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Intellipaat Big Data Course in Singapore lets you master Big Data Hadoop and Spark online to get ready for the Cloudera CCA Spark and Hadoop Developer Certification (CCA175), as well as master Hadoop administration with 14 real-time industry-oriented case-study projects. Get the best Hadoop training in Singapore from certified mentors as well as earn IBM Big Data Certificate.
Intellipaat is a renowned name in the domain of online training widely popular for providing the most industry-recognized and career-oriented Big Data Hadoop training in Singapore. This master’s program trains learners in four wide domains of Hadoop, viz., Developer, Admin, Analyst and Testing. Starting from MapReduce and Spark to Oozie and Flume, all the significant topics will be covered in this course. At the end of the training course, the learners will be carrying out a project work upon the completion of which they will achieve IBM certification.
There is no prerequisite to take up this Big Data training and to master Hadoop. But basics of UNIX, SQL and Java would be beneficial. At Intellipaat, we provide the complimentary Linux and Java courses with our Big Data certification training to brush-up the required skills so that you are good on your Hadoop learning path.
Singapore is known as the most technology-ready nation in the entire Southeast Asia. This nation is widely known for having the most progressive economic and trade market. Some of the world’s top companies like Facebook, Netflix, Twitter, etc. are investing massively in this market. As Big Data is considered as the recent advancement in the technology domain, the companies are increasingly using platforms like Hadoop to perform analytical operations. Hence, demand for Hadoop professionals is on the rise in this nation.
Being one of the most lucrative business markets, it is attracting investors from across the globe to thrive in this market. This emerging market has made the companies use Big Data Analytics at large scale which has caused the popularity of Hadoop to go high in this market. Therefore, candidates who wish to build their careers as Big Data Analysts should learn Hadoop.
Big Data Analytics does not remain inside the boundaries of IT companies alone anymore and spans across from manufacturing to service sectors nowadays. This growing use of Big Data has led the companies to adopt Hadoop, and hence learning it would help you grab top jobs in no time.
Big Data Hadoop online training course equips learners with all the essential skills required to become a successful Big Data Analyst. As part of this training, the learners will be carrying-out 14 real-time projects based on Hadoop components. Also, this training course helps you score higher in CCA175 and CCAH exams.
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1.1 The architecture of Hadoop cluster
1.2 What is High Availability and Federation?
1.3 How to setup a production cluster?
1.4 Various shell commands in Hadoop
1.5 Understanding configuration files in Hadoop
1.6 Installing a single node cluster with Cloudera Manager
1.7 Understanding Spark, Scala, Sqoop, Pig, and Flume
2.1 Introducing Big Data and Hadoop
2.2 What is Big Data and where does Hadoop fit in?
2.3 Two important Hadoop ecosystem components, namely, MapReduce and HDFS
2.4 In-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability and in-depth YARN – resource manager and node manager
Hands-on Exercise:
1. HDFS working mechanism
2. Data replication process
3. How to determine the size of the block?
4. Understanding a data node and name node
3.1 Learning the working mechanism of MapReduce
3.2 Understanding the mapping and reducing stages in MR
3.3 Various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle, and Sort
Hands-on Exercise:
1. How to write a WordCount program in MapReduce?
2. How to write a Custom Partitioner?
3. What is a MapReduce Combiner?
4. How to run a job in a local job runner
5. Deploying a unit test
6. What is a map side join and reduce side join?
7. What is a tool runner?
8. How to use counters, dataset joining with map side, and reduce side joins?
4.1 Introducing Hadoop Hive
4.2 Detailed architecture of Hive
4.3 Comparing Hive with Pig and RDBMS
4.4 Working with Hive Query Language
4.5 Creation of a database, table, group by and other clauses
4.6 Various types of Hive tables, HCatalog
4.7 Storing the Hive Results, Hive partitioning, and Buckets
Hands-on Exercise:
1. Database creation in Hive
2. Dropping a database
3. Hive table creation
4. How to change the database?
5. Data loading
6. Dropping and altering table
7. Pulling data by writing Hive queries with filter conditions
8. Table partitioning in Hive
9. What is a group by clause?
5.1 Indexing in Hive
5.2 The ap Side Join in Hive
5.3 Working with complex data types
5.4 The Hive user-defined functions
5.5 Introduction to Impala
5.6 Comparing Hive with Impala
5.7 The detailed architecture of Impala
Hands-on Exercise:Â
1. How to work with Hive queries?
2. The process of joining the table and writing indexes
3. External table and sequence table deployment
4. Data storage in a different table
6.1 Apache Pig introduction and its various features
6.2 Various data types and schema in Hive
6.3 The available functions in Pig, Hive Bags, Tuples, and Fields
Hands-on Exercise:Â
1. Working with Pig in MapReduce and local mode
2. Loading of data
3. Limiting data to 4 rows
4. Storing the data into files and working with Group By, Filter By, Distinct, Cross, Split in Hive
7.1 Apache Sqoop introduction
7.2 Importing and exporting data
7.3 Performance improvement with Sqoop
7.4 Sqoop limitations
7.5 Introduction to Flume and understanding the architecture of Flume
7.6 What is HBase and the CAP theorem?
Hands-on Exercise:Â
1. Working with Flume to generate Sequence Number and consume it
2. Using the Flume Agent to consume the Twitter data
3. Using AVRO to create Hive Table
4. AVRO with Pig
5. Creating Table in HBase
6. Deploying Disable, Scan, and Enable Table
8.1 Using Scala for writing Apache Spark applications
8.2 Detailed study of Scala
8.3 The need for Scala
8.4 The concept of object-oriented programming
8.5 Executing the Scala code
8.6 Various classes in Scala like getters, setters, constructors, abstract, extending objects, overriding methods
8.7 The Java and Scala interoperability
8.8 The concept of functional programming and anonymous functions
8.9 Bobsrockets package and comparing the mutable and immutable collections
8.10 Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), first Spark application using SBT/Eclipse, Spark Web UI, Spark in Hadoop ecosystem.
Hands-on Exercise:
1. Writing Spark application using Scala
2. Understanding the robustness of Scala for Spark real-time analytics operation
9.1 Introduction to Scala packages and imports
9.2 The selective imports
9.3 The Scala test classes
9.4 Introduction to JUnit test class
9.5 JUnit interface via JUnit 3 suite for Scala test
9.6 Packaging of Scala applications in the directory structure
9.7 Examples of Spark Split and Spark Scala
10.1 Introduction to Spark
10.2 Spark overcomes the drawbacks of working on MapReduce
10.3 Understanding in-memory MapReduce
10.4 Interactive operations on MapReduce
10.5 Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
10.6 The overview of Spark and how it is better than Hadoop
10.7 Deploying Spark without Hadoop
10.8 Spark history server and Cloudera distribution
11.1 Spark installation guide
11.2 Spark configuration
11.3 Memory management
11.4 Executor memory vs. driver memory
11.5 Working with Spark Shell
11.6 The concept of resilient distributed datasets (RDD)
11.7 Learning to do functional programming in Spark
11.8 The architecture of Spark
12.1 Spark RDD
12.2 Creating RDDs
12.3 RDD partitioning
12.4 Operations and transformation in RDD
12.5 Deep dive into Spark RDDs
12.6 The RDD general operations
12.7 Read-only partitioned collection of records
12.8 Using the concept of RDD for faster and efficient data processing
12.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions
13.1 Understanding the concept of key-value pair in RDDs
13.2 Learning how Spark makes MapReduce operations faster
13.3 Various operations of RDD
13.4 MapReduce interactive operations
13.5 Fine and coarse-grained update
13.6 Spark stack
14.1 Comparing the Spark applications with Spark Shell
14.2 Creating a Spark application using Scala or Java
14.3 Deploying a Spark application
14.4 Scala built application
14.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
14.6 Creating an application using SBT
14.7 Deploying an application using Maven
14.8 The web user interface of Spark application
14.9 A real-world example of Spark
14.10 Configuring of Spark
15.1 Working towards the solution of the Hadoop project solution
15.2 Its problem statements and the possible solution outcomes
15.3 Preparing for the Cloudera certifications
15.4 Points to focus on scoring the highest marks
15.5 Tips for cracking Hadoop interview questions
Hands-on Exercise:
1. The project of a real-world high value Big Data Hadoop application
2. Getting the right solution based on the criteria set by the Intellipaat team
16.1 Learning about Spark parallel processing
16.2 Deploying on a cluster
16.3 Introduction to Spark partitions
16.4 File-based partitioning of RDDs
16.5 Understanding of HDFS and data locality
16.6 Mastering the technique of parallel operations
16.7 Comparing repartition and coalesce
16.8 RDD actions
17.1 The execution flow in Spark
17.2 Understanding the RDD persistence overview
17.3 Spark execution flow, and Spark terminology
17.4 Distribution shared memory vs. RDD
17.5 RDD limitations
17.6 Spark shell arguments
17.7 Distributed persistence
17.8 RDD lineage
17.9 Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey
18.1 Introduction to Machine Learning
18.2 Types of Machine Learning
18.3 Introduction to MLlib
18.4 Various ML algorithms supported by MLlib
18.5 Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques
Hands-on Exercise:Â
1. Building a Recommendation Engine
19.1 Why Kafka and what is Kafka?
19.2 Kafka architecture
19.3 Kafka workflow
19.4 Configuring Kafka cluster
19.5 Operations
19.6 Kafka monitoring tools
19.7 Integrating Apache Flume and Apache Kafka
Hands-on Exercise:Â
1. Configuring Single Node Single Broker Cluster
2. Configuring Single Node Multi Broker Cluster
3. Producing and consuming messages
4. Integrating Apache Flume and Apache Kafka
20.1 Introduction to Spark Streaming
20.2 Features of Spark Streaming
20.3 Spark Streaming workflow
20.4 Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
20.5 Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
20.6 Important windowed operators and stateful operators
Hands-on Exercise:Â
1. Twitter Sentiment analysis
2. Streaming using Netcat server
3. Kafka–Spark streaming
4. Spark–Flume streaming
21.1 Introduction to various variables in Spark like shared variables and broadcast variables
21.2 Learning about accumulators
21.3 The common performance issues
21.4 Troubleshooting the performance problems
22.1 Learning about Spark SQL
22.2 The context of SQL in Spark for providing structured data processing
22.3 JSON support in Spark SQL
22.4 Working with XML data
22.5 Parquet files
22.6 Creating Hive context
22.7 Writing data frame to Hive
22.8 Reading JDBC files
22.9 Understanding the data frames in Spark
22.10 Creating Data Frames
22.11 Manual inferring of schema
22.12 Working with CSV files
22.13 Reading JDBC tables
22.14 Data frame to JDBC
22.15 User-defined functions in Spark SQL
22.16 Shared variables and accumulators
22.17 Learning to query and transform data in data frames
22.18 Data frame provides the benefit of both Spark RDD and Spark SQL
22.19 Deploying Hive on Spark as the execution engine
23.1 Learning about the scheduling and partitioning in Spark
23.2 Hash partition
23.3 Range partition
23.4 Scheduling within and around applications
23.5 Static partitioning, dynamic sharing, and fair scheduling
23.6 Map partition with index, the Zip, and GroupByKey
23.7 Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions
24.1 Create a 4-node Hadoop cluster setup
24.2 Running the MapReduce Jobs on the Hadoop cluster
24.3 Successfully running the MapReduce code
24.4 Working with the Cloudera Manager setup
Hands-on Exercise:
1. The method to build a multi-node Hadoop cluster using an Amazon EC2 instance
2. Working with the Cloudera Manager
25.1 Overview of Hadoop configuration
25.2 The importance of Hadoop configuration file
25.3 The various parameters and values of configuration
25.4 The HDFS parameters and MapReduce parameters
25.5 Setting up the Hadoop environment
25.6 The Include and Exclude configuration files
25.7 The administration and maintenance of name node, data node directory structures, and files
25.8 What is a File system image?
25.9 Understanding Edit log
Hands-on Exercise:
1. The process of performance tuning in MapReduce
26.1 Introduction to the checkpoint procedure, name node failure
26.2 How to ensure the recovery procedure, Safe Mode, Metadata and Data backup, various potential problems and solutions, what to look for and how to add and remove nodes
Hands-on Exercise:
1. How to go about ensuring the MapReduce File System Recovery for different scenarios
2. JMX monitoring of the Hadoop cluster
3. How to use the logs and stack traces for monitoring and troubleshooting
4. Using the Job Scheduler for scheduling jobs in the same cluster
5. Getting the MapReduce job submission flow
6. FIFO schedule
7. Getting to know the Fair Scheduler and its configuration
27.1 How ETL tools work in Big Data industry?
27.2 Introduction to ETL and data warehousing
27.3 Working with prominent use cases of Big Data in ETL industry
27.4 End-to-end ETL PoC showing Big Data integration with ETL tool
Hands-on Exercise:
1. Connecting to HDFS from ETL tool
2. Moving data from Local system to HDFS
3. Moving data from DBMS to HDFS,
4. Working with Hive with ETL Tool
5. Creating MapReduce job in ETL tool
28.1 Importance of testing
28.2 Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end-to-end tests, Functional testing, Release certification testing, Security testing, Scalability testing, Commissioning and Decommissioning of data nodes testing, Reliability testing, and Release testing
29.1 Understanding the Requirement
29.2 Preparation of the Testing Estimation
29.3 Test Cases, Test Data, Test Bed Creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, Hive and HBase) while loading the input (logs, files, records, etc.) using Sqoop/Flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges, etc.), reporting defects to the development team or manager and driving them to closure
29.4 Consolidating all the defects and create defect reports
29.5 Validating new feature and issues in Core Hadoop
30.1 Report defects to the development team or manager and driving them to closure
30.2 Consolidate all the defects and create defect reports
30.3 Responsible for creating a testing framework called MRUnit for testing of MapReduce programs
31.1 Automation testing using the OOZIE
31.2 Data validation using the query surge tool
32.1 Test plan for HDFS upgrade
32.2 Test automation and result
33.1 Test, install and configure
Working with MapReduce, Hive, and Sqoop
In this project, you will successfully import data using Sqoop into HDFS for data analysis. The transfer will be from Sqoop data transfer from RDBMSRead More..
Work on MovieLens Data For Finding the Top Movies
Create the top-ten-movies list using the MovieLens data. For this project, you will use the MapReduce program for working on the data file, Apache PigRead More..
Hadoop YARN Project: End-to-End PoC
Bring the daily incremental data into the Hadoop Distributed File System. As part of the project, you will be using Sqoop commands to bring theRead More..
Table Partitioning in Hive
In this project, you will learn how to improve the query speed using Hive data partitioning. You will get hands-on experience in partitioning of HiveRead More..
Connecting Pentaho with Hadoop Ecosystem
Deploy ETL for data analysis activities. In this project, you will challenge your working knowledge of ETL and Business Intelligence. You will configure Pentaho toRead More..
Multi-node Cluster Setup
Set up a Hadoop real-time cluster on Amazon EC2. The project will involve installing and configuring Hadoop. You will need to run a Hadoop multi-nodeRead More..
Hadoop Testing Using MRUnit
In this project, you will be required to test MapReduce applications. You will write JUnit tests using MRUnit for MapReduce applications. You will alsoRead More..
Hadoop Web Log Analytics
Derive insights from web log data. The project involves the aggregation of log data, implementation of Apache Flume for data transportation, and processing of dataRead More..
Hadoop Maintenance
Through this project, you will learn how to administer a Hadoop cluster for maintaining and managing it. You will be working with the name nodeRead More..
Twitter Sentiment Analysis
Find out what is the reaction of the people to the demonetization move by India by analyzing their tweets. You will have to download theRead More..
Analyzing IPL T20 Cricket
This project will require you to analyze an entire cricket match and get any details of the match. You will need to load the IPLRead More..
Movie Recommendation
Recommend the most appropriate movie to a user based on his taste. This is a hands-on Apache Spark project, which will include the creation ofRead More..
Twitter API Integration for Tweet Analysis
Analyze the user sentiment based on a tweet. In this Twitter analysis project, you will integrate the Twitter API and use Python or PHP forRead More..
Data Exploration Using Spark SQL – Wikipedia Data Set
In this project, you will be making use of the Spark SQL tool for analyzing Wikipedia data. You will be integrating Spark SQL for batchRead More..
This training course is designed to help you clear the Cloudera Spark and Hadoop Developer Certification (CCA175) exams. The entire training course content is in line with these certification programs and helps you clear these certification exams with ease and get the best jobs in the top MNCs.
As part of this Big Data course in Singapore, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.
At the end of this Big Data Hadoop training in Singapore, there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and help you score better.
Intellipaat Course Completion Certificate will be awarded upon the completion of the project work (after expert review) and upon scoring at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
I'm really thankful to Intellipaat about the Hadoop Architect Course with Big Data certification. First of all, the team supported me in finding the best Big Data online course based on my experiences and current assignment. Also, the session is so practical, and the trainers are seasoned and available for any queries even in offline mode after the sessions of Big Data Hadoop course inRead More..
This is regards to conveying my deepest gratitude to Intellipaat. The quality and methodology of the online Hadoop training is matchless. The self-study program for which I had enrolled for big data Hadoop training ticked all the right boxes. I had access to free tutorials and videos to help me in my learning endeavour. A special mention must be made regarding the promptness and enthusiasmRead More..
I wish I knew about Intellipaat online Hadoop training before. I have hugely benefitted from this big data Hadoop certification course. Excellent course material and highly recommended Hadoop trainers will provide you full understanding of Hadoop concepts.
Matt Peter
This online big data Hadoop training is extremely industry-focused and job-oriented. Overall I am giving 10 out of 10 for this Hadoop certification course from Intellipaat!
Mohit Rana
I mastered Hadoop through the Intellipaat Big Data Hadoop online training. Let me frankly tell you that this course is designed in a unique and comprehensive manner that is by far the best. Plus you get loads of free tutorials and video content all along. The entire coursework is easy to understand, very simple language but highly effective from the learner's point of view. ThereRead More..
Nandini Shankar
A big thank you to the entire Intellipaat Big Data Hadoop Team! You have delivered a great Hadoop online certification training course, with equally informative Hadoop online tutorials, Big Data video tutorials that are absolutely free. Highly experienced and qualified Big Data Hadoop trainers made the learning process completely effortless and enjoyable for me. I am extremely happy for having enrolled for the best HadoopRead More..
This Intellipaat Hadoop tutorial has delivered more than what they had promised to me. Since I have undergone previous Hadoop training I am quite familiar with Big Data Hadoop concepts but Intellipaat took it to a different level with their attention to details and Hadoop domain expertise. I recommend this training to everybody. You will learn everything from basic Hadoop concepts to advanced Hadoop technologyRead More..
Samar Jain
Thank you very much for your training. The trainer resolved my query in record time and that too as per my utmost sanctification. I have no words to describe my gratitude to Intellipaat.
sheelam Khan
Recently I completed Big Data Hadoop Certification Training from intellipaat. Great Learning. The best investment I ever made in my career. I've learnt and benefitted a lot from intellipaat big data online course and continue to be a member.
I had taken Intellipaat Big Data Hadoop Online. An excellent online mode of learning. Now I am confident I can look out for a career in Big Data. Upon successfully completing this big data course Thanks again and looking forward to a lot more learning from Intellipaat !! I highly recommend the big data online course. All the best.
Priyanka Chawla
I wanted to learn big data online since it had a huge scope. My career changed positively upon completion of Intellipaat Big Data Hadoop Online Training. Go with Intellipaat for a Bright Career !!! Thanks.
I am completely satisfied with the Intellipaat big data hadoop training. The trainer came with over a decade of industry experience. The entire big data online course was segmented into modules that were created with care so that the learning is complete and as per the industry needs.
I am very much happy with the Intellipaat big data Hadoop training. The trainer knowledge and experience was very good. I got more than what I had expected as part of the training program and because of this I could easily master the Hadoop technology. I would recommend the Intellipaat big data course to all.
Full marks for the Intellipaat support team for providing excellent support services. Hadoop was new to me and I used to have many queries, but the support team was very qualified and very patient in listening to my queries and resolving them to my highest expectations. The entire Big Data course was completely oriented towards the practical aspects.
Hi, Intellipaat Big Data course video quality is of the highest level. I had enrolled for the self-paced Big Data Hadoop training online; the videos offered the best platform for learning at one's leisurely pace, since it has been created by industry experts and the attention to detail and real-world examples in the videos are worth mentioning. According to me, this is an industry-recognized BigRead More..
I work as a Senior Technology Architect at Infosys. I work on many projects related to big data technology. After joining Intellipaat, one of the best Hadoop training institutes in Singapore, I feel more confident in working on hadoop related pojects and the outcome is much better compared to before. Thanks intellipaat team.
I took hadoop online training course from intellipaat and successfully completed it. I have already started working with big data hadoop team in my company. It feels amazing to be a part of hadoop group I highly recommend this as one of the best Big Data Training institutes in Singapore to everybody who wants to make their career in big data domain.
Intellipaat big data Hadoop developer course with spark is a boom for building your skills from beginning to advanced level. As I wanted to start with a big data course but I was not knowing how because I had no idea how to start, from where to start, and what to start, then I finally decided to take a course of big data Hadoop developerRead More..
Excellent course, I found Intellipaat's training team to be talented in their respective domain. My training was very good as it helped me in upgrading my skills, which proved to be a turning point in my career. Intellipaat's mentor was well-experienced and his teaching method was really great. This online course helped me to get deep understanding of the technology. Moreover, the excercise and projectsRead More..
Genuine platform for learning. I finished my course recently from Intellipaat. The trainers were excellent in teaching. Further, the course was well-structured and the lectures are really flexible. I am currently working and I still get the time to complete the course within the given time and it is mainly possible because of the 24*7 support system and the clarity of their teaching. Besides, they make us doRead More..
Amazing course learning experience It was a great learning experience with Intellipaat. The coursware is comprehensive, and has a variety of material like videos, PPTs, and PDFs that are neatly organized. Further, there are hands-on projects, assignments, code files for each module. And most importantly, the support I received as a learner while pursuing my course was exemplary.
It is a known fact that the demand for Hadoop professionals far outstrips the supply. So, if you want to learn and make a career in Hadoop, then you need to enroll for Intellipaat Hadoop course online which is the most recognized name in Hadoop training and certification. Intellipaat Hadoop training includes all major components of Big Data and Hadoop like Apache Spark, MapReduce, HBase, HDFS, Pig, Sqoop, Flume, Oozie and more. The entire Intellipaat Big Data training in Singapore has been created by industry professionals. You will get 24/7 lifetime support, high-quality course material and videos and free upgrade to latest version of course material. Thus, it is clearly a one-time investment for a lifetime of benefits.
Yes, Intellipaat provides other courses like Big Data Hadoop, Data Science Certification, Machine Learning, Artificial intelligence, Python, Python for Data Science, Data Analytics, and Business Analytics
If you are looking for free resources on Big Data Hadoop then read our blogs on Big Data Hadoop tutorial, and Big Data Hadoop Interview Questions.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.
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