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Applied Python for Data Science

ratings 

Geared for scientists and engineers with limited practical programming background or experience, Applied Python for Data Science is a hands-on introductory-level course that provides a ramp-up to using Python for scientific and mathematical computing.

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Course Access

Unlimited Duration

Last Updated

March 5, 2021

Students Enrolled

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Posted by

ashar hafeez

Certification

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises.In this course you will learn about:

· Create and run basic programs

· Design and code modules and classes

· Implement and run unit tests

· Use benchmarks and profiling to speed up programs

· Process XML and JSON

· Manipulate arrays with NumPy

· Get a grasp of the diversity of subpackages that make up SciPy

· Use Series and Dataframes with Pandas

· Create plots with Matplotlib

Optional / Upon Request: Use Jupyter notebooks for ad hoc calculations, plots, and what-if?

Course Curriculum

    • About Python 00:00:00
    • Starting Python 00:00:00
    • Using the interpreter 00:00:00
    • Running a Python script 00:00:00
    • Python scripts on Unix/Windows 00:00:00
    • Using the Spyder editor 00:00:00
    • Using variables 00:00:00
    • Builtin functions 00:00:00
    • Strings 00:00:00
    • Numbers 00:00:00
    • Converting among types 00:00:00
    • Writing to the screen 00:00:00
    • String formatting 00:00:00
    • Command line parameters 00:00:00
    • About flow control 00:00:00
    • White space 00:00:00
    • Conditional expressions (if,else) 00:00:00
    • Relational and Boolean operators 00:00:00
    • While loops 00:00:00
    • Alternate loop exits 00:00:00
    • About sequences 00:00:00
    • Lists and tuples 00:00:00
    • Indexing and slicing 00:00:00
    • Iterating through a sequence 00:00:00
    • Sequence functions, keywords, and operators 00:00:00
    • List comprehensions 00:00:00
    • Generator expressions 00:00:00
    • Nested sequences 00:00:00
    • File overview 00:00:00
    • Opening a text file 00:00:00
    • Reading a text file 00:00:00
    • Writing to a text file 00:00:00
    • Raw (binary) data 00:00:00
    • Creating dictionaries 00:00:00
    • Iterating through a dictionary 00:00:00
    • Creating sets 00:00:00
    • Working with sets 00:00:00
    • Returning values 00:00:00
    • Types of function parameters 00:00:00
    • Variable scoping 00:00:00
    • Documentation best practices 00:00:00
    • Creating and importing modules 00:00:00
    • Organizing modules into packages 00:00:00
    • Syntax errors 00:00:00
    • Exceptions 00:00:00
    • Using try/catch/else/finally 00:00:00
    • Handling multiple exceptions 00:00:00
    • Ignoring exceptions 00:00:00
    • The sys module 00:00:00
    • Launching external programs 00:00:00
    • Walking directory trees 00:00:00
    • Grabbing web pages 00:00:00
    • Sending e-mail 00:00:00
    • Paths, directories, and filenames 00:00:00
    • Dates and times 00:00:00
    • Zipped archives 00:00:00
    • The Zen of Python 00:00:00
    • Common idioms 00:00:00
    • Named tuples 00:00:00
    • Useful types from collections 00:00:00
    • Sorting 00:00:00
    • Lambda functions 00:00:00
    • List comprehensions 00:00:00
    • Generator expressions 00:00:00
    • String formatting 00:00:00
    • Defining classes 00:00:00
    • Constructors 00:00:00
    • Instance methods and data 00:00:00
    • Attributes 00:00:00
    • Inheritance 00:00:00
    • Multiple inheritance 00:00:00
    • Analyzing programs with pylint 00:00:00
    • Creating and running unit tests 00:00:00
    • Debugging applications 00:00:00
    • Benchmarking code 00:00:00
    • Profiling applications 00:00:00
    • The openpyxl module 00:00:00
    • Reading an existing spreadsheet 00:00:00
    • Creating a spreadsheet from scratch 00:00:00
    • Modifying an existing spreadsheet 00:00:00
    • Using ElementTree 00:00:00
    • Creating a new XML document 00:00:00
    • Parsing XML 00:00:00
    • Finding by tags and XPath 00:00:00
    • Parsing JSON into Python 00:00:00
    • Parsing Python into JSON 00:00:00
    • Working with CSV 00:00:00
    • iPython features 00:00:00
    • using Jupyter notebooks 00:00:00
    • Terminal and GUI shells 00:00:00
    • Creating and using notebooks 00:00:00
    • NumPy basics 00:00:00
    • Creating arrays 00:00:00
    • Indexing and slicing 00:00:00
    • Large number sets 00:00:00
    • Transforming data 00:00:00
    • Advanced tricks 00:00:00
    • What is SciPy do? 00:00:00
    • Some useful functions 00:00:00
    • SciPy subpackages 00:00:00
    • Pandas overview 00:00:00
    • Dataframes 00:00:00
    • Reading and writing data 00:00:00
    • Data alignment and reshaping 00:00:00
    • Fancy indexing and slicing 00:00:00
    • Merging and joining data sets 00:00:00
    • Creating a basic plot 00:00:00
    • Commonly used plots 00:00:00
    • Ad hoc data visualization 00:00:00
    • Advanced usage 00:00:00
    • Exporting images 00:00:00

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