Mastering Python


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Typology Course
Methodology Online
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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.

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Course programme

1. 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.

2. 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.

3. 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.

4. Regular Expressions, it's Packages 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.

5. 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.

6. Machine Learning Using Python - I

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.

7. Machine Learning Using Python - II

Learning Objectives - In this module, you will learn in detail about Supervised and Unsupervised learning and examples for each category.

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.

8. Scikit and Introduction to Hadoop

Learning Objectives - This module will cover Scikit and an introduction to Hadoop MapReduce concepts.

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.

9. 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.

10. 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.

Topics - Web Scraping, Introduction to Beautifulsoup Package, How to Scrape Webpages. A real world project showing scrapping data from Google finance and IMDB.

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