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Hands-on Python primer for data scientists

Course Code: 1197
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$1495

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Overview

This two day course introduces data analysts and business analysts, or anyone interested in data science to the Python programming language. The course aims to provide a baseline understanding of core concepts that can serve as a knowledge base to follow up with more in-depth training and real-world practice.

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Trivera

Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 5 days

Live Virtual Classroom
Duration: 5 days

What You'll learn

  • Overview of Python
  • Demonstration of script-based and web notebook-based Python
  • Essentials of Python for a data scientist
  • Key data science libraries, such as NumPy, Pandas, Matplotlib
  • Essentials of Python scripting

Outline

Module 1: An overview of Python

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

Module 2: Getting started

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

Module 3: Flow control

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

Module 4: Sequences, arrays, dictionaries and sets

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

Module 5: Working with files

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

Module 6: Functions

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

Module 7: Essential demos

  • Sorting
  • Exceptions
  • Importing modules
  • Classes
  • Regular expressions

Module 8: The standard library

  • Math functions
  • The string module

Module 9: Dates and times

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

Module 10: Python and data science

  • Data science essentials
  • Pandas overview
  • NumPy overview
  • SciKit overview
  • MatPlotLib overview
  • Working with Python in data science
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Prerequisites

Participants need to have a basic understanding of data science and a working knowledge of Microsoft Excel.

Who Should Attend

The course is highly recommended for –

  • Data analysts
  • Business analysts
  • Anyone else interested in data science

Interested in this course? Let’s connect!

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