Course Brief

How big is BIG? Become a big data expert through an intensive training program customised across various levels designed specifically for you. It will make participants solve real-time problems with huge datasets. Through this intensive program we aim to train the participants in a way that they are prepared to appear for International Certifications as mentioned below:

  • Hortonworks Data Platform Certified Developer:Java (HDPCD:Java)
  • Hortonworks Data Platform Certified Developer :Spark(HDPCD:Spark)
  • Hortonworks Data Platform Certified Developer (HDPCD)
  • CCA Spark from Cloudera

Course Structure:

There are 4 modules in BigData Specialization.

  • DataScience with R
  • Hadoop with MapReduce using Java
  • Hadoop Ecosystem (Sqoop, Flume, Pig, Hive)
  • Spark with Scala

    Java Language features (quick overview). Object oriented programming. Creating Java Program (source file declaration, compilation, execution). Class access modifiers. What is an interface? Abstract class? Local, static, final variables?

    Learning Outcomes:

    • Understand how to build Java applications and high level overview of the Java language features

    Object Orientation in more detail. Constructors. Data Encapsulation. Inheritance. Is-a, has-a. Polymorphism. Overriding/Overloading. Creating interfaces and their concrete classes. Static variables and methods. Coupling and cohesion.

    Learning Outcomes:

    • Declare Interfaces. Declare, Initialize, and Use Class Members. Use Overloading and Overriding. Develop Constructors. Describe Encapsulation, Coupling, and Cohesion.Use Polymorphism. Relate Modifiers and Inheritance. Use Superclass Constructors and Overloaded Constructors. Use IS-A and HAS-A Relationships

    Passing variables into methods. Array declaration, Construction and Initialization. Boxing and unboxing. Using wrapper classes. Garbage collection.

    Learning Outcomes:

    • Use Class Members. Develop Wrapper Code & Autoboxing Code. Determine the Effects of Passing Variables into Methods. Recognize when Objects Become Eligible for Garbage Collection

    Assignment, Relational, Instanceof, arithmetic, conditional and logical operators.

    Learning Outcomes:

    • To learn and apply the different types of operators supported in Java like arithmetic, conditional, relational etc

    Flow control (if, switch, labeled statements, while, for, do etc). Exceptions and its related keywords. Handling exceptions. Exception Hierarchy, Assertions (enabling and disabling assertions).

    Learning Outcomes:

    • Use if and switch Statements. Develop for, do, and while Loops. Use break and continue Statements. Develop Code with Assertions. Use try, catch, and finally Statements. State the Effects of Exceptions. Recognize Common Exceptions

    Collections overview. Object class methods (equal, hashcode etc). Different types of collections and their usage. Generic types. Polymorphism and Generics etc.

    Learning Outcomes:

    • Design Using Collections. Override equals() and hashCode(). Distinguish == and equals(). Use Generic Versions of Collections Including Set, List, and Map. Use Type Parameters, Write Generics methods. Use java.util to Sort and Search. Use Comparable and Comparator

    Inner classes, Method local inner classes, Anonymous Inner classes, Static nested classes.

    Learning Outcomes:

    • Using Inner Classes. Method-Local Inner Classes. Anonymous Inner Classes. Static Nested Classes

    Defining, Instantiating, and Starting Threads. Thread States and Transitions Synchronizing Threads. Interthread communication.

    Learning Outcomes:

    • Threading concept, Start New Threads, Recognize Thread States and Transitions. Use Object Locking to Avoid Concurrent Access. Write Code that Uses wait(), notify(), or notifyAll()

    Using the Javac and Java commands. Static imports. How to create JAR files.

    Learning Outcomes:

    • How to use Packages and Imports. Determine Runtime Behavior for Classes and Command-Lines. Use Classes in JAR Files Use Classpaths to Compile Code.

    Purpose of web application development. Various elements of web applications.

    Learning Outcomes:

    • Introduction to web application development.
    • Developing dynamic web applications using Servlet's

    Introduction to Hibernate,Hibernate Architecture.

    Learning Outcomes:

    • Object Persistence,Working with Persistent Objects.
    • Persistent Classes
    • Components,Associations
    • Transactions and Concurrency,HQL,Cache

    Overview of Spring3 modules,AspectOrientedProgramming.

    Learning Outcomes:

    • Dependency Injection /Inversion of control IOC Containers.
    • Spring 3 Expression Language(SpEL)
    • Annotation Driven Configuration in Depth
    • Spring Web MVC Annotation driven
    • Spring JDBC,Object Relational Mapping Intergration,Spring Transaction (TX) Management

    Struts 2 - Overview,Architecture.

    Learning Outcomes:

    • Introduction to Ajax.
    • Presentation with Struts Tags
    • Application Development with Struts 2
    • Action,Results,Interceptors

    Understand the advantages of the REST architecture for web services,Developing REST Full Web services using JAX-RS.

    Learning Outcomes:

    • Manage XML content using XML Schema and JAXB
    • Understanding Resources
    • Using Http Methods to represent CRUD operations

    Defining SOAP Messages with WSDL,Role of SOAP in Web services Anatomy of a WSDL document.

    Learning Outcomes:

    • Simple Web Services,Apache Axis2 Web Service Clients
    • Apache Axis2 Web Service End-points
    • Exposing Plain Old Java Objects (POJOs) as Web Services Implementing code-first Web services
    • Implementing Web Service Clients in Java Generating client code from WSDL

Mr. Younus

Prof. Mohammed Younus Shariff has over 10 years of satisfying training experience. His style of teaching leaves the students asking for more. Many a student has benefitted from interaction with him.

He is an Asst.Professor with the Department of Computer Science at The Keshav Memorial Institute of Technology (KMIT)

Prof. Shariff teaches Java,Python, Data Science with python, HTML5 , CSS3 and Android Programming.

    Introduction to DBMS, What is a Database, What is Management System, Different views of data, Advantages and disadvantages of RDBMS,Introduction to SQL,Sub language of SQL (DDL, DML, DQL/DRL, TCL & DCL).

    Learning Outcomes:

    • Understand the RDBMS concepts and to get an understanding of SQL at high level

    What is a database table?, How do we create tables?, How do we insert Data?, How to retrieve/select data?, How do we delete data?, How do we update data?

    Learning Outcomes:

    • Basic SQL related to creating, inserting, deleting and updating data

    Logical operators, Between operators, In operator, Pattern matching operators.

    Learning Outcomes:

    • Different SQL operators and their usage

    Detailed coverage of DDL (Data definition language):CREATE, ALTER, DROP, TRUNCATE, RENAME.

    Learning Outcomes:

    • Understand the different DDL statements

    MySql Functions, Aggregate functions,Scalar functions (String functions, date related, numeric related, conversion function, null related functions.),Group by, order by, having clause.

    Learning Outcomes:

    • Understand the aggregate functions and group by/order by and having clause as part of SQL

    Inner Join, Left Join, Right join and full join.

    Learning Outcomes:

    • Understanding Joins in SQL

    Triggers, Procedures, Functions, Views and ACID properties.

    Learning Outcomes:

    • Understand how to create stored procedures, triggers, functions, views and ACID properties

    Normalization (1NF, 2NF, 3NF & BCNF).

    Learning Outcomes:

    • Role of Normalization for good RDBMS design

Ms. Jyothi SanjeevaMani

Ms. Jyothi SanjeevaMani has over 15 years of satisfying teaching and technical training experience. She is a Research Scholar of Big Data Analytics from a reputed university. As a technical trainer she trained many students in industry oriented subjects like C, C++, Java, MySQL, Oracle (SQL, PL/SQL), Python, Linux, Openstack, BigData - Hadoop(MapReduce, Pig, Hive, Sqoop, Flume), Data Science with both Python and R.

She is an Asst.Professor with the Department of IT at The Keshav Memorial Institute of Technology (KMIT).

She is a dedicated, resourceful and a result oriented instructor, who strives to help students change marginal grades into good grades.

Design data architecture and manage the data for analysis, understand various sources of data like sensors/signal/GPS etc. data management, data quality(noise, outliers, missing values, duplicate data) and data preprocessing.

Export all the data onto the cloud eg AWS/Rackspace etc.

Maintain healthy, safe and secure working environment:

Introduction, workplace safety, report accidents and emergencies, protect health and safety as your work, course conclusion, assessment.

Introduction to big data tools like Hadoop, Spark, Impala etc, Data ETL process, identify gaps in data and follow up for decision making

Provide data/information in standard formats:

Introduction, knowledge management, standardized reporting and compliances, decision models, course conclusion,assessment.

Run descriptive to understand the nature of the available data, collate all the data sources to suffice business requirements, run descriptive statistics for all the variables and observe the data changes, outlier detection and elimination.

Hypothesis testing and determining the multiple analytical methodologies, train model on 2/3 sample data using various statistical/machine learning algorithms, test model on 1/3 sample or prediction etc.

Prepare the data for visualization, use tools like Tableau, QuickView and D3, draw insights out of visualization tool.

Product implementation.

Mr.Neil Gogte
Big Data Analytics

Mr. Neil Gogte is the Director and heads the Keshav Memorial Institute of Technology. (KMIT) He holds a Masters Degree in Computer Science from IIT - Mumbai (1987) and B.E from Osmania University, Hyderabad (1985). He started his career with CMC Ltd., and was part of the team at Sun Microsystems, USA. He also headed the Leapstone Inc.’s India Offshore development center.

Mr. Neil Gogte founded Teleparadigm Networks Ltd in 2001, a public limited and software services provider. The company offers a wide range of services in Telecom Building Practices, Healthcare, Finance and mobile data service based applications. He established Genesis Solutions Pvt. Ltd. (1991) an IT education and training firm. He offered training and consultancy to various projects involving technologies like C, C++, Oracle, Oracle financials, SAP, Java, Microsoft.NET, J2ME and BREW. Since 1991 till date, he has been the guiding force behind a number of engineering graduates. He has been a very successful technocrat - entrepreneur in the area of high end technology training with over 25 years of experience in the IT industry.
A true visionary with a multitude of skill sets; a natural leader without much a do, he is the motivating force for the team at Inferdata to start and keep up the good work.

  • Are there any pre requisites for learning Hadoop?

    Basic knowledge of Java and Mysql will help.

  • How long does it take to complete this Specialization?

    Most learners are able to complete the Specialization in about four and a half months.

  • Do I need to take the courses in a specific order?

    We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.

  • What will I be able to do upon completing this Specialization?

    Upon completion as a Big Data Specialization you will be able to help a company with the following:

    • Hadoop Certification

      This specialization will unlock great career opportunities as a Hadoop developer. Become a Hadoop expert by learning concepts like Pig, Hive, Flume and Sqoop. Get industry-ready with some of the best Big Data projects and real-life use-cases.

    • Cost reduction

      Big data technologies like Hadoop and cloud-based analytics provide substantial cost advantages.

    • Faster, better decision making
    • Analytics has always involved attempts to improve decision making, and big data doesn't change that. Large organizations are seeking both faster and better decisions with big data, and they're finding them. Driven by the speed of Hadoop and in-memory analytics, several companies are now focused on speeding up existing decisions.

    • New products and services

      Perhaps the most interesting use of big data analytics is to create new products and services for customers. Online companies have done this for a decade or so, but now predominantly offline firms are doing it too.

  • Are there any pre requisites for R ?

    No requirements are needed to learn R. The only knowledge that needed to learn R is basic statistical knowledge.

  • Why you should learn R first for data science

    • R is becoming the lingua franca for data science. That's not to say that it's the only language, or that it's the best tool for every job. It is, however, the most widely used and it is rising in popularity.
    • Beyond tech giants like Google, Facebook, and Microsoft, R is widely in use at a wide range of companies including Bank of America, Ford, TechCrunch, Uber, and Trulia.
    • R is popular in academia: R isn't just a tool for industry. It is also very popular among academic scientists and researchers.
    • Learning the "skills of data science" is easiest in R. To do this, you'll need to master the 3 core skill areas of data science: data manipulation, data visualization, and machine learning. Mastering these skill areas will be easier in R than almost any other language.

  • What are the payment options?

    You can pay by Credit Card, Debit Card or Net Banking from all the leading banks. We use a Payment Gateway.

  • Are there any pre requisites for Hadoop Ecosystem?

    Basic knowledge of Java will help.

  • What will I be able to do upon completing Hadoop?

    This specialization will unlock great career opportunities as a Hadoop developer. Become a Hadoop expert by learning concepts like Pig, Hive, Flume and Sqoop. Get industry-ready with some of the best Big Data projects and real-life use-cases.

  • Do you provide placement assistance?

    Inferdata has lot of recruitment firms contacts us for our students profiles from time to time. Since there is a big demand for this skill, we help our certified students get connected to prospective employers. Having said that, please understand that we don't guarantee any placements however if you go through the course diligently and complete the assignments and exercises you will have a very good chance of getting a job.

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