Picture 1 of 1
Stock photo
Picture 1 of 1
Stock photo
Data Science Essentials in Python : Collect - Organize - Explore - Predict - Value by Dmitry Zinoviev (2016, Trade Paperback)
Rocky Mountain Textbooks (97275)
96.3% positive feedback
Price:
$7.99
+ $6.99 shipping
Returns:
30 days returns. Buyer pays for return shipping. If you use an eBay shipping label, it will be deducted from your refund amount.
Condition:
Thanks for viewing our Ebay listing! If you are not satisfied with your order, just contact us and we will address any issue. If you have any specific question about any of our items prior to ordering feel free to ask.
- Buy It NowData Science Essentials in Python: Collect - Organize - Explore - Predict - Val,
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
About this product
Product Identifiers
PublisherPragmatic Programmers, LLC, T.H.E.
ISBN-101680501844
ISBN-139781680501841
eBay Product ID (ePID)221991297
Product Key Features
Number of Pages226 Pages
Publication NameData Science Essentials in Python : Collect-Organize-Explore-Predict-Value
LanguageEnglish
SubjectDecision-Making & Problem Solving, Databases / Data Mining, Programming Languages / Python
Publication Year2016
TypeTextbook
AuthorDmitry Zinoviev
Subject AreaComputers, Business & Economics
FormatTrade Paperback
Dimensions
Item Height0.5 in
Item Weight15.2 Oz
Item Length9.2 in
Item Width7.6 in
Additional Product Features
Intended AudienceCollege Audience
Dewey Edition23
IllustratedYes
Dewey Decimal005.74
SynopsisGo 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 Need: 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., 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 Need: 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.
LC Classification NumberQA76.9.D32
All listings for this product
Be the first to write a review
