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.
Section 1:Getting Started with Python
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.
Section 2: Sequences and File Operations
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.
Section 3:Deep Dive - Functions, Sorting, Errors and Exception Handling
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.
Section 4: Regular Expressions, it's Packages and Object Oriented Programming in Python
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.
Section 5: Debugging, Databases and Project Skeletons
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.
Section 6: Machine Learning Using Python - I
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.
Section 7: Machine Learning Using Python - II
Topics:Classification Problem, Classifying with k-Nearest Neighbours (kNN) Algorithm, General Approach to kNN, Building the Classifier from Scratch, Testing the Classifier, Measuring the Performance of the Classifier. Clustering Problem, What is K-Means Clustering, Clustering with k-Means in Python and an Application Example. Introduction to Pandas, Creating Data Frames, Grouping, Sorting, Plotting Data, Creating Functions, Converting Different Formats, Combining Data from Various Formats, Slicing/Dicing Operations.
Section 8: Scikit and Introduction to Hadoop
Topics:Introduction to Scikit-Learn, Inbuilt Algorithms for Use, What is Hadoop and why it is popular, Distributed Computation and Functional Programming, Understanding MapReduce Framework, Sample MapReduce Job Run.
Section 9: Hadoop and Python
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.
Section 10:Web Scraping in Python and Project Work
Topics:Web Scraping, Introduction to Beautifulsoup Package, How to Scrape Webpages. A real world project showing scrapping data from Google finance and IMDB.