Mastering Python

Course Features

Duration: 40 hours
Skill Level: All level
Language: English

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. This course will cover both basic and advance concepts of Python like writing python scripts, sequence and file operations in python, Machine Learning in Python, Web Scraping, Map Reduce in Python, Hadoop Streaming, Python UDF for Pig and Hive. You will also go through important and most widely used packages like pydoop, pandas, scikit, numpy scipy etc.

TARGET AUDIENCE

This course is exclusively designed for professionals aspiring to make a career in Big Data Analytics using Python Software Professionals
Analytics Professionals
ETL developers
Project Managers
Testing Professionals are the key beneficiaries of this course
Other professionals who are looking forward to acquire a solid foundation of this widely-used open source general-purpose scripting language, can also opt for this course

OBJECTIVES

At the end of the course, students will be able to:

Master the Basic and Advanced Concepts of Python
Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
Master the Concepts of Sequences and File operations
Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
Gain expertise in machine learning using Python and build a Real Life Machine Learning application
Understand the supervised and unsupervised learning and concepts of Scikit-Learn
Master the concepts of MapReduce in Hadoop
Learn to write Complex MapReduce programs
Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
Master the concepts of Web scraping in Python
Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience

1) Chapter 1: Installation and Setup (Part 1: Basic Use)

Where to find Python
Installing Python
Testing your installation

2) Chapter 2: Getting Started

What is Python?
Why Python?
Hello World interactive
Hello World command line
Hello World in a file
The print statement
Comments
Line structure
When Things Go Wrong Raising Exceptions

3) Chapter 3: Variables and Data Types

Variables
Identifiers
Binding
Data Types
Basic Numbers
Basic Strings
Using Tuples and Sequences
Using and modifying Lists
Using Dictionaries
Sequence slicing

4) Chapter 4: Operators

Basic Numeric Operators
Basic Arithmetic Operators
Exponentiation
Bitwise
Augmented Assignment
Truncating Division
Comparison and Logical
Chaining
Short-circuiting
The Range Function

5) Chapter 5: Control Structures

The If Statement
For loops
While loops

6) Chapter 6: Functions

Built-in functions
Defining functions
Using function objects
Passing arguments
Returning values
Function overloading
Named parameters
Default parameters
Function scope rules
Using the global statement
Pass by reference or value

7) Chapter 7: Exception Handling

The exception mechanism
Using the else clause
Using the finally clause
Using the raise statement

8) Chapter 8: Simple File I/O

Opening files
Reading and writing files
Reading whole files
Using a file interator
Reading and writing binary data

9) Chapter 9: Getting things done with modules and libraries

What is a module
Adding module names to your namespace
Finding modules
Standard modules
The sys module
Stdout, stderr, stdin
Exit
The ose module
Math with modules
Dates and Times

10) Chapter 1: Intermediate variables and operators (Part 2: Intermediate and Advanced Use)

Advanced variables and datatypes
List comprehensions
Pass statement
Print >>
Ternary operator
Docstrings and Pydoc
Using enumerate

11) Chapter 2: Strings

Strings and regular expressions
Using str() and repr()
Raw and Unicode strings
The re module

12) Chapter 3: Functions

Functions
Varargs with * and **
Defining vararg functions
Expanding sequences
Lambda functions
Embedding tests with __main__

13) Chapter 4: Classes and Object-oriented Programming

Defining and using Classes and Objects
Using the self reference
No privacy in Python
Constructors and Destructors
Simulating privacy in classes
Class data
Converting you object to a string
Inheritance
Overriding methods
New-style vs. old-style classes
Under the Object-oriented hood
Overloaded operators
Attribute access
Properties
Alternative control with __slots__

14) Chapter 5: Persistence

Persistence options
The marshal module
The pickle module
Accessing the MySql database

15) Chapter 6: GUIs with Tkinter

The Tkinter module
Hello World with Tkinter

16) Chapter 7: Numerical Processing

The NumPy module
Using NumPy
Array slicing in Numpy

17) Chapter 8: Calling C code

How to call C code
The Swig tool

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