BIG Data & Hadoop Applications in Education

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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.

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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.

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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.



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How Are Insights Derived from BIG Data?

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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 CharacteristicsFlexible

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?

Course Curriculum

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Instructor Led Live Virtual Sessions

Lifetime access to Course

Content via LMS

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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

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