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Python Certification Training

Edureka's Python Online Certification Training will make you an expert in Python programming. Learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through Beautiful soup. You earn Python Certification after completion of this training. 

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Why this course ?

  • Google, Facebook, Amazon, YouTube, NASA, Reddit, Quora, Mozilla & other Fortune 500 companies use Python
  • According to the TIOBE index, Python is one of the most popular programming languages in the world
  • Average salary of a Python Developer is $115,000 / year - Indeed.com
  • 17K + satisfied learners. Reviews

Instructor-led live online classes


Sun - Thu ( 15 Days )
09:30 PM - 11:30 PM ( EDT )
10% Off


Sat - Sun ( 5 Weeks )
11:00 AM - 02:00 PM ( EDT )
10% Off


Fri - Sat ( 5 Weeks )
09:30 PM - 12:30 AM ( EDT )
10% Off


Sun - Thu ( 15 Days )
09:30 PM - 11:30 PM ( EDT )
10% Off

Edureka For Business

Train your employees with exclusive batches and offers and track your employee's progress with our weekly progress report.

Instructor-led Live Sessions

30 Hours of Online Live Instructor-led Classes. Weekend class : 10 sessions of 3 hours each and Weekday class : 15 sessions of 2 hours each.

Real-life Case Studies

Live project based on the data scraped from social media sites in real time and finding insights.


Every class will be followed by practical assignments which aggregates to minimum 40 hours.

Lifetime Access

Lifetime access to Learning Management System (LMS) which has class presentations, quizzes, installation guide & class recordings.

24 x 7 Expert Support

Lifetime access to our 24x7 online support team who will resolve all your technical queries, through ticket based tracking system.


edureka! certifies you as 'Python Expert' based on your project performance, reviewed by our expert panel.


Access to global community forum for all our users that further facilitates learning through peer interaction and knowledge sharing.

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 advanced 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.

During this course, our expert Python instructors will help you:

  • Master the Basic and Advanced Concepts of Python
  • Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
  • Master the Concepts of Sequences and File operations
  • Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
  • Gain expertise in machine learning using Python and build a Real Life Machine Learning application
  • Understand the supervised and unsupervised learning and concepts of Scikit-Learn
  • Master the concepts of MapReduce in Hadoop and learn to write Complex MapReduce programs.
  • Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python.
  • Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics.
  • Master the concepts of Web scraping in Python.
  • Work on a real time project on Big Data Analytics using Python & gain hands on project experience.

It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger. 

It 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.

It has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that it is the " Next Big Thing " and a must for Professionals in the Data Analytics domain.

Experienced professionals or Beginners. Anyone who wants to learn programming with Python can start right away! The course is exclusively designed for professionals aspiring to make a career in Big Data Analytics using Python. 

Software Professionals, Analytics Professionals, ETL developers, Project Managers, Testing Professionals are the key beneficiaries of this course. 
Other professionals who are looking forward to acquire a solid foundation of this widely-used open source general-purpose scripting language, can also opt for this course.

There are no hard pre-requisites. 

Attendees having prior programming experience and familiarity with basic concepts such as variables/scopes, flow-control, and functions would be beneficial. Prior exposure to object-oriented programming concepts is not required, but definitely beneficial.

A real world project showing scrapping data from Google finance and IMDB using beautiful soup.
We will also perform Sentiment analysis over the live tweets fetched from twitter.
Your system should have a 3GB RAM, a processor better than core 2 duo.
We will help you set up Virtual machine by Edureka in your system with python and python IDE installed on it which will be a local access for you. In case your system doesn't meet the pre-requisites, we will help you to set up Python directly on your machine.

Learning Objectives - Understand what Python is and why it is so popular. Learn how to set up Python environment, flow control and 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.

Learning Objectives - 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.

Learning Objectives - 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.

Learning Objectives - 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.

Learning Objectives - 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.

Learning Objectives - 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.

Learning Objectives - 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.

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.

Learning Objectives - 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.

Learning Objectives - 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.

You will never miss a lecture at edureka! You can choose either of the two options:
  • View the recorded session of the class available in your LMS.
  • You can attend the missed session in any other live batch.
edureka is committed to provide you an awesome learning experience through world-class content and best-in-class instructors. We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.
All instructors at edureka are senior industry practitioners with minimum 10 - 12 years of relevant IT experience. They are subject matter experts who trained by edureka to provide impeccable learning experience to all our global users.
You can Call us at +91 90660 20867 /1844 230 6362 ( US Tollfree ) OR Email us at sales@edureka.co . We shall be glad to assist you. 

  • Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka’s Python Expert certificate.
  • edureka certification has industry recognition and we are the preferred training partner for many MNCs including e.g. Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc