BigData

Intro To Big Data

BigData.IntroToBigData History

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'''Hortonworks Academic Partner'''
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* Office hours: '''MW 10:30-12:00'''
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* Office hours: '''M 9:00-12:00'''
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* Building and Room: ''CEC B146B'''
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* Building and Room: '''CEC B146B'''
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! CS 591 - 001

!! News:

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! CS 567
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* Class Time: '''MW 9:00-10:15 AM'''
* Building and Room: '''ME 300'''
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* Class Time: '''MW 12:00-1:15 PM'''
* Building and Room: ''CEC B146B'''
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* Background in: '''data mining, machine learning or statistics'''
* UNM Learn: CS-567 (Fall 2015)

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* Background in: '''Data Mining, Machine Learning or Statistics'''
* UNM Learn: CS-567 (Fall 2016)

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* Office: ''' FEC 325 '''
* Office hours: '''TBD'''
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* Office: ''' CARC 2004A '''
* Office hours: '''MW 10:30-12:00'''
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* Class Time: '''MW 10:00-11:15 AM'''
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* Class Time: '''MW 9:00-10:15 AM'''
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* Building and Room: '''Centennial Engineering Center 1026'''
* Prerequisites: Fluent in at least one of the following programming languages: '''Python, Java, or C'''
* Preferred: background in '''data mining, machine learning or statistics'''
* UNM Learn: CS-591-001 (Fall 2014)

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* Building and Room: '''ME 300'''
* Prerequisites: Fluent in at least one of the following programming languages: '''Python, Java, or Scala'''
* Background in: '''data mining, machine learning or statistics'''
* UNM Learn: CS-567 (Fall 2015)

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* Office hours: '''M 11:30-12:30 AM''' and '''F 10:00-12:00 AM'''
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* Office hours: '''TBD'''
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'''Supported by AWS in Education Grant award'''
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'''Supported by AWS in Education Grant award'''
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'''Supported by AWS in Education Grant award'''


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* Office hours: '''MW 11:30-12:30 AM''' and '''T 10:00-11:00 AM'''
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* Office hours: '''M 11:30-12:30 AM''' and '''F 10:00-12:00 AM'''
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* Preferred: background in data mining, machine learning or statistics
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* Preferred: background in '''data mining, machine learning or statistics'''
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* Class Time: '''MW 10:30-11:15 AM'''
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* Class Time: '''MW 10:00-11:15 AM'''
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* Prerequisites: Fluent in at least one of the following programming languages: '''Python, Java, C, or Matlab'''
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* Prerequisites: Fluent in at least one of the following programming languages: '''Python, Java, or C'''
* Preferred: background in data mining, machine learning or statistics
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* Building and Room: '''TBD'''
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* Building and Room: '''Centennial Engineering Center 1026'''
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!! [[https://www8.unm.edu/pls/banp/bwlkfcwl.P_FacClaList?crn=48491 | Register!]]
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!! [[https://www8.unm.edu/pls/banp/bwckschd.p_disp_detail_sched?term_in=201480&crn_in=48491 | Register!]]
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[[Poster session BigData 2013 | Poster session to be held on Dec 13th at 1 pm in CARC]]
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[[Poster session BigData 2013]]
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!! [[ https://www8.unm.edu/pls/banp/bwckschd.p_disp_detail_sched?term_in=201380&crn_in=48491 | View catalog entry ]]
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!! [[https://www8.unm.edu/pls/banp/bwlkfcwl.P_FacClaList?crn=48491 | Register!]]
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[[Poster session BigData 2013 | Poster session to be held on Dec 13th at 1 pm in CARC]]

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* Class Time: '''MWF 9:00-9:50 AM'''
* Building and Room: '''CEC 1026'''
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* Class Time: '''MW 10:30-11:15 AM'''
* Building and Room: '''TBD'''
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* UNM Learn: CS-591-001 (Fall 2013)
* Facebook group: https://www.facebook.com/groups/207533772733004/
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* UNM Learn: CS-591-001 (Fall 2014)

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* Office hours: '''M 10:00-12:00 AM''' and '''T 10:00-11:00 AM'''


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* Office hours: '''MW 11:30-12:30 AM''' and '''T 10:00-11:00 AM'''


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* Introduction to Cloud computing and Amazon EC2

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!! [[ https://www8.unm.edu/pls/banp/bwckschd.p_disp_detail_sched?term_in=201380&crn_in=48491 | View catalog entry ]]
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!! [[ https://www8.unm.edu/pls/banp/bwckschd.p_disp_detail_sched?term_in=201380&crn_in=48491 | View catalog entry ]]


!! Archive

[[Poster session BigData 2013 | Poster session to be held on Dec 13th at 1 pm in CARC
]]
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!! News:

[[Poster session BigData 2013 | Poster session to be held on Dec 13th at 1 pm in CARC]]

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* Office hours: '''M 10:00-11:00 AM''' and '''T 9:00-11:00 AM'''


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* Office hours: '''M 10:00-12:00 AM''' and '''T 10:00-11:00 AM'''


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Intro to Big Data - 48491 - CS 591 - 001
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! CS 591 - 001

!! COURSE INFORMATION

* Class Time: '''MWF 9:00-9:50 AM'''
* Building and Room: '''CEC 1026'''
* Prerequisites: Fluent in at least one of the following programming languages: '''Python, Java, C, or Matlab'''
* UNM Learn: CS-591-
001 (Fall 2013)
* Facebook group: https://www.facebook.com/groups/207533772733004/

!!! Instructor
* '''Trilce Estrada''', Assistant Professor
* Email: '''estrada@cs.unm.edu'''
* Office: ''' FEC 325 '''
* Office hours: '''M 10:00-11:00 AM''' and '''T 9:00-11:00 AM'''


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! For more information visit the '''[[Syllabus]]'''
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! For more information look at the '''[[Syllabus]]'''
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This course explores key data analysis and management techniques, which applied to massive datasets are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and scientific discovery at large scale. In particular, we examine the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases, and stream computing engines. Additionally we review machine learning  methods that make possible the efficient analysis of large volumes of data in near real time. Finally, this course is highly interactive and based on the problem-based learning philosophy; students are expected to make use of said technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges.

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In this course we explore key data analysis and management techniques, which applied to massive datasets are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and scientific discovery at large scale. In particular, we examine the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases, and stream computing engines. Additionally we review machine learning  methods that make possible the efficient analysis of large volumes of data in near real time.

This
course is highly interactive and based on the problem-based learning philosophy; students are expected to make use of said technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges.

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This course explores key data analysis and management techniques, which applied to massive datasets are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and scientific discovery at large scale. In particular, we examine the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases, and stream computing engines. In the second section of the course we review machine learning  methods that make possible the efficient analysis of large volumes of data in near real time. Finally, in the third section of the course students are expected to make use of said technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges.

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This course explores key data analysis and management techniques, which applied to massive datasets are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and scientific discovery at large scale. In particular, we examine the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases, and stream computing engines. Additionally we review machine learning  methods that make possible the efficient analysis of large volumes of data in near real time. Finally, this course is highly interactive and based on the problem-based learning philosophy; students are expected to make use of said technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges.

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!! [[View catalog entry | https://www8.unm.edu/pls/banp/bwckschd.p_disp_detail_sched?term_in=201380&crn_in=48491]]
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!! [[ https://www8.unm.edu/pls/banp/bwckschd.p_disp_detail_sched?term_in=201380&crn_in=48491 | View catalog entry ]]
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! For more information visit the '''[[Syllabus]]'''
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! For more information visit the '''[[Syllabus]]'''

!! [[View catalog entry | https://www8.unm.edu/pls/banp/bwckschd.p_disp_detail_sched?term_in=201380&crn_in=48491]]
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Intro to Big Data - 48491 - CS 591 - 001
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For more information visit the '''[[Syllabus]]'''
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! For more information visit the '''[[Syllabus]]'''
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At the end of this course, the student will become familiar with the fundamental concepts of Big Data management an analytics; will become competent in recognizing challenges faced by applications dealing with very large volumes of data as well as in proposing scalable solutions for them; and will be able to understand how Big Data impacts business intelligence, scientific discovery, and our day-to-day life.
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At the end of this course, the student will become familiar with the fundamental concepts of Big Data management an analytics; will become competent in recognizing challenges faced by applications dealing with very large volumes of data as well as in proposing scalable solutions for them; and will be able to understand how Big Data impacts business intelligence, scientific discovery, and our day-to-day life.

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For more information visit the '''[[Syllabus]]'''
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!! Course description
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!! Course description:
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! Introduction to Big Data
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!! Course description
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! Introduction to Big Data

The field of computer science is experiencing a transition from computation-intensive to data-intensive problems, wherein data is produced in massive amounts by large sensor networks, new data acquisition techniques, simulations, and social networks. Efficiently extracting, interpreting, and learning from very large datasets requires a new generation of scalable algorithms as well as new data management technologies.

This course explores key data analysis and management techniques, which applied to massive datasets are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and scientific discovery at large scale. In particular, we examine the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases, and stream computing engines. In the second section of the course we review machine learning  methods that make possible the efficient analysis of large volumes of data in near real time. Finally, in the third section of the course students are expected to make use of said technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges.


!! Core topics:

* Large databases and their evolution.
* Big Data technology and trends, special consideration made to the Map-Reduce paradigm.
* Searching, indexing, and their implications to memory management.
* Information extraction and feature selection.
* Supervised-, unsupervised-learning, and stream mining.


!! Course objectives:

At the end of this course, the student will become familiar with the fundamental concepts of Big Data management an analytics; will become competent in recognizing challenges faced by applications dealing with very large volumes of data as well as in proposing scalable solutions for them; and will be able to understand how Big Data impacts business intelligence, scientific discovery, and our day-to-day life.