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
[https://www.timeanddate.com/worldclock/converter.html?iso=20200121T013000&p1=234]
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
*
STDIO
*
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
culture
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21/01/2020 Last update