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Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value
Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python.
Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data.
This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume.
Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option.
What You
You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.
Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data.
This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume.
Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option.
What You
You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.
226 pages, Paperback
Published September 13, 2016
5 people are currently reading
53 people want to read
About the author
Dmitry Zinoviev
25 books7 followersDmitriy is full professor of Mathematics & Computer Science in Suffolk University / Boston
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Displaying 1 - 6 of 6 reviews
February 13, 2017
While delivering extreme values as a fast paced tour of the tools available to a python engineer in programming, this book fails to explain theory more in depth, and will require substantial time invested in coding along side it.
I assigned this book to a small group of novice python engineers to read through, and for the most part they found themselves lost along the way with the greatest value being knowing whats available.
If you are mid-level or higher already in python and want to acclimate or just know about what data tools may be available to you within the context of python this book offers carefully written readable code samples on numerous popular libraries with basic applications.
An excellent quick reference to get you started down the right path.
I assigned this book to a small group of novice python engineers to read through, and for the most part they found themselves lost along the way with the greatest value being knowing whats available.
If you are mid-level or higher already in python and want to acclimate or just know about what data tools may be available to you within the context of python this book offers carefully written readable code samples on numerous popular libraries with basic applications.
An excellent quick reference to get you started down the right path.
July 8, 2018
I am unsure of the audience here. Is it for Data Scientists that want to learn Python or Pythonistas wanting to learn Data Science? While it seems to be more geared towards the latter, it falls short for either audience.
May 11, 2020
A quick overview on the essential tools in Python for data science. The book is great in showing you what tools exist and their basic usage, but it lacks the necessary details to solve problems on your own using them.
September 7, 2018
This provided me a useful overview of Python data science capabilities, but it does not provide much depth.
March 18, 2021
A helpful book containing useful tricks for working with data in Python.
March 1, 2025
Really useful as a guide to what's possible with practical examples and exercises. I'm using this right now to upgrade from pure Excel chops to something more scalable and fully featured!
Displaying 1 - 6 of 6 reviews