BIG Data & Hadoop Applications in Education

Slide ‹#›© 2015 BlueCamphor Technologies (P) Ltd. www.skillspeed.com
Applications of BIG Data in
Education

Introduction to Big Data
Big data is the term for a collection of data sets so
large and complex that it becomes difficult to
process using on-hand database management
tools or traditional data processing applications
Systems / Enterprises generate huge amount of
data from Terabytes to Petabytes
Understanding Big Data can give you innovative
solutions to various queries.

Applications of BIG Data in Education
• Higher Education Analytics
• Student Engagement
• Bookstore Effectiveness

BIG Data Applications in Education
Student Engagement
Data mining can help universities to get a holistic perspective about the students. BIG Data
answers quesitons like which learning behaviours are associated with better understanding and
higher grades or what is the future course selection for this student. This can also
accommodate a students interest in social & extra-curricular activites. This holistic perspective
allows institutions to create immersive learning experiences for all students.
Get Started with BIG Data & Hadoop
Higher Education Analytics
BIG Data enables the maximization of student learning & success in higher education. For
example, a student is often faced with questions like, what is the best major for me? Similarily,
instructors wonder, how should I customize learning paths so that no student is left behind?
The tracking of student performance, extra-curricular interactions and social behavior results in
the creation of a profile which is mapped with student profiles from the institution network to
suggest the most relevant major. Teachers are also instant access to student performances
based on which they can generate customized learning paths.

Bookstore Effectiveness
Big Data is used improve bookstore profitability using analytics-driven applications like
merchandising effectiveness and textbook inventory optimization.
Large retailers today find the combination of books bought together and sell them as a bundle
making customer experience hassle-free. Additionally, they can also understand the topics in
trend based on social media analytics to plan their purchases and inventories.
BIG Data Applications in Education

Current Examples of Big Data in Education
iParadigms
 iParadigms was founded by graduate student researchers at UC Berkeley who created software to monitor the
recycling of papers in their large undergraduate classes. Encouraged by the interest from their peers, they
assembled a team of instructors, mathematicians, and computer scientists to create the world's leading cloud-
based plagiarism detection and online grading service. iParadigms products serve more than 10,000 institutions
in 126 countries and process over 80 million submissions annually.
Course Smart
 Course Smart embeds analytics directly into digital textbooks. These analytics provide an “engagement index
score,” which measures how much students are interacting with their etextbook. This ranges from tracking
events such as text-highlighting, page-views, time spent on each page etc. The resultant engagement score index
carries out the accurate predictions of student learning path outcomes.

© 2015 BlueCamphor Technologies (P) Ltd.
How Are Insights Derived from BIG
Data?
Say Hello to Hadoop Development :)

Introduction to Hadoop
Apache Hadoop is a framework that allows the distributed processing of large data sets across clusters of
commodity computers using a simple programming model.
It is an Open-Source Data Management technology with extensive storage and distributed processing.
Hadoop
Characteristics
Flexible
Reliable
Economical
Scalable Get Started with BIG Data & Hadoop

Why Should You Learn Hadoop?
• Hadoop is a modern BIG Data processing framework which utilizes distributed computing clusters.
• It can process, manage and store unstructured data with absolute ease.
• Due to its scalability & effectiveness, companies are heavily adopting Hadoop.
• It has become the standard for storing, processing and analyzing big data.
• BIG Data & Hadoop professionals are in extremely high demand.
• This is the first step to becoming a data scientist or data architect.

Reports – 2015
Here are some of the other Reports that convey the same message:
• “It is expected that Hadoop and Big Data Analytics will grow to around $13.9 billion from 2012 to 2017.”
(Markets and Markets Research Report)
• “The Data market currently with the fastest growth are Hadoop and NoSQL software and services.”
(Technology Research Organization, Wikibon)
• “The Big Data market will likely hit $23.8 billion with a 31.7% rise per year.” (IDC Report)
• “Almost 90% organizations have embarked on Hadoop related projects and thus Hadoop skills are in huge
demand.” (According to the Big Data Executive Survey 2013)
• “Companies hiring Hadoop professionals Analytics professionals obtain a 250 percent hike in their
salaries.” (According to Analytics Industry Report)
Gartner had predicted, “Hadoop will be in most advanced analytics products by 2015.” By far and large, this
prediction has turned out to be true. Hadoop is “The Technology” to harness Big Data

Predictions for 2015
According to the Forrester Report:
• A new data economy will arise, Hadooponomics. Due to its vast data prowess, enterprise adoption of
Hadoop will become compulsory. All those companies, which were unsure of adopting Hadoop till 2014,
are bound to adopt it in 2015
• The surge in demand for Hadoop job professionals will be fulfilled by Java professionals & other software
engineers who will upgrade their skills
• The creation of SQL-on-Hadoop options will further enhance usability & adoption
• Due to the introduction of Yarn (Version 2.0), the usage of Hadoop will go beyond analytics. It will
transform into an application platform giving birth to new frameworks in the Hadoop ecosystem
• New Hadoop sources will emerge from organizations like Oracle, HP, Tibco and SAP. Vendors such as Red
Hat, VMware and Microsoft will include Hadoop in their operating Systems

Why SkillSpeed?
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Curriculum
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Course Topics
Module 1
Introduction to Big
Data and Hadoop
Module 2
HDFS Internals, Hadoop
Configurations and
Data Loading
Module 3
Introduction to Map
Reduce
Module 4
Advanced Map Reduce
Concepts
Module 5
Introduction to Pig
Module 6
Advanced Pig and
Introduction to Hive
Module 7
Advanced Hive
Concepts
Module 8
Extending Hive and
HBase Introduction
Module 9
Advanced HBase and
Oozie Introduction
Module 10
Project Set-up
Discussion

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BIG Data & Hadoop Applications in Education

BIG Data & Hadoop Applications in Education

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