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Python for Data Science Primer

SS Course: GK100668

Course Overview

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This course introduces data analysts, business analysts, and anyone interested in Data Science to the Python programming language as it s often used in Data Science in web notebooks. The goal is to provide you with a baseline understanding of core concepts that will serve as a platform of knowledge to follow-up with more in-depth training and real-world practice.

The course begins with a quick overview of Python with demonstrations of both script-based and web notebook-based Python and then you ll dive into the essentials of Python necessary for a data scientist. The end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas, and Matplotlib.

This class is hands-on and includes light programming labs that introduce students to basic Python syntax and concepts applicable to using Python to work with data.

                                                                  

Scheduled Classes

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03/04/24 - GVT - Virtual Classroom - Virtual Instructor-Led
03/07/24 - GVT - Virtual Classroom - Virtual Instructor-Led
05/13/24 - GVT - Virtual Classroom - Virtual Instructor-Led
07/15/24 - GVT - Virtual Classroom - Virtual Instructor-Led

Outline

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An Overview of Python

  • Why Python?
  • Python in the Shell
  • Python in Web Notebooks (iPython, Jupyter, and Zeppelin)
  • Demo: Python, Notebooks, and Data Science

Getting Started

  • Using variables
  • Built-in functions
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • Command line parameters

Flow Control

  • About flow control
  • White space
  • Conditional expressions
  • Relational and Boolean operators
  • While loops
  • Alternate loop exits

Sequences, Arrays, Dictionaries, and Sets

  • About sequences
  • Lists and list methods
  • Tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Sequence functions, keywords, and operators
  • List comprehensions
  • Generator Expressions
  • Nested sequences
  • Working with Dictionaries
  • Working with Sets

Working with files

  • File overview
  • Opening a text file
  • Reading a text file
  • Writing to a text file
  • Reading and writing raw (binary) data

Functions

  • Defining functions
  • Parameters
  • Global and local scope
  • Nested functions
  • Returning values

Essential Demos

  • Sorting
  • Exceptions
  • Importing Modules
  • Classes
  • Regular Expressions

The standard library

  • Math functions
  • The string module

Dates and times

  • Working with dates and times
  • Translating timestamps
  • Parsing dates from text
  • Formatting dates
  • Calendar data

Python and Data Science

  • Data Science Essentials
  • Pandas Overview
  • NumPy Overview
  • SciKit Overview
  • MatPlotLib Overview
  • Working with Python in Data Science

    Prerequisites

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    No prior programming experience is required.

      Who Should Attend

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      Business Analysts, Data Analysts, and anyone interested in data science who is comfortable working with numerical data in Excel or other spreadsheet environments.