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How this works :

At Simplilearn, we greatly value the trust of our patrons. Our courses were designed to deliver an effective learning experience, and have helped over half a million find their professional calling. But if you feel your course is not to your liking, we offer a 7-day money-back guarantee. Just send us a refund request within 7 days of purchase, and we will refund 100% of your payment, no questions asked!

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Raise refund request within 7 days of commencement of the first batch you are eligible to attend. Money back guarantee is void if the participant has accessed more than 25% content of an e-learning course or has attended Online Classrooms for more than 1 day.

  • 32 hours of instructor-led training
  • 15 hours of self-paced video
  • Includes topics on Spark streaming, Spark ML, and GraphX programming
  • 1 industry project for submission and 2 for hands-on practice
  • Includes downloadable ebooks and 30 demos

Course description

  • What is the focus of this course?

    With Simplilearn’s Apache Spark and Scala certification training you would advance your expertise in Big Data Hadoop Ecosystem.

    With this Apache Spark certification you will master the essential skills such as Spark Streaming, Spark SQL, Machine Learning Programming, GraphX Programming, Shell Scripting Spark.

    And with real life industry project coupled with 30 demos you would be ready to take up Hadoop developer job requiring Apache Spark expertise.

  • What learning outcomes can be expected?

    With Certification in Apache Spark and Scala training, you will be able to-

    • Get clear understanding of the limitations of MapReduce and role of Spark in overcoming these limitations
    • Understand fundamentals of Scala Programming Language and it’s features
    • Explain & master the process of installing Spark as a standalone cluster
    • Expertise in using RDD for creating applications in Spark
    • Mastering SQL queries using SparkSQL
    • Gain thorough understanding of Spark Streaming features
    • Master & describe the features of Spark ML Programming and GraphX Programming

  • Who should do this course?

    • Professionals aspiring for a career in field of real time Big data analytics
    • Analytics professionals
    • Research professionals
    • IT developers and testers
    • Data scientists
    • BI and reporting professionals
    • Students who wish to gain a thorough understanding of Apache Spark

  • What projects are included in this course?

    US based university has collected datasets which represent reviews of movies from multiple reviewers as a part of Research Project. To gain in depth insights from research data collected you have to perform a series of tasks in Spark on the data set provided.

Course preview

    • Lesson 00 - Course Overview 04:12
      • 0.1 Introduction00:13
      • 0.2 Course Objectives00:28
      • 0.3 Course Overview00:38
      • 0.4 Target Audience00:31
      • 0.5 Course Prerequisites00:21
      • 0.6 Value to the Professionals00:48
      • 0.7 Value to the Professionals (contd.)00:20
      • 0.8 Value to the Professionals (contd.)00:21
      • 0.9 Lessons Covered00:24
      • 0.10 Conclusion00:08
    • Lesson 01 - Introduction to Spark 25:34
      • 1.1 Introduction00:15
      • 1.2 Objectives00:26
      • 1.3 Evolution of Distributed Systems
      • 1.4 Need of New Generation Distributed Systems01:15
      • 1.5 Limitations of MapReduce in Hadoop01:06
      • 1.6 Limitations of MapReduce in Hadoop (contd.)01:07
      • 1.7 Batch vs. Real-Time Processing01:09
      • 1.8 Application of Stream Processing00:07
      • 1.9 Application of In-Memory Processing01:48
      • 1.10 Introduction to Apache Spark00:45
      • 1.11 Components of a Spark Project
      • 1.12 History of Spark00:50
      • 1.13 Language Flexibility in Spark00:55
      • 1.14 Spark Execution Architecture01:13
      • 1.15 Automatic Parallelization of Complex Flows00:59
      • 1.16 Automatic Parallelization of Complex Flows-Important Points01:13
      • 1.17 APIs That Match User Goals01:06
      • 1.18 Apache Spark-A Unified Platform of Big Data Apps01:38
      • 1.19 More Benefits of Apache Spark01:05
      • 1.20 Running Spark in Different Modes00:41
      • 1.21 Installing Spark as a Standalone Cluster-Configurations
      • 1.22 Installing Spark as a Standalone Cluster-Configurations00:08
      • 1.23 Demo-Install Apache Spark00:08
      • 1.24 Demo-Install Apache Spark02:41
      • 1.25 Overview of Spark on a Cluster00:47
      • 1.26 Tasks of Spark on a Cluster00:37
      • 1.27 Companies Using Spark-Use Cases00:46
      • 1.28 Hadoop Ecosystem vs. Apache Spark00:32
      • 1.29 Hadoop Ecosystem vs. Apache Spark (contd.)00:43
      • 1.30 Quiz
      • 1.31 Summary00:40
      • 1.32 Summary (contd.)00:41
      • 1.33 Conclusion00:13
    • Lesson 02 - Introduction to Programming in Scala 37:35
      • 2.1 Introduction00:11
      • 2.2 Objectives00:16
      • 2.3 Introduction to Scala01:32
      • 2.4 Features of Scala
      • 2.5 Basic Data Types00:24
      • 2.6 Basic Literals00:35
      • 2.7 Basic Literals (contd.)00:25
      • 2.8 Basic Literals (contd.)00:21
      • 2.9 Introduction to Operators00:31
      • 2.10 Types of Operators
      • 2.11 Use Basic Literals and the Arithmetic Operator00:08
      • 2.12 Demo Use Basic Literals and the Arithmetic Operator03:18
      • 2.13 Use the Logical Operator00:07
      • 2.14 Demo Use the Logical Operator01:40
      • 2.15 Introduction to Type Inference00:34
      • 2.16 Type Inference for Recursive Methods00:10
      • 2.17 Type Inference for Polymorphic Methods and Generic Classes00:30
      • 2.18 Unreliability on Type Inference Mechanism00:23
      • 2.19 Mutable Collection vs. Immutable Collection01:13
      • 2.20 Functions00:21
      • 2.21 Anonymous Functions00:22
      • 2.22 Objects01:08
      • 2.23 Classes00:36
      • 2.24 Use Type Inference, Functions, Anonymous Function, and Class00:09
      • 2.25 Demo Use Type Inference, Functions, Anonymous Function and Class07:40
      • 2.26 Traits as Interfaces00:57
      • 2.27 Traits-Example00:09
      • 2.28 Collections00:42
      • 2.29 Types of Collections00:25
      • 2.30 Types of Collections (contd.)00:26
      • 2.31 Lists00:28
      • 2.32 Perform Operations on Lists00:07
      • 2.33 Demo Use Data Structures04:10
      • 2.34 Maps00:46
      • 2.35 Maps-Operations
      • 2.36 Pattern Matching00:33
      • 2.37 Implicits00:37
      • 2.38 Implicits (contd.)00:18
      • 2.39 Streams00:22
      • 2.40 Use Data Structures00:07
      • 2.41 Demo Perform Operations on Lists03:25
      • 2.42 Quiz
      • 2.43 Summary00:37
      • 2.44 Summary (contd.)00:37
      • 2.45 Conclusion00:15
    • Lesson 03 - Using RDD for Creating Applications in Spark 51:02
      • 3.1 Introduction00:12
      • 3.2 Objectives00:23
      • 3.3 RDDs API01:40
      • 3.4 Features of RDDs
      • 3.5 Creating RDDs00:36
      • 3.6 Creating RDDs—Referencing an External Dataset00:19
      • 3.7 Referencing an External Dataset—Text Files00:51
      • 3.8 Referencing an External Dataset—Text Files (contd.)00:50
      • 3.9 Referencing an External Dataset—Sequence Files00:33
      • 3.10 Referencing an External Dataset—Other Hadoop Input Formats00:46
      • 3.11 Creating RDDs—Important Points01:09
      • 3.12 RDD Operations00:38
      • 3.13 RDD Operations—Transformations00:47
      • 3.14 Features of RDD Persistence00:57
      • 3.15 Storage Levels Of RDD Persistence00:20
      • 3.16 Choosing The Correct RDD Persistence Storage Level
      • 3.17 Invoking the Spark Shell00:23
      • 3.18 Importing Spark Classes00:14
      • 3.19 Creating the SparkContext00:26
      • 3.20 Loading a File in Shell00:11
      • 3.21 Performing Some Basic Operations on Files in Spark Shell RDDs00:20
      • 3.22 Packaging a Spark Project with SBT00:50
      • 3.23 Running a Spark Project With SBT00:32
      • 3.24 Demo-Build a Scala Project00:07
      • 3.25 Build a Scala Project06:51
      • 3.26 Demo-Build a Spark Java Project00:08
      • 3.27 Build a Spark Java Project04:31
      • 3.28 Shared Variables—Broadcast01:21
      • 3.29 Shared Variables—Accumulators00:52
      • 3.30 Writing a Scala Application00:20
      • 3.31 Demo-Run a Scala Application00:07
      • 3.32 Run a Scala Application01:43
      • 3.33 Demo-Write a Scala Application Reading the Hadoop Data00:07
      • 3.34 Write a Scala Application Reading the Hadoop Data01:23
      • 3.35 Demo-Run a Scala Application Reading the Hadoop Data00:08
      • 3.36 Run a Scala Application Reading the Hadoop Data02:21
      • 3.37 Scala RDD Extensions
      • 3.38 DoubleRDD Methods00:08
      • 3.39 PairRDD Methods—Join00:47
      • 3.40 PairRDD Methods—Others00:06
      • 3.41 Java PairRDD Methods00:09
      • 3.42 Java PairRDD Methods (contd.)00:06
      • 3.43 General RDD Methods00:06
      • 3.44 General RDD Methods (contd.)00:05
      • 3.45 Java RDD Methods00:08
      • 3.46 Java RDD Methods (contd.)00:06
      • 3.47 Common Java RDD Methods00:10
      • 3.48 Spark Java Function Classes00:13
      • 3.49 Method for Combining JavaPairRDD Functions00:42
      • 3.50 Transformations in RDD00:34
      • 3.51 Other Methods00:07
      • 3.52 Actions in RDD00:08
      • 3.53 Key-Value Pair RDD in Scala00:32
      • 3.54 Key-Value Pair RDD in Java00:43
      • 3.55 Using MapReduce and Pair RDD Operations00:25
      • 3.56 Reading Text File from HDFS00:16
      • 3.57 Reading Sequence File from HDFS00:21
      • 3.58 Writing Text Data to HDFS00:18
      • 3.59 Writing Sequence File to HDFS00:12
      • 3.60 Using GroupBy00:07
      • 3.61 Using GroupBy (contd.)00:05
      • 3.62 Demo-Run a Scala Application Performing GroupBy Operation00:08
      • 3.63 Run a Scala Application Performing GroupBy Operation03:13
      • 3.64 Demo-Run a Scala Application Using the Scala Shell00:07
      • 3.65 Run a Scala Application Using the Scala Shell04:02
      • 3.66 Demo-Write and Run a Java Application00:06
      • 3.67 Write and Run a Java Application01:49
      • 3.68 Quiz
      • 3.69 Summary00:53
      • 3.70 Summary (contd.)00:59
      • 3.71 Conclusion00:15
    • Lesson 04 - Running SQL Queries Using Spark SQL 30:24
      • 4.1 Introduction00:12
      • 4.2 Objectives00:17
      • 4.3 Importance of Spark SQL01:02
      • 4.4 Benefits of Spark SQL00:47
      • 4.5 DataFrames00:50
      • 4.6 SQLContext00:50
      • 4.7 SQLContext (contd.)01:13
      • 4.8 Creating a DataFrame00:11
      • 4.9 Using DataFrame Operations00:22
      • 4.10 Using DataFrame Operations (contd.)00:05
      • 4.11 Demo-Run SparkSQL with a Dataframe00:06
      • 4.12 Run SparkSQL with a Dataframe08:53
      • 4.13 Interoperating with RDDs
      • 4.14 Using the Reflection-Based Approach00:38
      • 4.15 Using the Reflection-Based Approach (contd.)00:08
      • 4.16 Using the Programmatic Approach00:44
      • 4.17 Using the Programmatic Approach (contd.)00:07
      • 4.18 Demo-Run Spark SQL Programmatically00:08
      • 4.19 Run Spark SQL Programmatically00:01
      • 4.20 Data Sources
      • 4.21 Save Modes00:32
      • 4.22 Saving to Persistent Tables00:46
      • 4.23 Parquet Files00:19
      • 4.24 Partition Discovery00:38
      • 4.25 Schema Merging00:29
      • 4.26 JSON Data00:34
      • 4.27 Hive Table00:45
      • 4.28 DML Operation-Hive Queries00:27
      • 4.29 Demo-Run Hive Queries Using Spark SQL00:07
      • 4.30 Run Hive Queries Using Spark SQL04:58
      • 4.31 JDBC to Other Databases00:49
      • 4.32 Supported Hive Features00:38
      • 4.33 Supported Hive Features (contd.)00:22
      • 4.34 Supported Hive Data Types00:13
      • 4.35 Case Classes00:15
      • 4.36 Case Classes (contd.)00:07
      • 4.37 Quiz
      • 4.38 Summary00:49
      • 4.39 Summary (contd.)00:49
      • 4.40 Conclusion00:13
    • Lesson 05 - Spark Streaming 35:09
      • 5.1 Introduction00:11
      • 5.2 Objectives00:15
      • 5.3 Introduction to Spark Streaming00:50
      • 5.4 Working of Spark Streaming00:20
      • 5.5 Features of Spark Streaming
      • 5.6 Streaming Word Count01:34
      • 5.7 Micro Batch00:19
      • 5.8 DStreams00:34
      • 5.9 DStreams (contd.)00:39
      • 5.10 Input DStreams and Receivers01:19
      • 5.11 Input DStreams and Receivers (contd.)00:55
      • 5.12 Basic Sources01:14
      • 5.13 Advanced Sources00:49
      • 5.14 Advanced Sources-Twitter
      • 5.15 Transformations on DStreams00:15
      • 5.16 Transformations on Dstreams (contd.)00:06
      • 5.17 Output Operations on DStreams00:29
      • 5.18 Design Patterns for Using ForeachRDD01:15
      • 5.19 DataFrame and SQL Operations00:26
      • 5.20 DataFrame and SQL Operations (contd.)00:20
      • 5.21 Checkpointing01:25
      • 5.22 Enabling Checkpointing00:39
      • 5.23 Socket Stream01:00
      • 5.24 File Stream00:12
      • 5.25 Stateful Operations00:28
      • 5.26 Window Operations01:22
      • 5.27 Types of Window Operations00:12
      • 5.28 Types of Window Operations Types (contd.)00:06
      • 5.29 Join Operations-Stream-Dataset Joins00:21
      • 5.30 Join Operations-Stream-Stream Joins00:34
      • 5.31 Monitoring Spark Streaming Application01:19
      • 5.32 Performance Tuning-High Level00:20
      • 5.33 Performance Tuning-Detail Level
      • 5.34 Demo-Capture and Process the Netcat Data00:07
      • 5.35 Capture and Process the Netcat Data05:01
      • 5.36 Demo-Capture and Process the Flume Data00:08
      • 5.37 Capture and Process the Flume Data05:08
      • 5.38 Demo-Capture the Twitter Data00:07
      • 5.39 Capture the Twitter Data02:33
      • 5.40 Quiz
      • 5.41 Summary01:01
      • 5.42 Summary (contd.)01:04
      • 5.43 Conclusion00:12
    • Lesson 06 - Spark ML Programming 40:08
      • 6.1 Introduction00:12
      • 6.2 Objectives00:20
      • 6.3 Introduction to Machine Learning01:36
      • 6.4 Common Terminologies in Machine Learning
      • 6.5 Applications of Machine Learning00:22
      • 6.6 Machine Learning in Spark00:34
      • 6.7 Spark ML API
      • 6.8 DataFrames00:32
      • 6.9 Transformers and Estimators00:59
      • 6.10 Pipeline00:48
      • 6.11 Working of a Pipeline01:41
      • 6.12 Working of a Pipeline (contd.)00:45
      • 6.13 DAG Pipelines00:33
      • 6.14 Runtime Checking00:21
      • 6.15 Parameter Passing01:00
      • 6.16 General Machine Learning Pipeline-Example00:05
      • 6.17 General Machine Learning Pipeline-Example (contd.)
      • 6.18 Model Selection via Cross-Validation01:16
      • 6.19 Supported Types, Algorithms, and Utilities00:31
      • 6.20 Data Types01:26
      • 6.21 Feature Extraction and Basic Statistics00:43
      • 6.22 Clustering00:38
      • 6.23 K-Means00:55
      • 6.24 K-Means (contd.)00:05
      • 6.25 Demo-Perform Clustering Using K-Means00:07
      • 6.26 Perform Clustering Using K-Means04:41
      • 6.27 Gaussian Mixture00:57
      • 6.28 Power Iteration Clustering (PIC)01:17
      • 6.29 Latent Dirichlet Allocation (LDA)00:35
      • 6.30 Latent Dirichlet Allocation (LDA) (contd.)01:45
      • 6.31 Collaborative Filtering01:13
      • 6.32 Classification00:16
      • 6.33 Classification (contd.)00:06
      • 6.34 Regression00:42
      • 6.35 Example of Regression00:56
      • 6.36 Demo-Perform Classification Using Linear Regression00:08
      • 6.37 Perform Classification Using Linear Regression02:01
      • 6.38 Demo-Run Linear Regression00:06
      • 6.39 Run Linear Regression02:14
      • 6.40 Demo-Perform Recommendation Using Collaborative Filtering00:05
      • 6.41 Perform Recommendation Using Collaborative Filtering02:23
      • 6.42 Demo-Run Recommendation System00:06
      • 6.43 Run Recommendation System02:45
      • 6.44 Quiz
      • 6.45 Summary01:14
      • 6.46 Summary (contd.)00:57
      • 6.47 Conclusion00:12
    • Lesson 07 - Spark GraphX Programming 46:26
      • 7.001 Introduction00:14
      • 7.002 Objectives00:17
      • 7.003 Introduction to Graph-Parallel System01:14
      • 7.004 Limitations of Graph-Parallel System00:49
      • 7.005 Introduction to GraphX01:21
      • 7.006 Introduction to GraphX (contd.)00:06
      • 7.007 Importing GraphX00:10
      • 7.008 The Property Graph01:25
      • 7.009 The Property Graph (contd.)00:07
      • 7.010 Features of the Property Graph
      • 7.011 Creating a Graph00:14
      • 7.012 Demo-Create a Graph Using GraphX00:07
      • 7.013 Create a Graph Using GraphX10:08
      • 7.014 Triplet View00:30
      • 7.015 Graph Operators00:51
      • 7.016 List of Operators00:23
      • 7.017 List of Operators (contd.)00:05
      • 7.018 Property Operators00:18
      • 7.019 Structural Operators01:02
      • 7.020 Subgraphs00:21
      • 7.021 Join Operators01:09
      • 7.022 Demo-Perform Graph Operations Using GraphX00:07
      • 7.023 Perform Graph Operations Using GraphX05:46
      • 7.024 Demo-Perform Subgraph Operations00:07
      • 7.025 Perform Subgraph Operations01:37
      • 7.026 Neighborhood Aggregation00:43
      • 7.027 mapReduceTriplets00:42
      • 7.028 Demo-Perform MapReduce Operations00:08
      • 7.029 Perform MapReduce Operations09:18
      • 7.030 Counting Degree of Vertex00:32
      • 7.031 Collecting Neighbors00:28
      • 7.032 Caching and Uncaching01:10
      • 7.033 Graph Builders
      • 7.034 Vertex and Edge RDDs01:17
      • 7.035 Graph System Optimizations01:22
      • 7.036 Built-in Algorithms
      • 7.037 Quiz
      • 7.038 Summary01:12
      • 7.039 Summary (contd.)00:55
      • 7.040 Conclusion00:11
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Exam & certification

  • How to become a Certified Apache Spark and Scala Professional?

    To become a Certified Apache Spark and Scala professional, it is mandatory to fulfill both the following criteria:
    • Completing project given by Simplilearn. The project is evaluated by the lead trainer. You can submit your project through LMS.  In case, you have any queries or difficulties while solving project then you can get assistance from experts at SimpliTalk to clarify such queries & difficulties. For Online Classroom Training, in case you have doubts in implementing the project, you may attend any of the ongoing batches of Apache Spark and Scala Certification Training to get help in Project work.
    • Clearing the online examination with a minimum score of 80%. In case, you don’t clear the online exam in the first attempt, you can re-attempt the exam one more time.
    At the end of the course, you will receive an experience certificate stating that you have 3 months experience in implementing Spark and Scala Project.

    Note: It is mandatory that you fulfill both the criteria i.e. completion of any one Project and clearing the online exam with minimum score of 80%, to become a Certified Spark and Scala Professional.

  • What are the prerequisites for the course?

    The prerequisites for the Apache Spark and Scala course are:
    • Fundamental knowledge of any programming language
    • Basic understanding of any database, SQL, and query language for databases
    • Working knowledge of Linux- or Unix-based systems (not mandatory)
    It is recommended to do a Big Data Hadoop Developer Certification Training as a prerequisite as it provides an excellent foundation for Apache Spark & Scala certification.

  • What do I need to do to unlock my Simplilearn certificate?

    Online Classroom:
    • You need to attend one complete batch.
    • Complete 1 project and 1 simulation test with a minimum score of 60%.
    Online Self-Learning:
    • Complete 85% of the course.
    • Complete 1 project and 1 simulation test with a minimum score of 60%.

Reviews

Instructor is very experienced in these topics. I like the examples given in the classes.

It was really a great learning experience. Big Data course has been instrumental in laying the foundation for the beginners, both in terms of conceptual content as well as the practical lab. Thanks to Simplilearn team that it was not less than a live classroom..Really Appreciate it..

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The training has been very good. Trainer was right on the targeted agenda with great technical skills. He covered all the topics with good number of examples and allowed us to do hands-on as well.

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Great course! I really recommend it!

Well-structured course and the instructor is very good. He has a good grip on the subject and clears our doubts instantly and he makes sure that all the students understand things correctly.

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It was really a wonderful experience to have such real-time project discussion during the training session. It helped to learn in depth.

Great Class. Very interactive. Overview of HDFS and MapReduce was very helpful, it was smooth transition to the Apache Spark and Scala knowledge. The content is good. Overall, excellent training.

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Getting a high quality training from industry expert at your convenience, affordable with the resources you need to master what you are learning.

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The trainer has excellent knowledge on the subject and is very thorough in answering the doubts. I hope Simplilearn will always continue to give trainers like this.

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FAQs

  • What are the System Requirements?

    Your system needs to fulfil the following requirements:
    • 64-bit Operating System
    • 8GB RAM

  • How will the Labs be conducted?

    We will help you to set up a Virtual Machine with local access. The detailed installation guide is provided in the Learning Management System.

  • How will you do the projects and get certified?

    Problem statements along with Data points are provided in the Learning Management System.

    On the completion of the course, you have to submit the project which will be evaluated by the trainer. On successful evaluation of the project and completion of the online exam, you will get certified as Spark and Scala Professional.

  • Who are the trainers?

    Highly qualified and certified instructors with industry relevant experience deliver trainings.

  • What are the modes of training offered for this course?

    We offer this training in the following modes:

    Live Virtual Classroom or Online Classroom: With instructor led online classroom training, you have the option to attend the course remotely from your desktop, laptop via video conferencing. This format saves productivity challenges and decreases your time spent away from work or home.

    Online Self-Learning: In this mode, you will get the lecture videos and you can go through the course as per your comfort level.

  • What if I miss a class?

    We provide the recordings of the class after the session is conducted. So, if you miss a class then you can go through the recordings before the next session.

  • Can I cancel my enrolment? Do I get a refund?

    Yes, you can cancel your enrolment. We provide a complete refund after deducting the administration fee. To know more, please go through our refund policy.

  • What are the payment options?

    Payments can be made using any of the following options and a receipt of the same will be issued to you automatically via email.
    • Visa Debit/credit Card
    • American Express and Diners Club Card
    • Master Card
    • PayPal

  • I want to know more about the training program. Whom do I contact?

    Please join our Live Chat for instant support, call us, or Request a Call Back to have your query resolved.

  • Who are our Faculties and how are they selected?

    All our trainers are working professionals and industry experts with at least 10-12 years of relevant teaching experience.

    Each of them have gone through a rigorous selection process which includes profile screening, technical evaluation, and training demo before they are certified to train for us.  

    We also ensure that only those trainers with a high alumni rating continue to train for us.

  • What is Global Teaching Assistance?

    Our teaching assistants are here to help you get certified in your first attempt.

    They are a dedicated team of subject matter experts to help you at every step and enrich your learning experience from class onboarding to project mentoring and job assistance.

    They engage with the students proactively to ensure the course path is followed.

    Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls.  

    We also have a dedicated team that provides on demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.