1. Python ETLhive brings a new training course in the widely acclaimed programming language Python, designed primarily for the budding programmers who wish to make it big in the Data Analytics Domain. This high- level programming language with its powerful library, clear syntax, and high readability has emerged as one of the "must-know" languages. The course at the ETLhive is intended to impart knowledge on the basic and advance conceptual frameworks of Python which includes an in-depth understanding of sequence and file operations, machine learning, python scripts, functions in python, web scraping etc. This course is ideal for programmers as it provides a helpful insight into debugging programming errors, therefore ensuring better programming abilities. Further, there are elaborate lectures on the importance and usage of Machine Learning and Scientific Computing and a hands-on training about setting up Python Environment. Who should opt for this course? Software Professionals such as Programmers, Web Developers, ETL Developers, Analytics Professionals, Automation Engineers, Hadoop Programmers, Project Managers, and even beginners must learn Python to compete well and to ensure their success in the IT sector. Pre-requisites Anyone having a basic knowledge of Windows or UNIX can apply for this course. An additional knowledge about programming will ensure faster learning and implementation in the real-time projects. Course and its structure Introduction to Python Defining Python History of Python and its growing popularity Features of Python and its wide functionality Python 2 vs Python 3 Running a Python Script Python Scripts on UNIX and Windows

2. Installation on Ubuntu-based Machines Python Identifiers and Keywords Indentation in Python Comments and Writing to the Screen Command Line Arguments and Flow Control User Input Summary Conclusion Python Data Types Objectives Variables and their types Variables - String Variables Variables - Numeric Types Variables - Boolean Variables Dictionary and Python Types of Variables - Dictionary Comparision of Variables Dictionary Methods and Manipulations Operators and Logical Operators Arithmatic Operations on Numeric Values Operators and Keywords for Sequences Summary Conclusion Functions and Error Handling Techniques Objectives of Functions Function Parameters Creating and Calling Functions Python user Defined Functions Python packages Functions Lambda Function Loops in Python - While, Nested, Demo-Create Statements - Break Statements, Continue Statements Python Exceptions Handling and Standard Exception Hierarchy

3. try... except...else try... finally...clause User-defined Exceptions Summary Conclusion Object Oriented Programming in Python Overview of Object Oriented Programming Defining Classes, Objects, and Initializers Attributes - Built-In Class Destroying Objects Methods - Instance, Class, Static, Private methods, and Inheritance Data Hiding Module Aliases and reloading modules Regular expressions Match Function, Search Function, and the Comparision Search and Replace Wildcard Summary Conclusion Error Debugging, Project Skeletons, and Machine Learning Debugging Errors - Unit Tests Project Skeleton Creating and Using the Skeleton Machine Learning with Python Defining Machine Learning Implementation of Machine Learning Algorithms Learning NumPy and Scipy Learning - Supervised or Unsupervised Supervised, Unsupervised Learning and Classification Classification and k-Nearest Neighbours (kNN)

4. Building, Testing, and Measuring the Performance of the Classifier Defining Clustering Problem k-Means Clustering Pandas - Creating and Manipulating Data Summary Conclusion Scikit, Hadoop and Python Defining Scikit-learn Inbuilt Algorithms Defining Hadoop and its growing popularity Hadoop and MapReduce Framework MapReduce Job Run and Python PIG, HIVE, and Python Web Scraping in Python Beautifulsoup Package Summary Conclusion