By Dmitry Zinoviev
Go from messy, unstructured artifacts kept in SQL and NoSQL databases to a neat, well-organized dataset with this fast reference for the busy facts scientist. comprehend textual content mining, desktop studying, and community research; approach numeric info with the NumPy and Pandas modules; describe and research facts utilizing statistical and network-theoretical equipment; and spot genuine examples of knowledge research at paintings. This one-stop answer covers the basic info technological know-how you wish in Python.
Data technology is without doubt one of the fastest-growing disciplines when it comes to educational learn, scholar enrollment, and employment. Python, with its flexibility and scalability, is readily overtaking the R language for data-scientific tasks. maintain Python data-science thoughts at your fingertips with this modular, fast connection with the instruments used to procure, fresh, research, and shop data.
This one-stop resolution covers crucial Python, databases, community research, common language processing, components of computer studying, and visualization. entry based and unstructured textual content and numeric info from neighborhood records, databases, and the web. manage, rearrange, and fresh the knowledge. paintings with relational and non-relational databases, information visualization, and easy predictive research (regressions, clustering, and selection trees). See how standard facts research difficulties are dealt with. and take a look at your hand at your individual ideas to a number of medium-scale tasks which are enjoyable to paintings on and glance reliable in your resume.
Keep this convenient speedy advisor at your part no matter if you are a pupil, an entry-level information technological know-how specialist changing from R to Python, or a professional Python developer who does not are looking to memorize each functionality and option.
What You Need:
You want a respectable distribution of Python 3.3 or above that incorporates a minimum of NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. an outstanding distribution that meets the necessities is Anaconda, on hand at no cost from www.continuum.io. if you happen to plan to establish your individual database servers, you furthermore mght desire MySQL (www.mysql.com) and MongoDB (www.mongodb.com). either applications are unfastened and run on home windows, Linux, and Mac OS.
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Additional resources for Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value (The Pragmatic Programmers)
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value (The Pragmatic Programmers) by Dmitry Zinoviev