ETLhive brings a new training course in the widely acclaimed programming language Python, designed primarily for the budding programmers who wish to make it bigin the Data Analytics Domain. This high-level programming language
2. What is Paython
• ETLhive brings a new training course in the
widely acclaimed programming language
Python, designed primarily for the budding
programmers who wish to make it bigin 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.
3. 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
• - Installation on Ubuntu-based Machines
• - Python Identifiers and Keywords
• - Indentation in Python
4. 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
5. Functions and Error
Handling Techniques
• - 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
• - try... except...else
• - try... finally...clause
• - User-defined Exceptions
• - Summary
• - Conclusion
6. 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
7. Machine Learning with
Python• - Defining Machine Learning
• - Implementation of Machine Learning
• - Machine Learning and Python
• - Algorithms
• - Learning NumPy and Scipy
• - Learning - Supervised or Unsupervised
• - Supervised, Unsupervised Learning and Classification
• - Classification and k-Nearest Neighbours (kNN)
• - Building, Testing, and Measuring the Performance of the Classifier
• - Defining Clustering Problem
• - k-Means Clustering
• - Pandas - Creating and Manipulating Data
• - Summary
• - Conclusion
