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Key Features



About The Program

IBM is the second-largest predictive analytics and Machine Learning solutions provider globally The Forrester Wave report, September 2018. A joint partnership with Data2businessinsights and IBM introduces students to integrated blended learning, making them experts in Big Data Engineering. This Big Data Engineer certification course developed in collaboration with IBM will make students industry ready to start their career as Big Data Engineer.

IBM is a leading cognitive solution and cloud platform company, headquartered in Armonk, New York, offering a plethora of technology and consulting services. Each year, IBM invests $6 billion in research and development and has achieved five Nobel prizes, nine US National Medals of Technology, five US National Medals of Science, six Turing Awards, and 10 Inductions in US Inventors Hall of Fame.

What can I expect from this Data2businessinsights Big Data Engineer Master's Program developed in collaboration with IBM?

Upon completion of this Big Data Engineer Master's Program, you will receive the certificates from IBM(for IBM courses) and Data2businessinsights for the courses in the learning path. These certificates will testify to your skills as an expert in Big Data Engineering. You will also receive the following:

  • Access to IBM Cloud Lite Account
  • Industry-recognized Big Data Engineer Master's Certificate from DeepNeuron

Data Scientist is one of the hottest professions.IBM predicts the demand for Data Scientists will rise by 28% by 2020. Data Scientist Master’s program co-developed with IBM encourages you to master skills including statistics, hypothesis testing, data mining, clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, Hadoop, Spark, PROC SQL, SAS Macros, recommendation engine, supervised, and unsupervised learning and more.

  • Machine Learning project management methodology
  • Data Collection - Surveys and Design of Experiments
  • Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application
  • Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
  • Balanced versus Imbalanced datasets
  • Cross Sectional versus Time Series vs Panel / Longitudinal Data
  • Batch Processing vs Real Time Processing
  • Structured versus Unstructured vs Semi-Structured Data
  • Big vs Not-Big Data
  • Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
  • Sampling techniques for handling Balanced vs. Imbalanced Datasets
  • What is the Sampling Funnel and its application and its components?
    • Population
    • Sampling frame
    • Simple random sampling
    • Sample
  • Measures of Central Tendency & Dispersion
    • Population
    • Mean/Average, Median, Mode
    • Variance, Standard Deviation, Range

A Big Data is the top ranking professional in any analytics organization. Glassdoor ranks Big Datas first in the 25 Best Jobs for 2019. In today’s market, Big Datas are scarce and in demand. As a Data Scientist, you are required to understand the business problem, design a data analysis strategy, collect and format the required data, apply algorithms or techniques using the correct tools, and make recommendations backed by data.

Data Visualization helps understand the patterns or anomalies in the data easily and learn about various graphical representations in this module. Understand the terms univariate and bivariate and the plots used to analyze in 2D dimensions. Understand how to derive conclusions on business problems using calculations performed on sample data. You will learn the concepts to deal with the variations that arise while analyzing different samples for the same population using the central limit theorem.

  • Gain an in-depth understanding of data structure and data manipulation
  • Understand and use linear and non-linear regression models and classification techniques for data analysis
  • Obtain an in-depth understanding of supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
  • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
  • Gain expertise in mathematical computing using the NumPy and Scikit-Learn packages
  • Understand the different components of the Hadoop ecosystem
  • Learn to work with HBase, its architecture, and data storage, learning the difference between HBase and RDBMS, and use Hive and Impala for partitioning
  • Understand MapReduce and its characteristics, plus learn how to ingest data using Sqoop and Flume
  • Master the concepts of recommendation engine and time series modeling and gain practical mastery over principles, algorithms, and applications of machine learning
  • Learn to analyze data using Tableau and become proficient in building interactive dashboards

Programming Languages,Tools & Packages

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Big Data Course Modules

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

  • 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
Concepts & practicals
  • 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 preparation
  • 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?
Concepts & practicals
  • 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
Job oriented: Hands-On preparation
  • 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?
Concepts & practicals
  • 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
Job oriented: Hands-On preparation
  • 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
Concepts & practicals
  • 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
Job oriented: Hands-On preparation
  • 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
Concepts & practicals
  • 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?
Job oriented: Hands-On preparation
  • 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
Concepts & practicals
  • 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.
Job oriented: Hands-On preparation
  • 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 Data2Businessinsights team


Earn your Big Data certificate

Our Big Data program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.

Differentiate yourself with a Big Data Certificate

The knowledge and Big Data skills you've gained working on projects, simulations, case studies will set you ahead of the competition.

Share your achievement

Talk about it on Linkedin, Twitter, Facebook, boost your resume, or frame it - tell your friends and colleagues about it.

Data Scientist

DeepNeuron Testimonials


  • Why Should I Learn Hadoop from DeepNeuron?

    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 Data2businessinsights Hadoop course which is the most recognized name in Hadoop training and certification. Data2businessinsights 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 Data2businessinsights Hadoop training 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.

  • Can I request for a support session if I need to better understand the topics?

    DeepNeuron 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.

  • What kind of projects are included as part of the training?

    DeepNeuron 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.

  • Does Deepneuron offer job assistance?

    Deepneuron 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.

  • Is it possible to switch from self-paced training to instructor-led training?

    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.

  • Does the job assistance program guarantee me a Job?

    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.

  • *For which all courses will I get certificates from IBM?

    Following are the list of courses for which you will get IBM certificates:

    • R Programming for Data Science
    • Python for Data Science

  • How do I earn the Master’s certificate?

    Upon completion of the following minimum requirements, you will be eligible to receive the Data Scientist Master’s certificate that will testify to your skills as an expert in Data Science.


    Course completion certificate

    Data Science and Statistics Fundamentals Required 85% of Online Self-paced completion
    Data Science with R Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
    Data Science with SAS Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
    Data Science with Python Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in course-end assessment and successful evaluation in at least 1 project
    Machine Learning and Tableau Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and successful evaluation in at least 1 project
    Big Data Hadoop and Spark Developer Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation of at least 1 project
    Capstone Project Required Attendance of 1 Live Virtual Classroom and successful completion of the capstone project

  • How do I enroll for the Data Scientist course?

    You can enroll in this Data Science training on our website and make an online payment using any of the following options:

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal

    Once payment is received you will automatically receive a payment receipt and access information via email.

  • If I need to cancel my enrollment, can I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our Refund Policy.

  • I am not able to access the online Data Science courses. Who can help me?

    Yes, we do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support. portal.

  • Who are the instructors and how are they selected?

    All of our highly qualified Data Science trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous 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.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. 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.

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