Hadoop and core java can be learnt by anyone who is aspiring to work in IT.

Batches can be scheduled as per students requirement and timing.

Weekend batches can also be conducted.

Use cases on different topics will be handed over.

Required softwares will be installed on students laptop/system.

Option of Home tutor can be kept open and it completely depends on distance and number of students.

After learning big data / hadoop or core java , will focus on interview questions and how to prepare for it.

Being a freelancer, certificate cant be provided.

Course fees can be paid in installments. And mode of payment option is also feasible.

Tools covered in Big data / Hadoop course are - Pig, Hive, HBase, Oozie, Sqoop, Flume, MapReduce.

What is Hadoop?
Hadoop is an open source software framework written in Java for distributed storage and processing of very large datasets on multiple clusters. The basic philosophy of Hadoop is to reduce dependence on expensive legacy system hardware to enable distributed parallel processing of very large amounts of data across inexpensive, standard, commodity servers to process and store data without any volume limitations. Hadoop makes the process of storing and managing data economical and reliable.
Key features of Hadoop?
Reliable- Fail Safe technology that prevents loss of data even in an event of hardware failure.
Powerfulâ?? unique storage method based on distributed file system resulting in faster data processing.
Scalableâ?? stores and distributes datasets to operate in parallel, allowing businesses to run applications on thousands of nodes.
Cost-effectiveâ?? runs on commodity machines & network
Simple and flexible APIsâ?? enables a large ecosystem of solution providers such as log processing, recommendation systems, data warehousing, fraud detection, etc.
The reason:
The reason for Hadoopâ??s success in the banking and finance domain is its ability to address various issues faced by the financial industry at minimal cost and time. Despite the various benefits of Hadoop, to apply it to a particular problem needs due diligence. Some of the scenarios in which it is used are:
Fraud Detection
Hadoop addresses most common industry challenges like fraud, financial crimes and data breaches effectively. Analyzing point of sale, authorization and transactions, and other points, banks can identify and mitigate fraud. Big Data also helps in picking up unusual patterns and alerting banks of the same, while also drastically reducing the time and resources required to complete these tasks.
Risk Management
Assess risks accurately using Big Data Solutions. Hadoop gives a complete and accurate view of risk and impact, enabling firms to makes informed decisions by analyzing transactional data to determine risk based on market behavior, scoring customers, and potential clients.
Data Storage and Security
Protection, easy storage and access of financial data are the optimal needs of banks and finance firms. While Hadoop Distributed File System (HDFS) provides scalable and reliable data storage designed to span large clusters of commodity servers, MapReduce processes each node in parallel, transferring only the package code for that node. This means information is stored in more than one cluster but with additional safety to provide a better and safer data storage option.
Analysis
Bank need to analyze unstructured data residing in various sources like social media profiles, emails, calls, complaint logs, discussion forums, etc. as well as through traditional sources like transactional data, cash and equity, trade and lending, etc. to get a better understanding of their customers. Hadoop allows financial firms to access and analyze this data and also provides accurate insights to help make the right decision.
Hadoop is also used in other departments like customer segmentation and experience analysis, credit risk assessment, targeted services, etc.