PYTHON FOR DATA SCIENCE
Our Python for data science consists of lectures and labs that will prepare you for a deep dive into Machine Learning. We focus on the following tools: Jupyter and IPython Notebook.
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built-in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python is continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Who is this course for?
This course is exclusively designed for professionals aspiring to make a career in Data Science using Python. Software Professionals, Analytics Professionals, ETL developers, Project Managers, Testing Professionals are the key beneficiaries of this course. Other professionals who are looking to acquire a solid foundation of this widely-used open source general-purpose scripting language, can also opt for this course.
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.
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.
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.
Regular Expressions 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.
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.
Machine Learning Using Python Introduction
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.
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.
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.