Data Science with Python

Python is an open-source, free programming language and environment for statistical computing, data analysis, and graphics. Python is becoming commonly used by data scientists, statisticians, analysts who need to make sense of data. Well designed for effective data handling and storage facility. This course introduces delegates to Python environment, delegate will learn how to manipulate data, data exploration, data transformation, modelling, and effective interpretation of results. Also you will be able to build statistical models such as regression, clustering, classification and other modelling techniques.

• Module 1: Applied Machine Learning
  Statistical learning vs. Machine learning
  Iteration and evaluation
  Bias-Variance trade-off
• Module 2: Introduction to Python
  Understand the basic and Advanced Concepts of Python
  Python language characteristics
  Python IDLE and execution Model
  PYTHON programs on UNIX and Windows platform
  Python Editors and IDEs
• Module 3: Python Basics
  Variables
  Keywords
  Buit-in Funtions
  Strings
  Different kind of literals
  Math Operations and Expressions
  Writing to Screen
  String Formatting
  Command Line Parameters
  Flow Control
• Module 4: Sequences and File Operations
  Text file I/O
  Opening a Text File
  The with Block
  Reading and writing a Text File
  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
• Module 5: Create functions, sorting different elements and error handling techniques
  Function Parameters
  Global Variables
  Variable Scope
  Returning Values
  Sorting lists, functions, collections and dictionaries.
  Errors
  Generic Handling and Handling Multiple Exceptions.
  Raising and Re-raising Exceptions
  Import Statement and Search Path Module
• Module 6: Modules
  Understanding what is a Modules
  OS and SYS Modules
  Environment Variables
  Launching External Processes
  Paths, Directories, and Filenames
  Walking Directory Trees
  Dates and Times
• Module 7: Pythonic Idioms
  Common Python Idioms
  Packing and Unpacking
  Lambda Functions
  List Comprehensions
  Generators vs Iterators
• Module 8: Services Modules and Packages
  Module Zipped Archives
  Import Statement
  Pyc File I/O
  Module search Path
  Packages
  Module Aliases
• Module 9: Classes
  Introduction to Python Classes
  Defining Classes
  Instance Objects Methods and Attributes
  Initializers
  Properties
  Class Data
  Inheritance
  Statistic Methods
  Pseudo-private Variables
• Module 10: Developer Tools
  Program Development
  Customizing pylint
  Unit Testing and Test Class
  Establishing Success or Failure
  Start-up and Clean-up
  Running Tests
  Python Debugger and Debug Mode
  Setting Breakpoints
  Benchmarking
• Module 11: XML and JSON
  Normal Approaches to XML
  Use the right Module
  ElementTree
  Creating, Parsing and Navigating a XML Document
  Advanced XPath
• Module 12: iPython
  Features of iPython
  Tab Completion
  External Commands
  Notebook
• Module 13: Popular Python Plugins
  Numpy extention
  Working and interacting with Numpy extension in Python environment
  Scipy Library
  Basic plotting using Matplotlib
  Introduction to Pandas in Python environment
• Module 14: Python Imaging Library
  The PIL
  Supported Image File Types
  The Image Class
  Reading and Writing
  Creating Thumbnails
  Coordinate System
  Cropping and Pasting
  Rotating, Resizing, and Flipping
  Enhancing
  Module 15: Deploying and using Models

Certificate of completion

Digital certificate - Included