead and apply machine learning concepts to your problems and make actual predictionsAbout the AuthorSinan Ozdemir is a data scientist, startup founder, and educator living in the San Francisco Bay Area with his dog, Charlie; cat, Euclid; and bearded dragon, Fiero. He spent his academic career studying pure mathematics at Johns Hopkins University before transitioning to education. He spent several years conducting lectures on data science at Johns Hopkins University and at the General Assembly before founding his own start-up, Legion Analytics, which uses artificial intelligence and data science to power enterprise sales teams.
After completing the Fellowship at the Y Combinator accelerator, Sinan has spent most of his days working on his fast-growing company, while creating educational material for data science.
Table of ContentsHow to Sound Like a Data ScientistTypes of DataThe Five Steps of Data ScienceBasic MathematicsImpossible or Improbable – A Gentle Introduction to ProbabilityAdvanced ProbabilityBasic StatisticsAdvanced StatisticsCommunicating DataHow to Tell If Your Toaster Is Learning – Machine Learning EssentialsPredictions Don't Grow on Trees – or Do They?Beyond the EssentialsCase Studies
With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
What you will learnGet to know the five most important steps of data scienceUse your data intelligently and learn how to handle it with careBridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable resultsBuild and evaluate baseline machine learning modelsExplore the most effective metrics to determine the success of your machine learning modelsCreate data visualizations that communicate actionable insightsRead and apply machine learning concepts to your problems and make actual predictionsAbout the AuthorSinan Ozdemir is a data scientist, startup founder, and educator living in the San Francisco Bay Area with his dog, Charlie; cat, Euclid; and bearded dragon, Fiero. He spent his academic career studying pure mathematics at Johns Hopkins University before transitioning to education. He spent several years conducting lectures on data science at Johns Hopkins University and at the General Assembly before founding his own start-up, Legion Analytics, which uses artificial intelligence and data science to power enterprise sales teams.
After completing the Fellowship at the Y Combinator accelerator, Sinan has spent most of his days working on his fast-growing company, while creating educational material for data science.
Table of ContentsHow to Sound Like a Data ScientistTypes of DataThe Five Steps of Data ScienceBasic MathematicsImpossible or Improbable – A Gentle Introduction to ProbabilityAdvanced ProbabilityBasic StatisticsAdvanced StatisticsCommunicating DataHow to Tell If Your Toaster Is Learning – Machine Learning EssentialsPredictions Don't Grow on Trees – or Do They?Beyond the EssentialsCase Studies
- GenresNonfictionProgramming
390 pages, Kindle Edition
Published December 16, 2016
18 people are currently reading
97 people want to read
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Displaying 1 - 4 of 4 reviews
October 31, 2018
Great introduction book to cover the important concepts and algorithms in Data Science and Machine Learning at the entry level, with the hands-on examples and practices. The descriptions and explanations are easy to understand and follow. Not too much "mathematic", but of course it requires the basic ideas of possibility and statistics. But in general the book is suitable and friendly for the beginners.
The only problem is the example code was written by Python2. It requires the reader/learner has a lot of Python knowledge and programming skills to fix the problems made by the differences between Python2 and Python3 before running the code.
The only problem is the example code was written by Python2. It requires the reader/learner has a lot of Python knowledge and programming skills to fix the problems made by the differences between Python2 and Python3 before running the code.
April 21, 2018
Excellent introduction - not perfect for sure, but explainations are simple, clear, engaging and often funny. Covers types of data and some very basics of: mathematics, probability, statistics, data visualization, machine learning and prediction methods.
March 3, 2022
A great book! The only downside is the code which is written in Python 2 and sometimes needs a lot of modifications in order to run. Additionally, some libraries used in the book are outdated. A must read if you want a great introduction to the data science field…
March 20, 2018
Good book with excellent descriptions of different algorithms
Displaying 1 - 4 of 4 reviews