Mastering Python
DESCRIPTION
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
- Where to find Python
- Installing Python
- Testing your installation
- 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
- Variables
- Identifiers
- Binding
- Data Types
- Basic Numbers
- Basic Strings
- Using Tuples and Sequences
- Using and modifying Lists
- Using Dictionaries
- Sequence slicing
- Basic Numeric Operators
- Basic Arithmetic Operators
- Exponentiation
- Bitwise
- Augmented Assignment
- Truncating Division
- Comparison and Logical
- Chaining
- Short-circuiting
- The Range Function
- The If Statement
- For loops
- While loops
- 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
- The exception mechanism
- Using the else clause
- Using the finally clause
- Using the raise statement
- Opening files
- Reading and writing files
- Reading whole files
- Using a file interator
- Reading and writing binary data
- 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
- Advanced variables and datatypes
- List comprehensions
- Pass statement
- Print >>
- Ternary operator
- Docstrings and Pydoc
- Using enumerate
- Strings and regular expressions
- Using str() and repr()
- Raw and Unicode strings
- The re module
- Functions
- Varargs with * and **
- Defining vararg functions
- Expanding sequences
- Lambda functions
- Embedding tests with __main__
- 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__
- Persistence options
- The marshal module
- The pickle module
- Accessing the MySql database
- The Tkinter module
- Hello World with Tkinter
- The NumPy module
- Using NumPy
- Array slicing in Numpy
- How to call C code
- The Swig tool
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