Masters in Data Analytics

(5.0) G 4.5/5 f 4.5/5
Course Duration

72 hours

Live Project



Industry recognized

Training Format




Certified Learners


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Happy Clients


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About Masters in Data Analytics Course

We offer a comprehensive Master’s program in Artificial Intelligence to become a certified Artificial Intelligence Engineer. This Artificial Intelligence certification program gives training on the skills required to become a successful Artificial Intelligence Engineer. Throughout this exclusive AI online course, you'll master Deep Learning, Machine Learning, and the programming languages required to excel in this domain which will be very helpful for you to kick-start your career in Artificial Intelligence.

Masters in Data Analytics Course Overview

This is an Artificial Intelligence training program that is a comprehensive learning approach for mastering the domains of Artificial Intelligence, Data Science, Business Analytics, Business Intelligence, Python coding and Deep Learning. This training program enables you to take on challenging roles in the Artificial Intelligence domain.

The AI courses will make students industry-ready for Artificial Intelligence and Data Science job roles.Upon completion of this AI Engineer Program, you will receive the certificate from our side in the Artificial Intelligence courses on the learning path*. This certificate will testify to your skills as an expert in Artificial Intelligence.

Learning Objectives in Artificial Intelligence training program:-

This Artificial Intelligence Engineer Master’s Program is a blend of Artificial Intelligence, Data Science, Machine Learning, and Deep Learning, enabling you to the real-world implementation of advanced tools and models. The way our course is designed, to give you in-depth knowledge of Artificial Intelligence concepts including the essentials of statistics required for Data Science, Python programming, and Machine Learning. Through these AI courses, you will learn how to use Python libraries like NumPy, SciPy, Scikit, and essential Machine Learning techniques.

Why Become an Artificial Intelligence expert:-

The current and future demand is stumbling. According to the New York Times reports candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, and earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for AI Engineers with the required skills.

Skills to be covered in Artificial Intelligence training courses:-

You will be able to accomplish the following by the end of this AI training

  • Well Understanding of the meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence.
  • Build and design your own intelligent agents, applying them to create practical Artificial Intelligence projects, including games, machine learning models, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, and agent decision-making functions.
  • Skills to master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and also functions.
  • How to write your own Python scripts and perform basic hands-on data analysis using Jupyter notebook.
  • In-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing.
  • Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package.
  • Master the skill to understand the concepts of supervised and unsupervised learning models, including linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline, recommendation engine, and time series modeling.
  • Master your skills on advanced topics in Artificial Intelligence, like convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces.

Projects of Artificial Intelligence :-

This Artificial Intelligence program includes real-life projects in different domains. These projects are designed to help you master the key concepts of Artificial Intelligence like supervised and unsupervised learning, reinforcement learning, support vector machines, Deep Learning, neural networks, convolutional neural networks, and recurrent neural networks.

You will go through devoted and exclusive mentored classes in order to create a high-quality industry project, solving a real-world problem. You will have projects that can be showcased to potential employers as a testament to your learning.

  • Project 1: Fare Prediction for Uber | Domain: Delivery (Commerce)
  • Project 2: Test bench time reduction for Mercedes-Benz | Domain: Automobile
  • Project 3: Products rating prediction for Amazon | Domain: E-commerce
  • Project 4: Demand Forecasting for Walmart | Domain: Sales
  • Project 5: Improving customer experience for Comcast | Domain: Telecom
  • Project 6: NYC 311 Service Request Analysis | Domain: Telecommunication
  • Project 7: MovieLens Dataset Analysis | Domain: Engineering
  • Project 8: Stock Market Data Analysis | Domain: Stock Market

Instructor-led, Live & Interactive Sessions

72 Hours
Online (Instructor-led)
Masters in Data Analytics

Course Price at


[Fixed - No Negotiations]

How we prepare you

Artificial Intelligence

Upon completion of this program you will get 360-degree understanding of Machine Learning. This course will give you thorough learning experience in terms of understanding the concepts, mastering them thoroughly and applying them in real work environment.

Hands-on experience in a live project

You will be given industry level real time projects to work on and it will help you to differentiate yourself with multi-platform fluency, and have real-world experience with the most important tools and platforms.

Unlimited Mock Interview and Quiz Session

As part of this, You would be given complete interview preparations kit, set to be ready for the Machine Learning. This kit has been crafted by 200+ years industry experience and the experiences of nearly 10000 DevOpsSchool's Machine Learning learners worldwide.

Agenda of the Masters in Data Analytics CourseDownload Curriculum

  • Introduction of Artificial Intelligence
  • Data Science & Python
  • Machine Learning
  • Deep Learning
  • Natural Language processing(NLP)
  • Decoding Artificial Intelligence
  • Fundamentals of Machine Learning and Deep Learning
  • Machine Learning Workflow
  • Performance Metrics

Course Introduction

  • Introduction
  • Data Analytics - Importance
  • Digital Analytics: Impact on Accounting
  • Data Analytics Overview
  • Types of Data Analytics
  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Data Analytics - Amazon Example
  • Data Analytics Benefits Decision-Makin
  • Data Analytics Benefits: Cost Reduction
  • Data Analytics Benefits: Amazon Example
  • Data Analytics: Other Benefits
  • Introduction
  • Terminologies in Data Analytics - Part One
  • Terminologies in Data Analytics - Part Two
  • Types of Data
  • Qualitative and Quantitative Data
  • Data Levels of Measurement
  • Normal Distribution of Data
  • Statistical Parameters
  • Introduction
  • Data Visualization
  • Understanding Data Visualization
  • Commonly Used Visualizations
  • Frequency Distribution Plot
  • Swarm Plot
  • Importance of Data Visualization
  • Data Visualization Tools - Part One
  • Data Visualization Tools - Part Two
  • Languages and Libraries in Data Visualization
  • Dashboard Based Visualization
  • BI and Visualization Trends
  • BI Software Challenges
  • Introduction
  • The Data Science Domain
  • Data Science, Data Analytics, and Machine Learning - Overlaps
  • Data Science Demystified
  • Data Science and Business Strategy
  • Successful Companies Using Data Science
  • Travel Industry
  • Retail
  • E-commerce and Crime agencies
  • Analytical Platforms across Industries
  • Introduction
  • Data Science Methodology
  • From Business Understanding to Analytic Approach
  • From Requirements to Collection
  • From Understanding to Preparation
  • From Modeling to Evaluation
  • From Deployment to Feedback
  • Introduction00:33
  • 7.02 Analytics for Products or Services01:53
  • 7.03 How Google Uses Analytics02:30
  • 7.4 How LinkedIn Uses Analytics00:37
  • 7.05 How Amazon Uses Analytics02:03
  • 7.6 Netflix- Using Analytics to Drive Engagement00:56
  • 7.7 Netflix- Using Analytics to Drive Success02:49
  • 7.08 Media and Entertainment Industry01:10
  • 7.09 Education Industry02:57
  • 7.10 Healthcare Industry01:39
  • 7.11 Government02:31
  • 7.12 Weather Forecasting
  • 8.1 Introduction00:29
  • 8.2 Case Study: EY01:05
  • 8.3 Customer Analytics Framework00:59
  • 8.4 Data Understanding01:42
  • 8.5 Data Preparation00:50
  • 8.6 Modeling02:05
  • 8.7 Model Monitoring01:11
  • 8.8 Latest Trends in Data Analytics01:11
  • 8.9 Graph Analytics00:45
  • 8.10 Automated Machine Learning01:24
  • 8.11 Open Source AI00:52
  • 8.12 Key Takeaways
  • 1.001 Introduction02:15
  • 1.002 What Is in It for Me00:10
  • 1.003 Types of Analytics02:18
  • 1.004 Areas of Analytics04:06
  • Knowledge Check
  • 2.001 Introduction02:12
  • 2.002 What Is in It for Me00:21
  • 2.003 Custom Formatting Introduction00:55
  • 2.004 Custom Formatting Example03:24
  • 2.005 Conditional Formatting Introduction00:44
  • 2.006 Conditional Formatting Example101:47
  • 2.007 Conditional Formatting Example202:43
  • 2.008 Conditional Formatting Example301:37
  • 2.009 Logical Functions04:00
  • 2.010 Lookup and Reference Functions00:28
  • 2.011 VLOOKUP Function02:14
  • 2.012 HLOOKUP Function01:19
  • 2.013 MATCH Function03:13
  • 2.014 INDEX and OFFSET Function03:50
  • 2.015 Statistical Function00:24
  • 2.016 SUMIFS Function01:27
  • 2.017 COUNTIFS Function01:13
  • 2.018 PERCENTILE and QUARTILE01:59
  • 2.019 STDEV, MEDIAN and RANK Function03:02
  • 2.020 Exercise Intro00:35
  • 2.21 Exercise
  • Knowledge Check
  • 3.001 Introduction01:47
  • 3.002 What Is in It for Me00:22
  • 3.003 Pivot Table Introduction01:03
  • 3.004 Concept Video of Creating a Pivot Table02:47
  • 3.005 Grouping in Pivot Table Introduction00:24
  • 3.006 Grouping in Pivot Table Example 101:42
  • 3.007 Grouping in Pivot Table Example 201:57
  • 3.008 Custom Calculation01:14
  • 3.009 Calculated Field and Calculated Item00:25
  • 3.010 Calculated Field Example01:22
  • 3.011 Calculated Item Example02:52
  • 3.012 Slicer Intro00:35
  • 3.013 Creating a Slicer01:22
  • 3.014 Exercise Intro00:58
  • 3.15 Exercise
  • Knowledge Check
  • 4.001 Introduction01:18
  • 4.002 What Is in It for Me00:18
  • 4.003 What is a Dashboard00:45
  • 4.004 Principles of Great Dashboard Design02:16
  • 4.005 How to Create Chart in Excel02:26
  • 4.006 Chart Formatting01:45
  • 4.007 Thermometer Chart03:32
  • 4.008 Pareto Chart02:26
  • 4.009 Form Controls in Excel01:08
  • 4.010 Interactive Dashboard with Form Controls04:13
  • 4.011 Chart with Checkbox05:48
  • 4.012 Interactive Chart04:37
  • 4.013 Exercise Intro00:55
  • 4.14 Exercise1
  • 4.15 Exercise2
  • Knowledge Check
  • 5.001 Introduction02:12
  • 5.002 What Is in It for Me00:24
  • 5.003 Concept Video Histogram05:18
  • 5.004 Concept Video Solver Addin05:00
  • 5.005 Concept Video Goal Seek02:57
  • 5.006 Concept Video Scenario Manager04:16
  • 5.007 Concept Video Data Table02:03
  • 5.008 Concept Video Descriptive Statistics01:58
  • 5.009 Exercise Intro00:52
  • 5.10 Exercise
  • Knowledge Check
  • 6.001 Introduction01:51
  • 6.002 What Is in It for Me00:21
  • 6.003 Moving Average02:50
  • 6.004 Hypothesis Testing04:20
  • 6.005 ANOVA02:47
  • 6.006 Covariance01:56
  • 6.007 Correlation03:38
  • 6.008 Regression05:15
  • 6.009 Normal Distribution06:49
  • 6.010 Exercise1 Intro00:34
  • 6.11 Exercise 1
  • 6.012 Exercise2 Intro00:17
  • 6.13 Exercise 2
  • 6.014 Exercise3 Intro00:19
  • 6.15 Exercise 3
  • Knowledge Check
  • 7.001 Introduction01:17
  • 7.002 What Is in It for Me00:18
  • 7.003 Power Pivot04:16
  • 7.004 Power View02:36
  • 7.005 Power Query02:45
  • 7.006 Power Map02:06
  • Knowledge Check
    1.01 Course Introduction
  • 2.01 Getting Started with Tableau00:29
  • 2.02 Download and Install Tableau Public02:01
  • 2.03 Load Data from Excel03:42
  • 2.04 User Interface of Tableau Public
  • 3.01 Core Topics in Tableau00:31
  • 3.02 Dimension vs Measures02:42
  • 3.03 Discrete vs. Continuous01:27
  • 3.04 Application of Discrete and Continuous Fields04:05
  • 3.05 Aggregation in Tableau
  • 4.01 Creating Charts in Tableau00:43
  • 4.02 Bar Chart02:51
  • 4.03 Stacked Bar Chart02:01
  • 4.04 Line Chart03:38
  • 4.05 Scatter Plot02:55
  • 4.06 Dual-Axis Charts05:42
  • 4.07 Combined-Axis Chart02:01
  • 4.08 Funnel Chart02:54
  • 4.09 Cross Tabs01:50
  • 4.10 Highlight Tables02:22
  • 4.11 Maps03:17
  • 4.12 Measure Name and Measure Values
  • 5.01 Working with Metadata00:35
  • 5.02 Data Types05:08
  • 5.03 Rename, Hide, Unhide and Sort Columns03:42
  • 5.04 Default Properties of Fields04:09
  • 6.01 Filters in Tableau00:43
  • 6.02 Dimension Filter07:38
  • 6.03 Date Filter06:25
  • 6.04 Measure Filter03:39
  • 6.05 Visual Filter06:00
  • 6.06 Interactive Filter08:13
  • 6.07 Data source Filter02:27
  • 6.08 Context Filter
  • 7.01 Applying Analytics to the Worksheet00:42
  • 7.02 Sets06:54
  • 7.03 Parameters05:22
  • 7.04 Group05:50
  • 7.05 Calculated Fields06:16
  • 7.06 Date Functions05:37
  • 7.07 Text Functions05:28
  • 7.08 Bins and Histogram04:05
  • 7.09 Sort03:15
  • 7.10 Reference and Trend Lines05:06
  • 7.11 Table Calculations03:49
  • 7.12 Pareto Chart02:52
  • 7.13 Waterfall Chart
  • 8.01 Dashboards in Tableau00:41
  • 8.02 Dashboard05:17
  • 8.03 Working with Layout07:42
  • 8.04 Objects in Dashboard09:37
  • 8.05 Making Interactive Dashboard04:10
  • 8.06 Actions in Dashboard08:23
  • 8.07 Best Practices for Dashboard Creation00:59
  • 8.08 Dashboards for Mobile03:28
  • 8.09 Story03:22
  • Case Study
  • 9.01 Modifications to Data Connections00:37
  • 9.02 Edit Data Source02:33
  • 9.03 Union03:32
  • 9.04 Joins07:04
  • 9.05 Data Blending
  • 10.01 Level of Detail00:32
  • 10.02 Introduction to Level of Detail (LOD)02:54
  • 10.03 Fixed LOD05:09
  • 10.04 Include LOD03:33
  • 10.05 Exclude LOD02:58
  • 10.06 Publish to Tableau Public
  • Course End Objectives
  • Learning Objectives
  • Getting Started Analyzing Data in Python04:14
  • Importing and Exporting Data in Python04:13
  • Introduction to Data Analysis with Python00:50
  • Python Packages for Data Science02:28
  • The Problem01:51
  • Understanding the Data02:26
  • Introduction
  • Learning Objectives
  • Binning in Python01:47
  • Data Formatting in Python03:23
  • Data Normalization in Python03:34
  • Dealing with Missing Values in Python05:57
  • Indicator variables in Python02:00
  • Pre-processing Data in Python02:09
  • Learning Objectives
  • Analysis of Variance (ANOVA)03:58
  • Correlation - Statistics02:37
  • Correlation02:29
  • Descriptive Statistics04:39
  • Exploratory Data Analysis01:20
  • GroupBy in Python
  • Learning Objectives
  • Introduction01:44
  • Linear Regression and Multiple Linear Regression06:34
  • Model Evaluation using Visualization04:44
  • Polynomial Regression and Pipelines04:25
  • Measures for In-Sample Evaluation03:37
  • Prediction and Decision Making
  • Learning Objectives
  • Model Evaluation07:30
  • Overfitting Underfitting and Model Selection04:20
  • Grid Search04:33
  • Model Evaluation and Refinement00:21
  • Ridge Regression
  • Lesson 01 Course Introduction
  • Lesson 02 Introduction to Programming
  • Lesson 3 Programming Environment Setup
  • Lesson 4 OOPs Concept with Python
  • Lesson 5 Programming Fundamentals of Python
  • Lesson 6 File handling, Exception handling, and Package handling
  • Lesson 7 Data Analytics Overview
  • Lesson 8 Statistical Computing
  • Lesson 9 Mathematical Computing using NumPy
  • Lesson 10 - Data Manipulation with Pandas
  • Lesson 11 - Data visualization with Python
  • Lesson 12 - Introduction to Model Building
  • Practice Projects
    • Bike-Sharing Demand Analysis
  • 1.001 Overview00:44
  • 1.002 Business Decisions and Analytics04:33
  • 1.003 Types of Business Analytics03:53
  • 1.004 Applications of Business Analytics08:57
  • 1.005 Data Science Overview01:29
  • 1.006 Conclusion
  • 2.001 Overview00:31
  • 2.002 Importance of R05:20
  • 2.003 Data Types and Variables in R02:14
  • 2.004 Operators in R04:39
  • 2.005 Conditional Statements in R02:45
  • 2.006 Loops in R05:07
  • 2.007 R script01:44
  • 2.008 Functions in R02:58
  • 2.009 Conclusion
  • 3.001 Overview01:04
  • 3.002 Identifying Data Structures13:14
  • 3.003 Demo Identifying Data Structures14:05
  • 3.004 Assigning Values to Data Structures04:51
  • 3.005 Data Manipulation09:23
  • 3.006 Demo Assigning values and applying functions07:46
  • 3.007 Conclusion
  • 4.001 Overview00:29
  • 4.002 Introduction to Data Visualization03:03
  • 4.003 Data Visualization using Graphics in R18:50
  • 4.004 ggplot205:14
  • 4.005 File Formats of Graphic Outputs01:08
  • 4.006 Conclusion
  • 5.001 Overview00:21
  • 5.002 Introduction to Hypothesis02:06
  • 5.003 Types of Hypothesis03:13
  • 5.004 Data Sampling02:48
  • 5.005 Confidence and Significance Levels04:33
  • 5.006 Conclusion
  • 6.001 Overview00:28
  • 6.002 Hypothesis Test00:47
  • 6.003 Parametric Test14:36
  • 6.004 Non-Parametric Test08:31
  • 6.005 Hypothesis Tests about Population Means02:09
  • 6.006 Hypothesis Tests about Population Variance00:45
  • 6.007 Hypothesis Tests about Population Proportions01:11
  • 6.008 Conclusion
  • 7.001 Overview00:26
  • 7.002 Introduction to Regression Analysis01:11
  • 7.003 Types of Regression Analysis Models01:38
  • 7.004 Linear Regression08:59
  • 7.005 Demo Simple Linear Regression07:29
  • 7.006 Non-Linear Regression03:49
  • 7.007 Demo Regression Analysis with Multiple Variables13:29
  • 7.008 Cross Validation01:48
  • 7.009 Non-Linear to Linear Models02:06
  • 7.010 Principal Component Analysis02:45
  • 7.011 Factor Analysis00:26
  • 7.012 Conclusion
  • 8.001 Overview00:31
  • 8.002 Classification and Its Types04:24
  • 8.003 Logistic Regression03:35
  • 8.004 Support Vector Machines04:26
  • 8.005 Demo Support Vector Machines11:13
  • 8.006 K-Nearest Neighbours02:34
  • 8.007 Naive Bayes Classifier02:53
  • 8.008 Demo Naive Bayes Classifier06:15
  • 8.009 Decision Tree Classification09:47
  • 8.010 Demo Decision Tree Classification06:25
  • 8.011 Random Forest Classification02:01
  • 8.012 Evaluating Classifier Models06:04
  • 8.013 Demo K-Fold Cross Validation04:09
  • 8.014 Conclusion
  • 9.001 Overview00:17
  • 9.002 Introduction to Clustering02:57
  • 9.003 Clustering Methods07:47
  • 9.004 Demo K-means Clustering11:15
  • 9.005 Demo Hierarchical Clustering05:02
  • 9.006 Conclusion
  • 10.001 Overview00:15
  • 10.002 Association Rule06:20
  • 10.003 Apriori Algorithm05:19
  • 10.004 Demo Apriori Algorithm10:37
  • 10.005 Conclusion


In Artificial Intelligence Course a Participant will get total 5 real time scenario based projects to work on, as part of these projects, we would help our participant to have first hand experience of real time scenario based software project development planning, coding, deployment, setup and monitoring in production from scratch to end. We would also help our participants to visualize a real development environment, testing environment and production environments.


As part of this, You would be given complete interview preparations kit, set to be ready for the Artificial Intelligence hotseat. This kit has been crafted by 200+ years industry experience and the experiences of nearly 10000 DevOpsSchool Machine Learning learners worldwide.


1 Course for Artificial Intelligence
Faculty Profile Check
Lifetime Technical Support
Lifetime LMS access
Top 46 Tools
Mock Interviews after Training
Training Notes
Step by Step Web Based Tutorials
Training Slides
Training + Additional Videos

With the demand for AI in a broad range of industries, Our AI course is well suited for a variety of roles and disciplines, including:

  • Developers who are aspiring to be an Artificial Intelligence Engineer or Machine Learning Engineer
  • Analytics Managers who are leading a team of analysts
  • Information Architects who want to gain expertise in Artificial Intelligence algorithms
  • Analytics professionals who want to work in machine learning or artificial intelligence
  • Freshers and Graduates looking to build a career in Artificial Intelligence and machine learning
  • Professionals who would like to tackle Artificial Intelligence in their fields to get more insight

Participants in this course should have:

  • Understanding of the fundamentals of Python programming
  • Basic knowledge of statistics

You will have the skills required to help you to land a dream job. Jobs that are ideal for Artificial Intelligence trained professionals include:

  • Artificial Intelligence Engineer
  • Data Scientist
  • Analytics Manager/Lead
  • Machine Learning Engineer
  • Statistical Programming Specialist

Masters in Data Analytics CERTIFICATION

What are the benefits of "Artificial Intelligence" Certification?

During this program, you will be engaged in various projects and assignments, which include real-world industry scenarios. Which will be very helpful to you and you can expedite your career effortlessly.You would be glad to know that our certification training is recognized all around the world.

Masters in Data Analytics COURSE FAQs

Because We provide the best Artificial Intelligence training course that gives you all the skills needed to work in the domains of AI, Machine Learning, Deep Learning, Data Science with R Statistical computing and Python to give the professionals an added advantage. After the completion of the training, you will be awarded the Artificial Intelligence certification.

You can know more about us on Web, Twitter, Facebook and linkedin and make your own decision. Also, you can email us to know more about us. We will call you back and help you more about the trusting DevOpsSchool for your online training.

You will have the skills required to help you to land a dream job. Jobs that are ideal for Artificial Intelligence trained professionals are like Artificial Intelligence Engineer, Analytics Manager/Lead, Machine Learning Engineer, Statistical Programming Specialist.

All of our trainers are industry experts with years of relevant experience in the industry. All of them have gone through a scrupulous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

We provide the best Artificial Intelligence training course that gives you all the skills needed to work in the domains of AI, Machine Learning, Deep Learning, Data Science with R Statistical computing and Python to give the professionals an added advantage. After the completion of the training, you will be awarded the Artificial Intelligence certification.

You will be working on real-time projects and step-by-step assignments which have high relevance in the corporate world, and the curriculum is designed by industry experts. Upon the completion of the training course, you can apply for some of the best dream jobs in top MNCs around the world and can get top salaries. We offer lifetime access to videos, course materials, at no extra fee. Hence, it is clearly a one-time investment.

You will never lose any lecture at DevOpsSchool. There are two options available: You can view the class presentation, notes and class recordings that are available for online viewing 24x7 through our Learning management system (LMS). You can attend the missed session, in any other live batch or in the next batch within 3 months. Please note that, access to the learning materials (including class recordings, presentations, notes, step-bystep-guide etc.)will be available to our participants for lifetime.

Please email to

  • Google Pay/Phone pe/Paytm
  • NEFT or IMPS from all leading Banks
  • Debit card/Credit card
  • Xoom and Paypal (For USD Payments)
  • Through our website payment gateway

If you are reaching to us that means you have a genuine need of this training, but if you feel that the training does not fit to your expectation level, You may share your feedback with trainer and try to resolve the concern. We have no refund policy once the training is confirmed.

Our fees are very competitive. Having said that if the participants are in a group then following discounts can be possible based on the discussion with representative.

  • Two to Three students – 10% Flat discount
  • Four to Six Student – 15% Flat discount
  • Seven & More – 25% Flat Discount

DevOpsSchool provides "Masters in Data Analytics Course" certificate accredited by which is industry recognized and does holds high value. Participant will be awarded with the certificate on the basis of projects, assignments and evaluation test which they will get within and after the training duration.



Abhinav Gupta, Pune


The training was very useful and interactive. Rajesh helped develop the confidence of all.


Indrayani, India


Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.


Ravi Daur , Noida


Good training session about basic Devops concepts. Working session were also good, howeverproper query resolution was sometimes missed, maybe due to time constraint.


Sumit Kulkarni, Software Engineer


Very well organized training, helped a lot to understand the DevOps concept and detailed related to various tools.Very helpful


Vinayakumar, Project Manager, Bangalore


Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.


Abhinav Gupta, Pune


The training with DevOpsSchool was a good experience. Rajesh was very helping and clear with concepts. The only suggestion is to improve the course content.

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  DevOpsSchool is offering its industry recognized training and certifications programs for the professionals who are seeking to get certified for DevOps Certification, DevSecOps Certification, & SRE Certification. All these certification programs are designed for pursuing a higher quality education in the software domain and a job related to their field of study in information technology and security.