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Python for Scientists QuickStart

  • Course Code: Py4SciQS2112
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This introductory course is for programmers and data analysts who want to take advantage of Python’s tools and infrastructure for working with numerical data.

Synopsis

Python for Scientists QuickStart is a brief ramp-up to get programmers going with the Python language, and to become familiar with some of the data science libraries available.

This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by graduated lab exercises.

Introduction to Python is a practical introduction to a working programming language. Students will immediately be able to start learning Python techniques for data science.

NOTE: This is not a data science concepts course. It covers tools available in Python for doing data science and how to use them, at an introductory level.

outlookTarget Audience

This introductory course is for programmers and data analysts who want to take advantage of Python’s tools and infrastructure for working with numerical data.

Prerequisites

While not required, basic skills with at any programming language will be helpful.

Course Materials

In addition to the course manual, students will receive two PDF-based quick references

  • Python 3
  • Pandas

Lab exercises

Most chapters have a short exercise to be done at the completion of each chapter, as well as extra labs for after-class practice. The labs increase in complexity as more advanced topics are introduced. Because of the short duration of this class, students will be expected to complete the additional labs on their own time.

Course Outline

  1. A brief overview of Python
    1. What is Python?
    1. Python Timeline
    1. Advantages/Disadvantages of Python
    1. Getting help with pydoc
  2. The Python environment
    1. Starting Python
    1. Using iPython
    1. Running a Python script
    1. IDEs and notebooks
  3. Jupyter notebooks
    1. What is a notebook
    1. Creating a notebook
    1. Documentation cells
    1. Code cells
    1. Capturing output
    1. Saving and exportingj
  4. Getting started
    1. Using variables
    1. Working with strings
    1. Working with numbers
    1. Converting among types
  5. Basic input and output
    1. Writing to the screen
    1. Formatting output
    1. Command line parameters
    1. Reading from the keyboard
  6. Flow control
    1. Conditional expressions
    1. Relational and Boolean operators
    1. While loops
  7. Lists and tuples
    1. Lists and list methods
    1. Tuples
    1. Indexing and slicing
    1. Iterating over a sequence
  8. Working with files
    1. File overview
    1. Opening a text file
    1. Reading/writing files
  9. Dictionaries
    1. About dictionaries
    1. Creating and updating dictionaries
    1. Iterating through a dictionary
  10. Functions and modules
    1. Defining functions
    1. Parameters and return values
    1. Variable scope
    1. Creating modules
    1. The import statement
    1. Module search path
  11. Exception handling
    1. All about Exceptions
    1. Using try/catch/else/finally
  12. Numpy and scipy
    1. The Python scientific stack
    1. Scipy resources
    1. Numpy arrays
    1. Working with numpy
  13. Pandas
    1. Creating a dataframe
    1. Simple operations
    1. Boolean indexes
    1. Selecting data
    1. Creating plots
    1. Exporting data to files
  14. Matplotlib
    1. Matplotlib overview
    1. Creating simple plots
    1. Modifying plots

Primary Hardware/Software Requirements

Operating System

  • Any (Windows, Mac, or Linux)

Software

  • Python (Anaconda distribution strongly suggested)
  • A Python-aware IDE (PyCharm Community Edition recommended; other good choices are Visual Studio Code or Eclipse)
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