Python Certification Course | Python Training | Edureka

Python Certification Training

Edureka's Python course helps you gain expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role. You will use libraries like Pandas, Numpy, Matplotlib, Scipy, Scikit, Pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping and big data analytics leveraging Apache Spark.

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Why should you take Python course ?

  • According to the TIOBE index, Python is one of the most popular programming languages in the world
  • Average salary of a Data Scientist professional is $150,000 / year -
  • 23K + satisfied learners. Reviews
  • Hands-on practice with Cloud Lab

Instructor-led Python live online classes


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Goal : Give brief idea of what Python is and touch on basics.


  • Define Python
  • Know why Python is popular
  • Setup Python environment
  • Discuss flow control
  • Write your first Python program


  • Get an overview of Python  
  • Learn about Interpreted Languages  
  • List the Advantages/Disadvantages of Python  
  • Explore Pydoc  
  • Start Python  
  • Discuss Interpreter PATH
  • Use the Interpreter  
  • Run a Python Script  
  • Discuss Python Scripts on UNIX/Windows  
  • Explore Python Editors and IDEs  
  • Use Variables, Keywords, Built-in Functions, Strings, Different literals, Math operators and expressions, Writing to the screen, String formatting, Command line parameters and Flow Control. 

Hands On:

  • Data types - string, numbers, dates
  • Keywords
  • Variables
  • Literals

Goal : Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.


  • Define Reserved Keywords and Command Line Arguments
  • Describe how to Get User Input from Keyboard
  • Describe Flow Control and Sequences
  • Practice Working with Files
  • Define and Describe Dictionaries and Sets


  • 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.
  • Working with files
  • Modes of opening a file
  • File attributes
  • File methods

Hands On:

  • List - properties, related operations
  • Tuple - properties, related operations, comparison with list
  • Dictionary - properties, related operations, comparison with list
  • Set - properties, related operations, comparison with dictionary

Goal : Learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.


  • Explain Functions and various forms of Function Arguments
  • Explain Standard Library
  • Define Modules
  • Describe Zip Archives and Packaging


  • 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 waysModule Aliases and Regular Expressions

Hands On / Demo :

  • Functions - syntax, arguments, keyword arguments, return values
  • Lambda - features, syntax, options, comparison with functions
  • Sorting - sequences, dictionaries, limitations of sorting
  • Errors and exceptions - types of issues, remediation
  • Packages and module - modules, import options, sys path

Goal : Understand the Object-Oriented Programming world in Python and use of standard libraries.


  • Implement Regular Expression and its Basic Functions
  • Use Classes, Objects, and Attributes
  • Develop applications based on Object Oriented Programming and Methods


  • The sys Module
  • Interpreter information
  • 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
  • Initializes
  • Instance methods
  • Properties
  • Class methods and data
  • Static methods
  • Private methods and Inheritance

Hands On:

  • Regular expressions - regex library, search/match object, findall, sub, compile
  • Classes - classes and objects, access modifiers, instance and class members
  • OOPS paradigm - Inheritance, Polymorphism and Encapsulation in Python

Goal : Learn how to debug, how to use databases and how a project skeleton looks like in Python.


  • Debug python scripts using pdb
  • Debug python scripts using IDE
  • Classify Errors
  • Develop Unit Tests
  • Create project Skeletons
  • Implement Database using SQLite
  • Perform CRUD operations on SQLite database


  • Debugging
  • Dealing with errors
  • Using unit tests
  • Project Skeleton
  • Required packages
  • Creating the Skeleton
  • Project Directory
  • Final Directory Structure
  • Testing your set up
  • Using the skeleton
  • Creating a database with SQLite 3
  • CRUD operations
  • Creating a database object.

Hands On:

  • Debugging - debugging options, logging, troubleshooting
  • Unit testing - TDD, unittest library, assertions, automated testing
  • Project skeleton - industry standard, configurations, sharable libraries
  • RDBMS - Python for RDBMS, PEP 49, CRUD operations on Sqlite

Goal : Get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations.


  • Statistics - data terminology, measurement scales, types of data
  • Libraries - IPython, Matplotlib
  • Measures, Moments, Variance, Std. Deviation using numpy
  • Distributions, Probability and Bayes’ Theorem using Scipy
  • Numpy - arrays, matrices, related operations
  • Scipy - overview, areas of application


  • Data terminology
  • Scales of measurement
  • Types of data
  • Ipython notebook installation
  • Numerical measure
  • Matplotlib introduction
  • Deviation and variance
  • Standard deviation
  • Covariance and correlation
  • Conditional probability
  • Bayes theorem
  • Distribution/Probability functions
  • Installing Numpy
  • Numpy arrays and matrices
  • Installing Scipy
  • Scipy Modules and stats

Hands On:

  • Statistics - scales of measurement, numerical measures, variance, standard deviation, covariance and correlation, probability, Bayes theorem and distribution functions
  • Numpy - arrays, matrices and types of operations
  • Scipy - stats modules, physical constants, skewness, kurtosis

Goal : Learn in detail about Supervised and Unsupervised learning and examples for each category.


  • Define Machine Learning and understand Supervised vs Unsupervised
  • Apply Supervised Learning process flow, regression analysis
  • Apply Unsupervised Learning process flow, clustering
  • Apply Linear Regression, Multivariate Regression 
  • Measure accuracy using Mean Squared Error, Cross Validation
  • Analyze data using Pandas


  • Introduction to Machine Learning
  • Areas of implementation of Machine learning
  • Why Python
  • Major classes of Learning Algorithms
  • Supervised vs. Unsupervised learning
  • Inference models
  • Linear regression and mean squared error
  • Multivariate regression
  • Cross validation
  • Regression Summary
  • Introduction to Pandas
  • Creating Data frames
  • Grouping
  • Sorting
  • Plotting Data
  • Creating functions
  • Converting different formats
  • Combining data from various formats
  • Slicing/Dicing operations 

Hands On:

  • Supervised learning - Linear Regression and RMSE, Multivariate Regression, Cross Validation
  • Pandas - Series, DataFrames, data analysis involving grouping, sorting, filtering, munging, visualization/plotting and mesh up

Goal : Tackle complex machine learning problems requiring classification or clustering.

At the end of this Module, you should be able to:

  • Feature engineer datasets using PCA, Bias/Variance analysis
  • Apply classifications algorithms like KNN, Random Forests, SVM etc.
  • Apply clustering algorithms like K-Means, Hierarchical clustering etc.
  • Compute classification and clustering metrics to ascertain model accuracy


  • Feature engineering
  • Dealing with categorical data
  • Dealing with text data
  • Using encoders
  • Count, TF-IDF Vectorizer
  • Bias/Variance tradeoff
  • Principal Component Analysis (PCA)
  • KNN
  • Decision Trees
  • Random Forests
  • Ensemble Learning
  • Averaging and boosting algorithms
  • Random Forest classifier
  • Support Vector Machines (SVM)
  • Support Vector Classifier
  • Accuracy measures - AUC, ROC, Confusion Matrix, Log Loss
  • Clustering algorithms and accuracy measures
  • K-Means clustering
  • Silhouette coefficient
  • Hierarchical clustering using Dendrogram
  • Density-based clustering using DBSCAN

Hands On:

  • Data analysis activity using live datasets from Google Finance
  • Encoders, vectorizers, PCA, KNN, CART, Random Forest Ensemble, SVM, Clustering, Accuracy measures using Metrics

Goal : Learn Spark basics and run machine learning models over Spark.

At the end of this Module, you should be able to discuss:

  • Apache Spark - Concepts, RDD, MLLib, Data frames
  • Transformations, Actions, Shuffling, Persistence and Data Removal
  • Shared variables - accumulators and broadcast
  • Spark SQL and Data frames
  • Spark MLlib
  • Regression, Classification & Clustering with PySpark


  • Apache Spark introduction
  • Spark engine
  • Spark core API
  • Spark libraries
  • SparkContext and SparkConf
  • Concepts - RDD, Shuffling and Persistence
  • RDD transformations and actions
  • Shared variables - Accumulators, Broadcasts
  • Spark SQL and Dataframes
  • Spark MLlib
  • Regression with PySpark
  • Classification with PySpark
  • Clustering with PySpark

Hands On:

  • SparkContext and SparkConf
  • RDDs, Accumulators, Broadcasts, data removal
  • Spark SQL DataFrames
  • Regression, Classification and Clustering using Spark MLlib

Goal : Discuss about the powerful web scraping using Python and discuss a real-world project.


  • Discuss web scraping and its advantages
  • Discuss Steps Involved in Web Scraping
  • Use BeautifulSouppackage and its functions
  • Scrape IMDB webpage
  • Fetch Streaming Tweets from Twitter
  • Perform Sentiment Analysis on tweets Fetched from Twitter and determine which is more popular Ferrari or Porsche


  • Web scraping
  • Introduction to Beautiful soup package
  • How to scrape webpages
  • A Real-world project showing scrapping data from Google finance and IMDB.

Hands On:

  • Scraping - BeautifulSoup and its functions, pulling content using regex, restricting content using SoupStrainer
  • Scraping IMDB, Reddit
  • Tweet sentiment analysis using Twitter API for Python

. Call a Course Adviser for discussing Curriculum Details . 1844 230 6362
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. 

Edureka's Python Certification Training not only focuses on fundamentals of Python, Statistics, Machine Learning and Spark but also helps one gain expertise on applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems, quiz and assignments and scenarios that help you gain practical experience in addressing an automation problem that would either require only Python or Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds. 

With exponential growth in data (as is evident from new Kaggle competitions that now support Torrent for downloading humongous data sets), it goes without saying that Data Scientist is incomplete without Big Data. So, in this course, we ensure you become a fully qualified Data Scientist by also teaching you basics of Spark in the context of data analysis and Machine Learning. 

Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scipy, scikit, pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping and big data analytics leveraging Apache Spark.

During this Python online course, our experts instructors will help you: 

  • Write Python scripts, unit test code
  • Understand different types of Machine Learning problems and related data
  • Programmatically download and analyze data
  • Apply machine learning techniques and algorithms over data
  • Learn feature engineering techniques like PCA
  • Ascertain accuracy of predictions using RMSE, Log Loss, AUC, Cross Validation
  • Learn techniques to deal with different types of data – ordinal, categorical, encoding
  • Compare algorithms and improve accuracy
  • Learn data visualization
  • Using IPython notebooks, master the art of presenting step by step data analysis.

Get Python certification post completion of this course

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.

Read more on Top 10 reasons to learn Python

There is a booming demand for skilled data scientists and machine learning with Python professionals across all industries that make this course suited for participants at all levels of experience. We recommend this Python course especially for the following professionals:

Below are the professionals which should definitely take this course. 

  • Programmers, Developers, Technical Leads, Architects
  • Data Scientists & Data Analyst
  • Business Analysts
  • Business Intelligence Manager
  • Statisticians and Analysts
  • Project Managers

There are no hard pre-requisites. Basic understanding of Computer Programming terminologies is sufficient. 

Also, basic concepts related to Data analysis are beneficial but not mandatory. 

You will also get familiar with basics of statistics during the course. 

Industry: Human Resource management

Challenge –
AB Consultants is a company that outsources its employees as Consultants to top various IT firms. Their business had been increasing quite well over past, however in recent times there has been a slowdown in terms of growth because their best and most experienced employees have started leaving the Company. Inorder to prevent this proactively you first need to dive in to the Company’s Employee Data and find out an answer as to know why the best and most experienced employees are leaving.

Solution –
As a Data Analyst of the Company you are required do an analysis and find out patterns as to why the best employees are leaving so early.
Using Python, you derive at a forecast model to predict which employees could be leaving the company, as well as a probability as to why our best and most experienced employees are leaving prematurely. This will help to plan our next steps to avoid the churn out. You decide to create a script that will contain the following:

  • A visualization and distribution (of all the employee relative fields)
  • Forecast using different Machine Learning models and see the outcome
  • Compare different Machine Learning models and cross validate them
  • Find out why best and most experienced employees are leaving prematurely
  • Give a Final Prediction Model (the best one) to Forecast
The system requirement for Python course is a system with Intel i3 processor or above, minimum 3GB RAM (4GB recommended) and an operating system can be of 32bit or 64 bit.
Practicals can be executed using virtual machine given by Edureka with python and PyCharm community edition installed on it. Using PyCharm Community Edition, both the Spark and Python practicals can be executed. 

The detailed step-wise installation guides are present in LMS which will help them to install and set-up the environment for Python. 

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 for Data Science 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.

Cloud Lab New

Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment
You will never miss a class at Edureka! You can choose either of the two options:
1. You can go through the recorded session of the missed class and the class presentation that are available for online viewing through the LMS.
2. You can attend the missed session, in any other live batch. Please note, access to the course material will be available for lifetime once you have enrolled into the course.
We do provide placement assistance by routing relevant job opportunities to you as and when they come up. To get notified on relevant opportunities, it is important that you fill out your profile details.

It is important to attend classes and complete assignments. Course completion is an important criterion based on which we screen profiles of learners interested in a particular job. Also, before your profile is shared with prospective employers, you will have to go through an internal assessment by edureka. So it is important to be well versed with the course concepts to become eligible for placement opportunities.
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 . We shall be glad to assist you. 
You no longer need a credit history or a credit card to purchase this course. Using ZestMoney, we allow you to complete your payment with a EMI plan that best suits you. It's a simple 3 step procedure:
  • Fill your profile: Complete your profile with Aadhaar, PAN and employment details.
  • Verify your account: Get your account verified using netbanking, ekyc or uploading documents
  • Activate your loan: Setup automatic repayment using NACH to activate your loan

  • Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka’s Python for Data Science 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


Python Certification Training