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.
He has been teaching at Suffolk University in Boston, MA since 2001..
His research interests include computer simulation and modeling, network science, social network analysis, and digital humanities.
About the Author: Dmitry Zinoviev has an MS in Physics from Moscow State University and a PhD in Computer Science from Stony Brook University.
Both packages are free and run on Windows, Linux, and Mac OS.
If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com).
A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io.
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.
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.
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.
See how typical Data analysis problems are handled.
Work with relational and non-relational databases, Data visualization, and simple predictive analysis (regressions, clustering, and decision trees).
Arrange, rearrange, and clean the data.
Access structured and unstructured text and numeric Data from local files, databases, and the Internet.
This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization.
Keep Python data-Science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data.
Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects.
Data Science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment.
This one-stop solution covers the essential Data Science you need in Python.
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.
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
