Instructor Led Training 39 Hours
Exercises & Project Work 54 Hours
Self Paced Video 70 Hours
MARCH 8th Sat & Sun 8 PM IST (GMT +5:30) ENROLL NOW
MARCH 14th Sat & Sun 8 PM IST (GMT +5:30) ENROLL NOW
MARCH 21st Sat & Sun 8 PM IST (GMT +5:30) ENROLL NOW

Online Training

  • Course 1

    Python Programming Certification Training

Self Paced

  • Course 2

    Java Certification Training

  • Course 3

    AWS Certification Training

  • Course 4

    Soft Skills Training

Course Syllabus

Section 1: Introduction

About Course

Python is one of the leading, flexible, and powerful open source languages ​​that is easy to learn and use, and offers a powerful library for data manipulation and analysis. For more than a decade, Python has been used in scientific computing and high-volume fields such as finance, oil and gas, physics and signal processing.

Eduranz’s Python for Data Science Course not only focuses on the basics of Python, statistics, and machine learning but also helps to gain experience in applied data science at the Python scale. This Data Science with Python training is a step by step guide for Python with Data Science with broad hands. This Python for Data Science course is filled with a variety of problem and activity scenarios and assignments that will allow you to gain direct experience in dealing with predictive modeling problems that require or require machine learning in Python. From statistical basics such as center, and mode to explore functions such as data analysis, regression, classification, grouping, Naive Bayes, cross-validation, label coding, random greening, decision making, and support vector machines with examples and supporting practices.

In addition, you will learn to improve machine learning, which is an important aspect of artificial intelligence. You can train your machine using real-world scenarios using machine learning algorithms.

The Python for Data Science course also covers basic and advanced Python concepts such as writing Python scripts, sorting, and operating Python files. They use libraries like Pandas, Numpy, Matplotlib, Scikit, and main concepts such as Python Machine Learning, scripts, and sequencing.

  • Programmers, Developers, Technical Leads, Architects
  • Developers aspiring to be a ‘Machine Learning Engineer’
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • ‘Python’ professionals who want to design automatic predictive models

The pre-requisites for Eduranz Python for Data Science course require the basic understanding of Computer Programming Languages. Proficiency in any programming language will come in handy for you.

Syllabus

In this Chapter, we will start from the very basic and understand the relevance of python followed by its setup, syntax, basic commands and a lot more, check below to know more:
• Introduction to Python
• Features of Python
• Advantages of using Python compared to other programming languages
• Types of Companies using Python
• Installation of Python on Windows, Mac and Linux distributions for Anaconda Python Deployment of Python IDE
• Basic commands of Python

Lab-Exercise:–
• Installing Python Anaconda for Windows, Linux, and Mac
• Writing a “Hello World Program”

This Chapter will cover all the concepts in Data Types and Objects with their respective hands-on and practical sessions, you will learn how to work with data types, number, strings, lists, tuples and dictionaries, you can check the below topics to know more:

• Understanding Python Data Types and Numbers
• Performing Simple arithmetic operations in Python
• Assigning Variables in Python
• Operators in Python
o Understanding all the basic operators in Python
o Comparison Operators in Python
o Chaining Comparison Operators with Logical Operators
• Working with strings like indexing and slicing a string, formatting, etc
• Working with Lists like creating a basic and multiple list, adding elements & more
• Covering the essential concepts of Tuples
• Sets & Boolean in Python
• Dictionaries in python like creating and adding elements to a Dictionary

Lab-Exercise:
• 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

 

Diving deep into Data Structure & Data Types

  • Vectors
  • Matrices
  • Factors
  • Data frames
  • Lists
  • Importing Data from various sources
  • Database Input: Connecting to database
  • Exporting Data to various formats
  • Viewing Data: Viewing partial data and full data
  • Variable & Value Labels: Date Values

Case study: We will go through a Case Study on HR Analytics


In this Python Chapter, we will go through all the basic concepts of Python Statements like if else, for loops, while loops, Control flow and all the essential Functions in Python, check the below topics to know more about this chapter:
• If Elif and Else Statements
• For loops in Python
• While loops & some useful operators in Python (range vs xrange on 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
• Intro to Python functions
o Types of Python Functions
• Defining a Function in Python
o Rules for naming python function (identifier)
• Python Function Parameters
• Python Return Statement and calling a function
• Function Arguments
• What is the Python Function Argument?
o Types of Python Function Arguments
o Default Argument in Python
o Python Keyword Arguments
o Python Arbitrary Arguments
• Python Built-In Functions with Syntax and Examples
• Lambda Expressions, Map, and Filter Functions

Lab-Exercise:
Along with covering basics to advanced topics in this chapter, we will also cover the Lab-exercises and practical for all the above-mentioned topics based on an industrial-based case studies such as:
• 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

essential concepts of OOP, where in we will start from basics to the advanced level while able to write smooth codes using the concepts of OOP, we will cover the topics like:
• Intro to Object-Oriented Programming and its need
• 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

Lab-Exercise:
• Write a Class
• Writing a Python program and incorporating the OOP concepts in it
Creating a Bank Account using OOP concepts


In this part of the chapter, we will understand the concepts of Modules, Packages and deep-dive into some of the common errors in Python, along with the concepts of Exception Handling, topics as shown below:
• Intro to Modules
• Working on PyPi using pip Install: Installing external packages and modules
• Numeric, Logarithmic, Power, Trigonometric and Angular functions in Python
• Understanding Python Errors and Exceptions
• Syntax Errors in Python
• Handling Exceptions in Python
• Raising Exceptions
• User-defined Exceptions
• Unit Testing in Python

Lab-Exercise:
• Demo on Modules
• Demo on Exception Handling
• Running Tests with Unittest Library

  • Understanding more about Analytics World
  • Data Science Vs Data Analytics Vs Machine Learning Vs Artificial Intelligence Vs Business Analysis
  • Analytics keywords and their definitions
  • Business Objectives
  • Key driving factors in Analytics world

Case-Study: Case Study on how Predictive Analysis is helpful for Sales Industry

Chapter where you will learn all the essential topics of Decorators and Generators in Python such as:
• Understanding Decorators in Python
• Syntax of Decorators and Working with them
• Understanding Generators in Python
• Working with Generators

Lab-Exercise:
• Demonstration on Decorators and Generators

  • Performing Data Preparation steps
    • Consolidation/aggregation
    • Outlier treatment
    • Flat Liners
    • Missing values
  • Dummy Creation
    • Variable Reduction
  • Data Alignment and fine tuning

Upon the completion of Python concepts successfully, we will proceed towards the advanced concepts in Python, starting off with NumPy, a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on arrays.
This Chapter will cover the topics as shown below:

• Intro to NumPy
• Creating Arrays in NumPy
• Using Arrays and Scalars
• Indexing NumPy Arrays
• Numpy Array Manipulation
• Array Transpotation
• Universal Array Function
• Array Processing
• Array Input and Output

Lab-Exercise:
• Importing NumPy Modules
• Creating and Initializing NumPy Arrays of different dimensions
• Working with arange in Numpy arrays
• Perform arithematic operation on NumPy Arrays
• Create 3 Dimensional NumPy array

Scientific computing using SciPy
In this Chapter, we will learn Python’s very reliable library used for Scientific and technical computing, which is SciPy, we will cover the concepts as shown below:
• Introduction to SciPy
• Its Functions Building NumPy
• Clusters, Linning, Signals, Optimization, Integration, Sub packages and SciPy with Bayesian Theory.

Lab-Exercise:
• Working with SciPy Cluster and Lining
• Import SciPy by applying the Bayes phrase to the specified notes.

  Implementing Decision Tree model in R
This Chapter will cover Pandas, a Python library used for the Data Manipulation, the topics to be covered are as shown below:
• What is data manipulation?
• Use panda libraries to manipulate data
• Dependency of NumPy library libraries
• Pandas Series objects,
• Panda 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

Hands-on Exercise – Manipulating data with pandas by Importing & navigating spreadsheets containing variable types such as float, integer, double, and others.

Learn how to create beautiful and interactive data visualizations in Python using various libraries such as:
• Matplotlib
• Seaborn
• Pandas Built-in Data Visualization
• Plotly and Cufflinks
• Geographical Plotting
Also, we will draw understand charts and diagrams using these libraries

Lab-Exercise:
• Using MatPlotLib to create pie charts, scatter plots, line graphs, and histograms
• Create Graphs and Charts using different libraries

We will start off this chapter by revising the previous concepts in data analysis such as Pandas, MatplotLib, Numpy and SciPy. Then, we will move onto the topics shown below:
• Understanding of Machine Learning
• Understanding SciKit Learn
• Need of Machine Learning
• Types in Machine Learning
• Machine Learning Worklfow
• Understanding SciKit Learn!
• Machine Learning Use-Cases
• A brief understanding of various ML Algorithms:
o Supervised Learning
o Unsupervised Learning

Lab-Exercise:
• Working with Machine Learning Algorithms


While diving more into Machine Learning, in this chapter, we will understand one of its essential types that is Supervised Learning. We will cover the topics as shown below:
• What exactly is Supervised learning?
• Understanding the Classification and Regression Algorithms
• What is 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)

Lab-Exercise:
• Working with
• Using SciKit Library with Random Forest algorithm for implementing Supervised Learning

After learning the concepts of Supervised Learning, this chapter will cover the yet another type of Machine Learning, which is Unsupervised Learning, below are the topics which we are going to cover:
Introduction to Unsupervised Learning
Looking into the 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
Working with association rule mining measures covering support, trust, lift, and apriori Algorithm,
Lab-Exercise:

• 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

This is an advanced topic where we will integrate spark with python for performing cluster-computing. PySpark, a tool of apache, lightning-fast cluster computing technology, designed for fast computation)
Introduction to Pyspark with PySpark, need for a Python Spark, Fundamentals of PySark, Pyspark in Industry, Installing PySpark, Fundamentals of PySpark, Excellence Mapreduce, Use of PySpark Demo and PySpark.

Hand Exercise: Demonstration of contours and conditional statements related to tuple operations, properties, lists, etc., List operations, related properties, set properties, related operations, dictionary operations, related properties

Soft Skill Course Curriculum

In this chapter, we will discuss some of the common mistakes to avoid while communicating, also we will talk about ways to improve communication skills to enhance your personality. Take a look at the table of content

  • Understanding communication skills
  • Common mistakes to avoid
  • Tips to improve communication skills

This chapter highlights the science behind job interview preparation. Check out the table of content.

  • What to do before the interview?
  • What to do during the interview?
  • What to do after the interview?‘

In this part of the chapter, we will discuss dos and don’ts while building a resume. We will also see how to build the best resume, for both freshers and working professionals, to impress the interviewer.

This chapter highlights dos and don’ts while writing a cover letter, also gives a step by step walkthrough to building the best cover letter for both freshers and working professionals.

In this chapter, we will discuss the importance of a professional profile to showcase skills and achievements. We will also see how to build an attractive LinkedIn profile.

Here we will elaborate different ways to write a professional email. We will also talk about dos and don’ts while writing a professional mail.

Java Course Curriculum

In this chapter, we will start from the very basic and understand the relevance of Java programming language followed by the concept of OOP, OOP principles, and a lot more, check below to know more:

● Introduction to Java Programming

● Introduction to Object Orientated Programming (OOP concept)

● Object Oriented Programming Principles

○ Inheritance

○ Encapsulation

○ Abstraction

○ Polymorphism

● Features of Java programming language

● Java bytecode

● Java Virtual machine or JVM

This chapter highlights basic data types, variables, operators and many more. Check out the table of content.

● Basic Data Types

○ Primitive Data Types

○ Reference or Object Data Types

● Variables

● Operators

○ Arithmetic operators

○ Bit wise operators

○ Relational operators

○ Boolean logic operators

○ Assignment operators

● Operator precedence

● Decision making and control statements

● Arrays

In this part of the chapter, we will understand the concepts of selection statements, control statements, iterations, while, do while, for loop, along with the concepts of break, return, and many more as shown below:

● Selection statements in Java

● If statement

● Switch statement

● Iteration statement

● While and do-while

● For loop

● For-each loop

● Nested loop

● Jump statement

● Break

● Continue

● Return

This chapter highlights concepts of objects and classes in depth. Let us take a look at the table of content for this chapter.

● Objects in Java

● Constructors in Java

● Returning and passing objects as parameters

● Nested and inner classes in Java

● Single and multilevel inheritance in java,

● Extended classes in Java

● Access Control in Java

● Using super in Java

● Overloading and overriding methods in Java

● Abstract classes in Java

● Using final with inheritance in Java

In this chapter, we will focus on some of the packages, how to define, access, and import packages in Java. We will also discuss interfaces in Java. Take a look at the table of content for this chapter. ● Defining a package in Java

● Concept of CLASSPATH in Java

● Access modifiers in java

● Importing a package in Java

● Defining and implementing interfaces in Java

Here we will elaborate different string handling methods in Java. Take a look at the detailed content list that are discussed in this chapter.

● String constructors in Java

● Special string operations in Java

● Character extraction in Java

● Searching and comparing strings in Java

● String buffer class in Java.

This chapter elaborates some of the most important concepts of java programming language. Take a look at the table of content.

● Exception handling in Java

● Types of Exception in Java

● Uncaught exceptions in Java

● TRY in Java

● Catch and multiple catch statements in Java

● Using THROW in Java

● File handling

○ I/O streams

○ File I/O

This chapter highlights java collection, maps, queues, JDBC, drivers and many more.

● Collection framework in Java

● Preeminent interfaces in Java

● Comparable and Comparator in Java

● Lists in Java

● Maps in Java

● Sets in Java

● Queues in Java

● Java Database Connectivity (JDBC)

● Drivers in Java

● Accessing drivers in Java

● Connection in Java

● Statement in Java

● CRUD Operations in Java (with examples)

● Pepared statement in Java

● Callable statement in Java

AWS Solutions Architects Curriculum

In this chapter, you will be first introduced to Cloud Computing, different models and its concepts, then we will move towards essential concepts in AWS as shown below:
• What is Cloud Computing?
• Different Cloud Computing Models
• What is AWS? And how is AWS a leader in the cloud market?
• Intro to AWS Management Console
• Quick intro to the essential AWS Services such as:
o Elastic Cloud Compute (EC2)
o AWS Simple Storage Service (S3)
o Virtual Private Cloud (VPC)
o Amazon Machine Image (AMI)
o Elastic Block Storage (EBS)
o Elastic Load Balance (ELB) and more
• Thorough understanding of AWS Architecture
• Understanding Public/Elastic IP’s and comparing them
• Launching, Initiating and Terminating and AWS EC2 Instance
• What is Auto-Scaling?
• Best Practices and Costs for AWS EC2 and understanding various backup services concepts in AWS

Lab- Exercise:
• Setting-up for AWS free-tier account
• Launching an EC2 Instance
• Creating an S3 bucket through console and AWS CLI
• Understanding the process of hosting a website
• Launching a Linux Virtual Machine using an AWS EC2 Instance

In this chapter, you will understand the essential concepts behind the object storage, where in you will identify when to use which service and topics like:
• AWS Storage
• In-depth of AWS S3 – Creation, Version Control, Security, Replication, Transfer, Acceleration
• Other concepts in S3 like, storage classes, life cycle policy, cost-optimization and more
• Create and Configure CloudFront with S3
• Understanding and working with Elastic Block Storage
• Amazon Glacier storage for persisting data backup and archiving
• Data importing and exporting using Amazon Snowball

Lab- Exercise:
• Host a Static Website on AWS S3
• Uploading images and documents to AWS S3 from a Website
• Replicate Data across regions
• Export and Import data from Glacier storage using lifecycle policy
• Access any static website using AWS CloudFront
• Elastic Block Storage (EBS) for block-level persistent storage volumes with S3 buckets
• Connect cloud-based storage with the on-premise software using AWS Storage Gateway

We will start this chapter by understanding the essential concepts behind Auto-Scaling and various topics as shown below:
• Auto-Scaling mechanism and its components
• Auto-Scaling Lifecycle and its policy
• Understanding Fault Tolerance in AWS
• Understanding Elastic load balancing and its types
• Comparison between Classic, Network and Application Load Balancer
• Accessing the Elastic Load Balancer
• Working with Route53 and various routing policies

Lab- Exercise:
• Creating a Classic and a Network load balancer
• Working with Application load balancer and Auto-Scaling
• Scaling policy in Auto-Scaling
• Understanding how to register a domain using Route 53
• Routing internet traffic to the resources and automatically checking health of resources

In this part of the Chapter, we will first understand the essential types of AWS Database services which are basically used for managing structured and unstructured data in other-term we call them SQL and NoSQL Databases. The topics which will be covered here are:
• What exactly is a Relational Database?
• Understanding AWS RDS and Amazon Aurora
• Benefits of using AWS RDS and Amazon Aurora
• What exactly is a NoSQL or a Non-Relational Database?
• Understanding NoSQL Service of AWS i.e., AWS DynamoDB
• Working with a Data warehousing product i.e., Amazon Redshift
• Using an in-memory data store with ElasticCache
• Using AWS Kinesis for the Analysis of Data

Lab- Exercise:
• Creating an RDS Instance and its read replica instance
• Adding data to a replica RDS
• Storing an application’s data to master RDS
• Creating Tables and running queries in master RDS
• Creating PostgreSQL and MySQL instance using Amazon Aurora
• Create a NoSQL Table and run queries in it using DynamoDB
• Working with Redis Cache
• Using Kinesis Data Stream for Visualizing the website’s traffic

In this chapter, we will get an in-depth knowledge of Amazon Virtual Private Cloud, IAM, CloudWatch and CloudTrail, as shown below:
• Understanding VPC and its components
• Benefits of VPC
• VPC Use-Cases
• Understanding & Creating a Virtual Private Network
• Working on VPC Networking, IP addressing and VPN Connections
• Network Access Control List and Security Groups and Network Address Translation (NAT)
• Understanding VPC Peering
• Deep-dive into Identity Access Management (IAM)
• User management using IAM
• Policies and API Keys access using various AWS Services
• Key Management Service in IAM
• Accessing billings and creating alerts on billings
• IAM Best Practices

Lab- Exercise:
• Creating a VPN and attach an EC2 instance on it and access it on internet and a private network by using AWS PrivateLink
• Creating two instances on different VPC’s with the help of VPC peering
• Creating a new role for an application which can access to an S3 Bucket
• Creating new policies for new users to either give them the admin or restricted privileges
• Rotating different credentials for IAM users to keep the users account protected
• Log-in to AWS Console using MFA
• Accessing AWS Services by creating API keys
• Creating multiple budgets according to each and every service used on a monthly basis

This chapter will give you the insights of Monitoring services in AWS, where in you will work on concepts like:
• Managing IAM events using AWS CloudTrail
• Monitor and Manage AWS resources using CloudWatch
• Deploy configuration alerts and notifications using CloudWatch
• CloudWatch Billing and exploring Trusted Advisor

Lab- Exercise:
• Logging IAM events through AWS CloudTrail
• Monitoring an EC2 Instance with the help of CloudWatch
• Storing Logs in S3 by enabling ClouTrail

Learn about different Application Services provided by AWS used for sending E-mails, to receive notifications and for queuing the messages, this chapter will also deal with the latest Serverless Computing service which AWS Lambda, find the more topics below:
• Working with AWS Simple Email Service (SES), AWS Simple Notification Service (SNS), AWS Simple Queue Service (SQS) and AWS Simple Work Flow (SWF)
• Working with the orchestration service i.e., AWS Elastic Beanstalk
• AWS OpsWorks and CLI
• Work with AWS Lambda – A Serverless Computing Service

Lab- Exercise:
• Send an Email using AWS SES
• Enable and generate a notification service by sending a notification using AWS SNS
• Orchestrate an S3 Bucket using Elastic Beanstalk
• Copy an Object by using AWS Lambda
• Send an E-mail to the user using AWS Lambda whenever an object is added to S3 bucket
• Sending a notification via Lambda whenever a message is sent to SQS
• Model and Provision your apps with the help of AWS OpsWorks

In this chapter, you will know how to do the configuration management of your server along with its automation, check more topics below:
• Manage resources using AWS CloudFormation
• Configuration Management and Automation of Servers with the help of OpsWorks
• More into AWS Elastic Beanstalk
• Comparing CloudFormation, OpsWorks and Elastic Beanstalk
• Automatically checking the health of resources using Route53
• Using AWS CloudFormation for the provisioning of Infrastructure

Lab- Exercise:
• Routing the traffic towards the resources and checking the health of the resources automatically
• Installing LAMP Server on an EC2 Instance using CloudFormation
• Managing Servers using AWS OpsWorks Stacks
• Deploying a Web Application with the help of DynamoDB using Elastic Beanstalk (for orchestration)


This Section will cover all the important aspects required to clear the AWS Solutions Architect Exam:
• Examination guidelines
• Most likely asked Interview Questions
• Some other useful tips for completing exams and job interviews.

Projects

  • Projects

    Project 1: Analysis of the Python Name Template

     

    Industry: General

     

    Challenge: How to analyze trends and the most popular baby names

     

    Topic: In this Python project, you are working with the United States Social Security Administration (SSA), which provides data on the frequency of baby names from 1880 to 2016. This project requires data analysis by considering various methods. You will see the most common names, identify name trends, and find the most popular names for a particular year.

     

    highlights:

     

    Data analysis with the Pandas Library

    Provide data frame manipulation

    Land for bars and boxes with Matplotlib

    Project 2: – Python Web Scraping for Science

     

    In this project, you will be introduced to the web crawling process in Python. This includes installing Beautiful Soup, scraping libraries on the Web, editing general data and web page formats, exploring main object types, navigating strings, finding search trees, navigation options, Analyzer, Search Trees, and searching CSS. Class arguments, lists, functions, and keywords.

     

    Project 3: Predict customer churn at telecommunications companies

     

    Industry – Telecommunications

     

    Task Issues – To maximize the profitability of your telecommunications company by reducing cooling rates

     

    Topic: In this project, you work with telecommunications company customer records. This note contains telephone client subscription data. Each column contains a telephone number, call minutes at different times, fees paid, account life, and whether the customer has canceled some services by unsubscribing. The aim is to predict whether the customer will eventually swell or not.

     

    highlights:

     

    Expand the Scikit-Learn ML library

    Develop code with the Jupyter Notebook

    Create a model using the execution matrix

Certification

Features

Personal Mentor


At the time of enrollment team Eduranz will assign one mentor for you and he will be guiding you in this lifetime Journey.

24/7 Tech Adviser Support


Lifetime 24/7 Technical and Non Technical Support from team Eduranz.

Lifetime Access


Get Lifetime opportunity to access and attend the live sessions multiple times.

Assignments & Quizzes


Every module will be followed by certification based assessment and quiz.

Certification


Become a Certified Professional.

Job Assistance


Team Eduranz will update your Resume before forwarding it to our 60+ global Clients.

FAQs

No one misses any lecture at Eduranz, because you will be provided with the recorded sessions of the class on your LMS withn 24 hours and despite that, you can also attend any different live session to cover up the missed topic and aks your doubts from the trainer.

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.Independent study online: In this mode, you will receive videos with lectures and can continue the course as you wish.

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.

All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them underwent a rigorous selection process that included screening profiles, technical assessments, and training demonstrations before being certified for training. We also ensure that only high-level graduates live in our faculty.

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 training. We also don’t limit the number of tickets you can collect when solving questions and doubts.

Eduranz offers independent learning for those who want to learn at their own pace. This training also gives you the benefits of email questions, tutorial sessions, 24×7 support, and access to modules or LMS for lifelong learning. In addition, you will receive the latest version of learning material at no additional cost. Independent Eduranz training is 75% lower than teacher-led online training. If you experience problems while studying, we can arrange virtual courses directly with the trainer at any time.

Eduranz actively supports all trainees who have successfully completed the training. That’s why we are involved in more than 80 top MNCs worldwide. This way you can be in exclusive organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco and other similar-sized companies. We also support you during job interviews and preparation of your CV.Is it possible to switch from independent training to teacher-led training?In any case, you can switch from self-directed self-training to online training only by paying an additional amount and participating in the next set of training that will be specifically notified to you.

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 Eduranz’s partner organizations, which includes a lot of top MNCs worldwide that are also part of the Fortune 500 list.

Our job assistance program will help you reach the job that you have been seeking. In our support program, we help you by sharing your resume with the companies that we have tie ups with, along with helping you in resume building, getting your prepared for the interviews through mock sessions by our industry experts (from various companies like IBM, Microsoft, Accenture, Delloite etc.), also by providing you with mock interview questions and exhaustive session by Eduranz. However, Eduranz is not a recruitment or job agency, we do not guarantee you a job, we simply direct your resumes to different companies, then after that, entire process is handled by the employer and company and the result is totally based on the employer’s decision.

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