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rimshah sabir

Aug 22 • 5 tweets • 3 min read

Strategies for Finding Data:

#pythonkachilla2 #pythonkachilla #pakdatasci #DataScience #MachineLearning #DataVisualization

Browsing Data Compendia:
This is a good strategy if you are not sure what types of variables exist or what data would be relevant for your project
- Select a data compendia
- Determine the subject area or data type that your topic or variable falls under
- Read the descriptions

Searching by Topic:
This guide provides several links to data sources by topic. These links are by no mean exhaustive, but can be a good place to start and can help you get a sense of who are some of the major collectors of data in your topic area.

Targeted Searching:
- Identify the home website of a relevant organization (i.e. the Centers for Disease Control is a major source of health data)
- Look for whether the page has a link called "Data" or "Statistics"

Alternatively, use a Google Advanced Search to search the website for data
- Use the site or domain search in the advanced search to limit to the website (i.e. cdc.gov)
- Add your keyword terms and add the terms (data OR statistics

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More from @SabirRimshah

rimshah sabir

@SabirRimshah

Aug 24

Part 2: Biopython

Key features and examples.
#pythonkachilla2 #pythonkachilla #pakdatasci #DataScience #MachineLearning #DataVisualization #codanics

A Thread!

Sequences.
A core concept in Biopython is the biological sequence, and this is represented by the Seq class. A Biopython Seq object is similar to a Python string in many respects: it supports the Python slice notation, can be concatenated with other sequences and is immutable.

Sequence annotation.
The SeqRecord class describes sequences along with information such as name description and features in the form of SeqFeature objects. Each SeqFeature object specifies the type of the feature and its location.

Read 8 tweets

rimshah sabir

@SabirRimshah

Aug 23

Biopython: an open-source collection of non-commercial Python tools for computational biology and bioinformatics It contains classes to represent biological sequences and sequence annotations
Thread
#pythonkachilla2 #pythonkachilla #pakdatasci #DataScience #MachineLearning

- History:
Began in 1999 and it was first released in July 2000. It was developed during a similar time frame and with analogous goals to other projects that added bioinformatics capabilities to their respective programming languages, including BioPerl, BioRuby and BioJava.

- Design:
Follows the conventions used by the Python programming language to make it easier for users familiar with Python. For example, Seq and SeqRecord objects can be manipulated via slicing, in a manner similar to Python's strings and lists. #DataVisualization

Read 4 tweets