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Python Primer for Data Scientists | Quickstart to Python Basics

Course Code: 1249
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$1695

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Overview

The Python Primer for Data Scientists course introduces participants to core concepts of writing and executing Python scripts along with working with Python in web notebooks in an interactive manner. The course also covers how to combine and apply the skills learnt in the course to work data science libraries, such as Panda, SciKit, NumPy and Matplotlib. The course is an introductory course targeted towards data scientists.

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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
  • Flow control
  • Sequences and arrays
  • The standard library
  • Essentials of data science in Python
  • Working with dates and times

Outline

Module 1: An overview of Python

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

There are no prerequisites for this course.

Who Should Attend

The course is highly recommended for –

  • Business analysts
  • Data analysts
  • Data science enthusiasts

Interested in this course? Let’s connect!

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