Programming

Introduction

Features of the Java Language
Java Environment
Object Oriented Programming in Java
Java Program Structure.
Java and Unicode

Data Type, Variables and Constants, Loops and Logic:

• Data and Variables
• Integer Data Types, Floating Point Data Types
• Arithmetic Calculations, Mixed Arithmetic expressions, the op= operator, Mathematical functions and constants, Bitwise operators
• Enumerated data type
• Boolean Variables
• Operator precedence
• Program comments
• Loops and Logic
• Making decisions, Logical operators, The Conditional Operator, The switch Statement
• Variable scope, Loops, Assertions.

Arrays and Strings:

• Arrays
• Strings
• Operations on Strings
• Mutable Strings

Defining Classes

• What is a class?, defining classes.
• Defining methods, Defining constructors.
• Defining and using a class.
• Method overloading, Using Objects.
• Understanding packages, controlling access to class members
• Nested classes
• The finalize() method
• Native method

Extending classes and inheritance

• Class inheritance, choosing base class access attributes
• Polymorphism
• Multiple Levels of inheritance
• Abstract classes
• The Universal superclass
• Methods accepting a variable number of arguments
• Casting Object
• Enumeration in detail
• Using the final modifier
• Interfaces
• Anonymous classes

Exceptions

• Types of Exceptions
• Dealing with exceptions
• Exception Objects
• Defining your own exceptions

Stream based I/O

• Understanding Streams
• Accessing Files and Directories
• Writing Files
• Reading Files
• Serializing Objects

Generic Class Types and Collection Framework

• Generic Class Type
• What are Generic types?
• Defining a Generic Class Type
• Generic Type and Generic Interfaces
• Variables of a Raw Type
• Using wildcards as type parameter arguments
• Arrays and parameterized types
• The Collection Framework
• Collections of Object
• Iterators
• Collection Classes
• Using Vectors
• Linked Lists
• Using Maps

A Collection of useful classes

• Utility Methods for Arrays
• Observable and Observer Objects
• Generating Random Numbers
• Dates and Times
• Regular Expressions

Thread

• Understanding Threads
• Managing Threads
• Thread Priorities

Why Python ?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built-in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python is continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.

Who is this Course for

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

Curriculum for Python

Getting Started with Python

Learning Objectives – In this module, you will understand what Python is and why it is so popular. You will also learn how to set up Python environment, flow control and will write your first Python program.

Topics

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, Built-in Functions, Strings, Different Literals, Math Operators and Expressions, Writing to the Screen, String Formatting, Command Line Parameters and Flow Control.

Sequences and File Operations

Learning Objectives – In this module, you will learn different types of sequences in Python, the power of dictionary and how to use files in Python.

Topics – Lists, Tuples, Indexing and Slicing, Iterating through a Sequence, Functions for all Sequences, Using Enumerate(), Operators and Keywords for Sequences, The xrange() function, List Comprehensions, Generator Expressions, Dictionaries and Sets.

Deep Dive – Functions, Sorting, Errors and Exception Handling

Learning Objectives – In this module, you will understand how to use and create functions, sorting different elements, Lambda function, error handling techniques and using modules in Python.

Topics – 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.

Regular Expressions and Object Oriented Programming in Python

Learning Objectives – In this module, we understand the Object-Oriented Programming world in Python, use of standard libraries and regular expressions.

Topics – 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 Data, Static Methods, Private Methods and Inheritance, Module Aliases and Regular Expressions.

Debugging, Databases and Project Skeletons

Learning Objectives – In this module, you will learn how to debug, how to use databases and how a project skeleton looks like in Python.

Topics – 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.

Machine Learning Using Python Introduction

Learning Objectives – This module will help you understand what Machine Learning is, why Python is preferred for it and some important packages used for scientific computing.

Topics – 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. In this module we will also build a small Machine Learning application and discuss the different steps involved while building an application.

Hadoop and Python

Learning Objectives – In this module, you will understand how to use Python in Hadoop MapReduce as well as in PIG and HIVE.

Topics – 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.

Web Scraping in Python and Project Work

Learning Objectives – In this module, we will discuss about the powerful web scraping using Python and a real world project.