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Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data
Learn to effectively manage data and execute data science projects from start to finish using Python
Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook DescriptionPractical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.
The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.
As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.
By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.
What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is forThe book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.
The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
Table of ContentsIntroduction to Data ScienceGetting Started with PythonSQL and Built-in File Handling Modules in PythonLoading and Wrangling Data with Pandas and NumPyExploratory Data Analysis and VisualizationData Wrangling Documents and SpreadsheetsWeb ScrapingProbability, Distributions, and SamplingStatistical Testing for Data SciencePreparing Data for Machine
Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook DescriptionPractical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.
The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.
As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.
By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.
What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is forThe book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.
The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
Table of ContentsIntroduction to Data ScienceGetting Started with PythonSQL and Built-in File Handling Modules in PythonLoading and Wrangling Data with Pandas and NumPyExploratory Data Analysis and VisualizationData Wrangling Documents and SpreadsheetsWeb ScrapingProbability, Distributions, and SamplingStatistical Testing for Data SciencePreparing Data for Machine
620 pages, Kindle Edition
Published September 30, 2021
9 people are currently reading
8 people want to read
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Displaying 1 - 2 of 2 reviews
December 14, 2021
Packt sent me a copy of this book to review. It's 621 pages with the best and worst characteristics of a typical data science boot camp: very broad, necessarily shallow, frequently not quite perfect. Even an imperfect map can tell you a lot about the territory, and it could be the right book for you.
George has pulled together a lot of material, some of it good. He includes introductory Python and command line, enough SQL to be confused about SQL, examples with Bitcoin prices, an idiosyncratic survey of visualization, web scraping, statistics, and the big machine learning models, including the big three boosted tree algorithms, which I appreciate. He includes some NLP, and even some on ethics.
George's own list of omissions (page 571) illustrates what he thinks is almost in scope:
* Recommender systems
* Networks and graph analysis
* Machine learning explainability
* Test-driven development (TDD)
* Reinforcement learning
* Neural networks
Maybe the moral is that “data science” is too big a topic for one book. Trying to pack so much in has a cost. Here's the complete section on “Paired t- and z-tests”:
"""
One last type of t- or z-test is the paired test. This is for paired samples, like before-and-after treatments. For example, we could measure the blood pressure of people before and after taking a medication to see if there is an effect. A function that can be used for this is scipy.stats.ttest_rel, which can be used like this:
scipy.stats.ttest_rel(before, after)
This will return a t-statistic and p-value like with other scipy t-test functions.
"""
If you've never heard of a paired t-test before, it's great this book tells you about it. You can start to ask questions like: Why is this a separate test? Does it have some advantage over a regular t-test? Hopefully you also question some parts of the book, as when Bayesian methods are dismissed as “much more complex to implement than a t-test.”
This is a map that can point you in a lot of interesting directions, which is valuable!
George has pulled together a lot of material, some of it good. He includes introductory Python and command line, enough SQL to be confused about SQL, examples with Bitcoin prices, an idiosyncratic survey of visualization, web scraping, statistics, and the big machine learning models, including the big three boosted tree algorithms, which I appreciate. He includes some NLP, and even some on ethics.
George's own list of omissions (page 571) illustrates what he thinks is almost in scope:
* Recommender systems
* Networks and graph analysis
* Machine learning explainability
* Test-driven development (TDD)
* Reinforcement learning
* Neural networks
Maybe the moral is that “data science” is too big a topic for one book. Trying to pack so much in has a cost. Here's the complete section on “Paired t- and z-tests”:
"""
One last type of t- or z-test is the paired test. This is for paired samples, like before-and-after treatments. For example, we could measure the blood pressure of people before and after taking a medication to see if there is an effect. A function that can be used for this is scipy.stats.ttest_rel, which can be used like this:
scipy.stats.ttest_rel(before, after)
This will return a t-statistic and p-value like with other scipy t-test functions.
"""
If you've never heard of a paired t-test before, it's great this book tells you about it. You can start to ask questions like: Why is this a separate test? Does it have some advantage over a regular t-test? Hopefully you also question some parts of the book, as when Bayesian methods are dismissed as “much more complex to implement than a t-test.”
This is a map that can point you in a lot of interesting directions, which is valuable!
October 21, 2023
For me the book was too broad and too shallow to understand many of the covered topics. A more focused definition of Data Science and a selection of only a handful of topics would have been more useful. As it is, the book gives you keywords, that you have to explore on your own to understand what the topics is about and how to apply it to your problem.
Displaying 1 - 2 of 2 reviews