文件列表:

batch_four/ (0, 2018-06-09)
batch_four/.env (175, 2018-06-09)
batch_four/datetime/ (0, 2018-06-09)
batch_four/datetime/hello_datetime.py (609, 2018-06-09)
batch_four/datetime/pandas_datetime.py (217, 2018-06-09)
batch_four/datetime/parse_util.py (319, 2018-06-09)
batch_four/datetime/strings_to_datetime.py (733, 2018-06-09)
batch_four/datetime/time_basics_pandas.py (545, 2018-06-09)
batch_four/datetime/time_delta.py (418, 2018-06-09)
batch_four/datetime/time_delta_add_substract.py (185, 2018-06-09)
batch_four/docker-compose.yml (170, 2018-06-09)
batch_four/jupyter/ (0, 2018-06-09)
batch_four/jupyter/Dockerfile (304, 2018-06-09)
batch_four/jupyter/requirement.txt (0, 2018-06-09)
batch_four/project/ (0, 2018-06-09)
batch_four/project/usgs_earthquakes_2014.csv (19441088, 2018-06-09)
batch_four/requirement.txt (0, 2018-06-09)
batch_four/session-1/ (0, 2018-06-09)
batch_four/session-1/addition.py (249, 2018-06-09)
batch_four/session-1/comments.py (268, 2018-06-09)
batch_four/session-1/hello.py (154, 2018-06-09)
batch_four/session-1/hello_multiple.py (167, 2018-06-09)
batch_four/session-1/interview.py (266, 2018-06-09)
batch_four/session-1/mathematics.py (187, 2018-06-09)
batch_four/session-1/variables.py (434, 2018-06-09)
batch_four/session-2/ (0, 2018-06-09)
batch_four/session-2/Session-2.pdf (1173632, 2018-06-09)
batch_four/session-2/addition.py (253, 2018-06-09)
batch_four/session-2/dictionaries.py (367, 2018-06-09)
batch_four/session-2/for_demo.py (187, 2018-06-09)
batch_four/session-2/for_demo_adv.py (207, 2018-06-09)
batch_four/session-2/for_demo_adv_nospam.py (275, 2018-06-09)
batch_four/session-2/if_demo.py (302, 2018-06-09)
batch_four/session-2/if_elif_demo.py (319, 2018-06-09)
batch_four/session-2/if_else_demo.py (259, 2018-06-09)
batch_four/session-2/interview.py (266, 2018-06-09)
batch_four/session-2/lab_one.py (595, 2018-06-09)
batch_four/session-2/lists_demo.py (522, 2018-06-09)
... ...

# Python for Data Science This repository has documentation about courses conducted for Python Programming Language. Typical audience for these courses were Software Developers, Data Science, Machine Learning Students. Each course typically conducted for 30 to 40 hrs. Each Directory contains the batches of courses conducted for different set of students at different times. The directories contain document, lecture notes (in the form of PDF Presentaton), assignments, code which was used during demonstrations. Topics covered in most of the branches are: * Introduction * Hello Python * Comments * Mathematics * Variables * Data Types * Python Lists * Python Strings * Loops, Handling Conditions * Handling Errors * Python Functions * Module Development * Understanding a few Important Modules - sys * Package Development * Developing Classes * Iterators * Regular Expressions * Dealing with Input and Output * Files and Directories * Getting Data from Internet * Database * Introduction to SQLite * Groupby etc * Data Science Libraries * Numpy * Pandas * Dealing with Data * Handling Missing Data * Merging Data Sets, Grouping them

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