Data Science Essentials in Python: Collect - Organize - by Dmitry Zinoviev

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By Dmitry Zinoviev

Go from messy, unstructured artifacts saved in SQL and NoSQL databases to a neat, well-organized dataset with this speedy reference for the busy info scientist. comprehend textual content mining, desktop studying, and community research; procedure numeric info with the NumPy and Pandas modules; describe and research info utilizing statistical and network-theoretical equipment; and spot real examples of information research at paintings. This one-stop resolution covers the basic facts technological know-how you wish in Python.

Data technological know-how is likely one of the fastest-growing disciplines when it comes to educational examine, pupil enrollment, and employment. Python, with its flexibility and scalability, is instantly overtaking the R language for data-scientific tasks. preserve Python data-science techniques at your fingertips with this modular, fast connection with the instruments used to obtain, fresh, study, and shop data.

This one-stop answer covers crucial Python, databases, community research, normal language processing, components of computing device studying, and visualization. entry dependent and unstructured textual content and numeric information from neighborhood documents, databases, and the web. set up, rearrange, and fresh the information. paintings with relational and non-relational databases, info visualization, and easy predictive research (regressions, clustering, and selection trees). See how general info research difficulties are dealt with. and check out your hand at your personal recommendations to quite a few 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 scholar, an entry-level info technology specialist changing from R to Python, or a pro Python developer who does not are looking to memorize each functionality and option.

What You Need:

You desire a first rate distribution of Python 3.3 or above that comes with at the very least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. a superb distribution that meets the necessities is Anaconda, on hand at no cost from in case you plan to establish your individual database servers, you furthermore mght want MySQL ( and MongoDB ( either applications are unfastened and run on home windows, Linux, and Mac OS.

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Working with Text Data • 34 Unit 14 Handling CSV Files CSV is a structured text file format used to store and move tabular or nearly tabular data. It dates back to 1972 and is a format of choice for Microsoft Excel, Apache OpenOffice Calc, and other spreadsheet software. S. government website that provides access to publicly available data, alone provides 12,550 data sets in the CSV format. A CSV file consists of columns representing variables and rows representing records. ) The fields in a record are typically separated by commas, but other delimiters, such as tabs (tab-separated values [TSV]), colons, semicolons, and vertical bars, are also common.

D", "{"] 2. Conversion of the words to all same-case characters (all uppercase or lowercase). 3. Elimination of stop words. Use the corpus stopwords and additional application-specific stop word lists as the reference. Remember that the words in stopwords are in lowercase. If you look up “THE” (definitely a stop word) in the corpus, it won’t be there. 4. Stemming (conversion of word forms to their stems). NLTK supplies two basic stemmers: a less aggressive Porter stemmer and a more aggressive Lancaster stemmer.

You can later change the table’s properties to accommodate your project needs. The command CREATE TABLE, followed by the new table name and a list of columns, creates a new table. For each column, define its name and data type (in this order). The most common MySQL data types are TINYINT, SMALLINT, INT, FLOAT, DOUBLE, CHAR, VARCHAR, TINYTEXT, TEXT, DATE, TIME, DATETIME, and TIMESTAMP. The following command creates the table employee with the columns empname (text of variable length), salary (floating point number), and hired (date).

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