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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.
Unlimited Duration
March 5, 2021
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
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- 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
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- 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
- 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
- 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
- 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
- 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
- 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
- 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
- What is SciPy do? 00:00:00
- Some useful functions 00:00:00
- SciPy subpackages 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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