$319.89 – $425.98

Weekdays Only Python Training in Naples | Introduction to Python for beginn...

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Weekdays Only Python training is a LIVE Instructor led training delivered from January 20, 2020 - February 12, 2020 for 16 hours over 4 weeks, 8 sessions, 2 sessions per week, 2 hours per session.

  • All Published Ticket Prices are in US Dollars
  • The course will be taught in English language only.

Weekly Schedule
  • 4 Weeks | Monday, Wednesday Every Week
  • 5:30 PM - 7:30 PM US Pacific time each day
  • January 20 - February 12, 2020 US Pacific time

Please check your local date and time for first session

Features and Benefits
  • 4 weeks, 8 sessions, 16 hours of total Instructor-led LIVE training
  • Training material, instructor handouts and access to useful resources on the cloud provided
  • Practical Hands-on Lab exercises provided
  • Actual code and scripts provided
  • Real-life Scenarios

Course Objectives
  • Complete knowledge of Python
  • Learn how to use lists, tuples, loops, decision statement, etc. in python
  • Build packages in Python
  • Working with Exception handling, Inheritance
  • Work independently in Project with scripting and Automation

What are the prerequisites to learn Python?

The course can be taken by any IT professional having basic knowledge of:

  • Unix or Windows Operating System
  • Any Programming Language
  • Computer concepts

Who can take Python Course?
  • Any Professional who is interested in building career in python web development/ software automation /Data Analytics.
  • Any one who wants to learn python programming for any purpose

Python Online Training Course Curriculum

1. Getting Started with Python

  • Python Overview

  • About Interpreted Languages

  • Advantages/Disadvantages of Python pydoc

  • Starting Python

  • Interpreter PATH

  • Using the Interpreter

  • Running a Python Script

  • Python Scripts on UNIX/Windows

  • Python Editors and IDEs.

  • Using Variables

  • Keywords

  • Strings Different Literals

  • Math Operators and Expressions

  • Writing to the Screen

  • String Formatting

  • Command Line Parameters and Flow Control

  • Built-in Functions

2. Sequences and File Operations

  • Lists

  • Tuples

  • Indexing and Slicing

  • Iterating through a Sequence

  • Functions for all Sequences

  • Using Enumerate()

  • Operators and Keywords for Sequences

  • Dictionaries and Sets

  • The xrange() function

  • List Comprehensions

  • Generator Expressions

3. Deep Dive – Functions Sorting Errors and Exception Handling

  • Functions

  • Function Parameters

  • Global Variables

  • Variable Scope and Returning Values. Sorting

  • Alternate Keys

  • Lambda Functions

  • Sorting Collections of Collections

  • Sorting Dictionaries

  • Sorting Lists in Place

  • Errors and Exception Handling

  • Handling Multiple Exceptions

  • The Standard Exception Hierarchy

  • Using Modules

  • The Import Statement

  • Module Search Path

  • Package Installation Ways

4. Regular Expressions It’s Packages and Object Oriented Programming in Python

  • The Sys Module

  • Interpreter Information


  • Launching External Programs

  • Paths Directories and Filenames

  • Walking Directory Trees

  • Math Function

  • Random Numbers

  • Dates and Times

  • Zipped Archives

  • Introduction to Python Classes

  • Defining Classes

  • Initializers

  • Instance Methods

  • Properties

  • Class Methods and DataStatic Methods

  • Private Methods and Inheritance

  • Module Aliases and Regular Expressions.

5. Debugging, Databases and Project Skeletons

  • Debugging

  • Dealing with Errors

  • Using Unit Tests

  • Project Skeleton

  • Required Packages

  • Creating the Skeleton

  • Project Directory

  • Final Directory Structure

  • Testing your Setup

  • Using the Skeleton

  • Creating a Database with SQLite 3

  • CRUD Operations

  • Creating a Database Object

6. Machine Learning Using Python

  • Introduction to Machine Learning

  • Areas of Implementation of Machine Learning

  • Why Python

  • Major Classes of Learning Algorithms

  • Supervised vs Unsupervised Learning

  • Learning NumPy

  • Learning Scipy

  • Basic plotting using Matplotlib

  • Machine Learning application

7. Supervised and Unsupervised Learning

  • Classification Problem

  • Classifying with k-Nearest Neighbours (kNN)

  • Algorithm

  • General Approach to kNN

  • Building the Classifier from Scratch

  • Testing the Classifier

  • Measuring the Performance of the Classifier

  • lustering Problem

8. Scikit and Introduction to Hadoop

  • Introduction to Scikit-Learn

  • Inbuilt Algorithms for Use

  • What is Hadoop and why it is popular

  • Distributed Computation and Functional Programming

  • Understanding MapReduce Framework Sample

  • Map Reduce Job Run

9. Hadoop & Python

  • PIG and HIVE Basics

  • Streaming Feature in Hadoop

  • Map Reduce Job Run using Python

  • Writing a PIG UDF in Python

  • Writing a HIVE UDF in Python

  • Pydoop and MRjob Basics

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