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Fast Track to Python for Data Science | Introduction to Python for Data Science

Price
2,100 USD
3
TTPS4873
Classroom Training, Online Training
Open Source

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  • Date: 17-Jun-2024 to 19-Jun-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,100 USD
  • Date: 29-Jul-2024 to 31-Jul-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,100 USD
  • Date: 9-Sep-2024 to 11-Sep-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,100 USD
  • Date: 21-Oct-2024 to 23-Oct-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,100 USD
  • Date: 2-Dec-2024 to 4-Dec-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,100 USD
  • Date: 13-Jan-2025 to 15-Jan-2025
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,100 USD
View Global Schedule

Course Description

Overview

Fast Track to Python for Data Science is a three-day, hands-on course that introduces data analysts and business analysts to the Python programming language, as it’s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice.

Students will explore basic Python syntax and concepts applicable to using Python to work with data. The course begins with quick introduction to Python, with demonstrations of both script-based and web notebook-based Python, and then dives into the essentials of Python necessary to a data scientist. The tail end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas and Matplotlib. Students will explore the concepts and work with large data sets in a workshop style lab. This class is hands-on and includes basic programming labs that introduce students to basic Python syntax and concepts applicable to using Python to work with data, AI and machine learning basics.

Students will explore basic Python syntax and concepts applicable to using Python to work with data. The course begins with quick introduction to Python, with demonstrations of both script-based and web notebook-based Python, and then dives into the essentials of Python necessary to a data scientist. The tail end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas and Matplotlib. Students will explore the concepts and work with large data sets in a workshop style lab.

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students will learn to leverage core Python scripting for data science skills using the most current and efficient skills and techniques.

Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore:
  • How to work with Python interactively in web notebooks
  • The essentials of Python scripting
  • Key concepts necessary to enter the world of Data Science via Python

Objectives


 

Audience

This introductory-level course is geared for data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks.
 

Prerequisites

    While there are no specific programming prerequisites, students should be comfortable working with files and folders and should not be afraid of the command line and basic scripting. This is for attendees new to Python.
     

Topics

An Overview of Python
  • Why Python?
  • Python in the Shell
  • Python in Web Notebooks (iPython, Jupyter, Zeppelin)
  • Demo: Python, Notebooks, and Data Science
Getting Started
  • Using variables
  • Builtin functions
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • Command line parameters
  • Running standalone scripts under Unix and Windows
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
Sorting
  • The sorted() function
  • Alternate keys
  • Lambda functions
  • Sorting collections
  • Using operator.itemgetter()
  • Reverse sorting
Errors and Exception Handling
  • Syntax errors
  • Exceptions
  • Using try/catch/else/finally
  • Handling multiple exceptions
  • Ignoring exceptions
Essential Demos
  • 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
numpy
  • numpy basics
  • Creating arrays
  • Indexing and slicing
  • Large number sets
  • Transforming data
  • Advanced tricks
Python and Data Science
  • Data Science Essentials
  • Working with Python in Data Science
Working with Pandas
  • pandas overview
  • Dataframes
  • Reading and writing data
  • Data alignment and reshaping
  • Fancy indexing and slicing
  • Merging and joining data sets
Time Permitting matplotlib
  • Creating a basic plot
  • Commonly used plots
  • Ad hoc data visualization
  • Advanced usage
  • Exporting images
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