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June 20 Sat & Sun
Weekend batch
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8:00 PM IST to 11:00 PM IST
June 26 Sat & Sun
Weekend batch

8:00 PM IST to 11:00 PM IST
July 3 Sat & Sun
Weekend batch

8:00 PM IST to 11:00 PM IST
July 11 Sat & Sun
Weekend batch

8:00 PM IST to 11:00 PM IST

Program Price

$149/- $299/-

40% off limited time offer

Program Syllabus

  • • Introduction to Python
  • • Features of Python
  • • Advantages of using Python
  • • Companies using Python
  • • Installation process of Python
  • • Basic commands of Python

  • • Installing Python Anaconda for Windows, Linux, and Mac
  • • Writing a “Hello World Program”
  • • Python Data Types
  • • Numbers
  • • Simple arithmetic operations in Python
  • • Assigning Variables in Python
  • • Operators in Python
  • • Strings
  • • Indexing, slicing, and formatting
  • • Lists
  • • Tuples
  • • Sets
  • • Boolean
  • • Dictionaries

  • • Adding, Subtracting, Multiplying and Dividing numbers using arithmetic operations
  • • Creating a list with multiple distinct and duplicate elements
  • • Accessing and removing the elements from a list
  • • Slicing a list
  • • Creation and Concatenation of Tuples
  • • Slicing of Tuples
  • • Demonstration of Set and Boolean operations
  • • Demonstration on Python Dictionaries
  • • Python statements
  • • If Elif and Else Statements
  • • For loop
  • • While loop
  • • Range vs xrange in Python
  • • List Comprehensions in Python
  • • Chaining comparison in python
  • • Else with for and Switch Case in Python
  • • Using iteration in python
  • • Iterators in Python
  • • Iterators function
  • • Python functions and its types
  • • Defining a Function in Python
  • • Rules for naming Python function (identifier)
  • • Python Function Parameters
  • • Python Return Statement and calling a function
  • • Function arguments
  • • Python function argument and its types
  • • Default argument in Python
  • • Python keyword arguments
  • • Python arbitrary arguments
  • • Python built-In functions with syntax and examples
  • • Lambda expressions, map, and filter Functions

  • • Write a Python Function with or without the parameters
  • • Demo on If Else Statements and Iterators Functions
  • • Demo on Simple Boolean and Simple Math Functions
  • • Demo on create an object and write a for loop to print all odd numbers
  • • Demo on smaller or a greater number
  • • Use Lambda Expression to Map and Filter the Functions
  • • OOP concept
  • • Attributes
  • • Class Keywords
  • • Class Object Attributes
  • • Methods in Python
  • • Data Hiding and Object Printing
  • • Constructors and Destructors in Python
  • • Class and static variable in python
  • • Class method and static method in python
  • • Inheritance, Encapsulation, Polymorphism & Abstraction
  • • Special Methods - Magic Method

  • • Write a Class
  • • Writing a Python program and incorporating the OOP concepts in it
  • • Creating a Bank Account using OOP concepts
  • • Modules
  • • Installing external packages and modules
  • • Working oPn yPi using pip Install
  • • Numeric, Logarithmic, Power, Trigonometric and Angular functions
  • • Python Errors and Exceptions
  • • Syntax Errors in Python
  • • Handling Exceptions in Python
  • • Raising Exceptions
  • • User-defined Exceptions
  • • Unit Testing in Python

  • ● Demo on Modules
  • ● Demo on Exception Handling
  • ● Running Tests with Unittest Library
  • • Decorators in Python
  • • Syntax of Decorators and Working with them
  • • Generators in Python
  • • Working with Generators

  • • Demonstration on Decorators and Generators
  • • NumPy
  • • Creating Arrays in NumPy
  • • Using Arrays and Scalars
  • • Indexing NumPy Arrays
  • • NumPy Array Manipulation
  • • Array Transportation
  • • Universal Array Function
  • • Array Processing
  • • Array Input and Output

  • • Importing NumPy Modules
  • • Creating and Initializing NumPy Arrays of different dimensions
  • • Working with arange in NumPy arrays
  • • Perform arithmetic operation on NumPy Arrays
  • • Create 3 Dimensional NumPy array
  • • SciPy
  • • Clusters, Linning, Signals, Optimization, Integration, Sub packages
  • • Bayesian Theory

  • • Working with SciPy Cluster and Lining
  • • Import SciPy by applying the Bayes phrase to the specified notes.
  • • Data manipulation
  • • Pandas libraries
  • • Dependency of NumPy libraries
  • • Pandas Series objects
  • • Pandas data frames
  • • Load and process data with Pandas
  • • Combining data objects
  • • Merging, and various types of data object attachments
  • • Record & clean notes, edit notes, visualize notes

  • • Manipulating data with pandas by Importing & navigating spreadsheets containing variable types such as float, integer, double, and others.
  • • Matplotlib
  • • Seaborn
  • • Pandas Built-in Data Visualization
  • • Plotly and Cufflinks
  • • Geographical Plotting

  • • Using Matplotlib to create pie charts, scatter plots, line graphs, and histograms
  • • Create Graphs and Charts using different libraries
  • • Web scraping in Python
  • • Web scraping libraries
  • • Beautifulsoup and Scrapy
  • • Installation of beautifulsoup
  • • Installation of Python parser lxml
  • • Creating soup object with input HTML
  • • Searching of tree
  • • Full or partial parsing
  • • Output print
  • • Searching the tree

  • • Installation of Beautiful soup and lxml Python parser
  • • Making a soup object with input HTML file
  • • Navigating using Py objects in soup tree
  • • Introduction to Machine Learning
  • • Understanding SciKit Learn
  • • Need of Machine Learning
  • • Types in Machine Learning
  • • Machine Learning Workflow
  • • Understanding SciKit Learn!
  • • Machine Learning Use-Cases
  • • Machine Learning Algorithms
  • • Supervised Learning
  • • Unsupervised Learning

  • • Working with Machine Learning Algorithms
  • • Supervised learning
  • • Classification and Regression Algorithms
  • • Linear regression and how to do calculations in Linear Regression?
  • • Understanding Linear regression in Python
  • • Understanding Logistics regression
  • • Working with Supports vector machine
  • • xgboost (standalone step)

  • ● Working with Classification and Regression Algorithms
  • ● Using SciKit Library with Random Forest algorithm for implementing Supervised Learning
  • ● Xgboost
  • • Unsupervised Learning
  • • Use Cases of Unsupervised Learning Understanding Clustering,
  • • Types of Clustering - Exclusive Clustering, Overlapping Clustering, Hierarchical Clustering
  • • Understanding K-Means Clustering and its algorithm
  • • Stepwise calculation of k-means algorithm
  • • Running k-means with SciKit Library
  • • Understanding association mining rule
  • • Market basket analysis
  • • Association rule mining and Apriori Algorithm

  • ● Demo on Unsupervised Learning
  • ● Demo on Algorithms in the SciKit Learn package for applying machine learning techniques and training the network model
  • ● Demo on Apriori
  • As part of the ongoing research study in your organization, you need to build a program to track and study recent scientific data posted on the website.

  • A medical firm has given you the task of analyzing whether the patient is ‘normal’, or ‘Suspect of having a disease’ or actually has a ‘disease’.

  • As you are holding the position of Data Scientist in your current organization, you need to build a model to categorize words based on sentiments. This model should tell whether words detected are positive or negative.

  • Andrew is a Data Analyst in a company named ValueAnalytics, he has been assigned a project to analyze the Stock Market from a data set of Technology Stocks, by using the different libraries, he has to extract the stock information and perform the visualization of different aspects, along with analyzing the risk of a stock from its past history. find the change in the price of the stock over time, find the daily return of the stock on average, find the moving average of the various stocks, find the correlation between different stocks' closing prices, find the correlation between different stocks' daily returns, find the value we should put at risk by investing in a particular stock, also, attempt to predict future stock behaviour.

  • Post the election, the government has given your company a contract of doing the analysis on the Election and Donor Data. You as the data analyst are supposed to answer a few questions by analyzing the aggregated poll data. Like how many votes are done and different aspects in it along with analyzing the average donations given to Democrats and Republican (more questions are asked during the project).


Python for Data Science Certification Training

An industry-oriented course designed by experts. Become a Data Scientist by mastering Python programming and concepts of Data Science as well as Machine Learning.

Key Features

  • 7+ Projects, hands-on, and case studies

  • 42+ Hours of interactive learning

  • 30+ Hours of exercise and project work

  • Lifetime access to LMS

  • Attend as many batches for lifetime

  • 24/7 Technical Support

  • Resume Building

  • Placement Assistance

  • Dedicated Learner Delight Team

About Course

Python programming, NumPy, SciPy, Pandas, Matplotlib, Seaborn, Plotly, Cufflinks, Beautifulsoup, Scrapy, SciKit, Machine Learning algorithms, supervised, unsupervised, and more.

Freshers, anyone willing to build a career as a Data Scientist.

No prerequisite. We teach everything from scratch.

The landscape of Data Science is projected to double its size by the year of 2025 (in 2019 it was 3.03 billion.) Data Scientists are the highest paying professionals in the industry. The average salaries of Data Scientists in India and in the US are 8 lacs/yr. and $124k/yr. respectively.

Hiring Companies

Source : Indeed

Our Learners Work For

Course Completion Certificate

What People Think

Amit Soni

Amit Soni

Business Development Manager at Qatar Computer Ser

This course on Python will leave one owning all the information that is required to shape up and grow. From Instructor to the support team has always made sure that every individual gets the entire volume of the subject .

Manoj Gera

Manoj Gera

Data Architect at Webster Bank

Rahul Pohankar

Rahul Pohankar

Senior Manager

The Python course at Eduranz was better than any real time classes I ever attended. The faculty was wonderful and always available. The classes were practical oriented and it has been a great learning experience.Thank you Eduranz Team!

Rajeev Kathuria

Rajeev Kathuria

West Head-Partner Management at Samsung

The instructor was very good and prompt in responding to questions. Excellent virtual class experience. Good Work eduranz!

Ljiljana Spasovic Botha

Ljiljana Spasovic Botha

Business Development Officer at SASLO

Had a great learning session where the concepts are clear to understand and can solve the given assignments easily.

Train Your Employees

We offer flexible and cost-effective group membership for your business, goverment organization.

Connect Now


Eduranz offers a unique online Python for Data Science Certification course for professionals who are willing to build a career in this rousing domain. There are many reasons to choose Eduranz: Interactive online instructor-led live classes conducted by SMEs
Personal mentors who will keep a stage track of your progress in the course
A Substantial LMS which allows the users to view their recorded sessions from their live classes along with the self-recorded courses
Real-time exercises, assignments, industry-based use cases and real-world projects
24/7 learning support by the Eduranz’s dedicated tech support team
Large community of learners from across the globe
Industrially as well as globally recognized certificate by Eduranz
Personalized job support, resume and interview preparation

You never miss any lecture at Eduranz, because you will be provided with the recorded sessions of the live class on your LMS within 24 hours and despite that, you can also attend any different live session to cover up the missed topic and ask your doubts from the trainer or you can simply reschedule your batch and get yourself a new batch assigned.

Live Virtual Classes or Online Classes. With online class training, you can access courses via video conferencing from your desktop to increase productivity and reduce work time and personal time.

Eduranz offers a 24/7 request solution and you can pick up your tickets at any time from our dedicated support team. You can use email support for all your questions. If your request is not answered via email, we can also arrange one-on-one discussions with the faculty. You will be glad to know that you can switch to Eduranz support after completing the training. We also don’t limit the number of tickets you can collect when solving questions and doubts.

Yes, Eduranz has a dedicated placement assistance team. Our job assistance program will help you reach the job you have been seeking. Under this program, we help you by building your professional resume and then sharing it across our network companies that we have tie ups with.

Eduranz offers the most up-to-date, relevant and valuable projects in the real world as part of the training program. In this way, you can integrate what you have learned in the real industry. Each training is delivered with various projects where you can thoroughly test your skills, learning and practical knowledge so that you are well prepared for the industry. They work on very interesting projects in the fields of high technology, e-commerce, marketing, sales, networking, banking, insurance and more. After successfully completing your project, your skills will be counted as a result of six months of intensive industry experience.

After completing the Eduranz Training Program along with all real projects, tests and assignments and achieving at least 60% points in the qualification exam; you will receive an industrial recognized certificate by Eduranz. This certification is recognized by companies all across the industry, which includes a lot of top MNCs worldwide.

Our job assistance program will help you reach the job you have been seeking. Under this program, we help you by building your professional resume and then sharing it across our network companies that we have tie ups with. You will also be prepared for interviews through mock sessions. However, Eduranz is not a recruitment agency. We do not guarantee you a job. After we share your profiles with the companies, the further process depends upon your performance and their decision.

All of our highly qualified instructors are industry experts with minimum 10-12 yrs. of relevant IT experience. Each of them underwent a rigorous selection process that included screening profiles, teaching assessments, and training demonstrations.

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