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Big Data: Data Analysis Boot CampIntroduction and Overview

Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhD

29 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 201929 March 2019

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Table of contents (1 of 1)

1 IntroductionThe global view

2 OverviewThe world from 50,000feet.Text

3 AdministriviaMiscellaneous andnecessary things

4 Process Overview5 Q & A6 Conclusion7 References8 Files9 Vita

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The global view

Big Data: Data Analysis Boot Camp

We will cover aspects common toall Big Data investigations,including: defining Big Data,surveying tools and techniquesfor processing Big Data, andvisualizing selected aspects ofBig Data.The emphasis of the camp is tounderstand what is Big Datadata analysis beyond themarketing hype of the 3Vs ofvolume, variety, and velocity,

Image from [1].

More detailed information at:https://www.odu.edu/cepd/bootcamps/data-analysisc©Old Dominion University

https://www.odu.edu/cepd/bootcamps/data-analysis

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The world from 50,000 feet.

Things we’ll be covering over the next three days:

Friday1 Administrivia2 What is BD?3 What is R?4 Looking at the built-in

iris and Titanic datasetsSaturday

1 Visualizing data withdifferent packages

2 Exploring cluster analysis(of different types)

3 Linear regression andsome variants

4 Classification techniques5 Text analysis6 Serial vs. parallel

processing

Sunday1 R limitations2 R and Hadoop3 R and SQL and No-SQL

DBMs4 Hands-on with real-world

crime data5 Wrap-up

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Text

Not required reading, but referenced throughout ourtime together.

Learning Predictive Analyticswith R (LPAR)

Big Data Analytics with R(BDAR)

Not necessary, but really helpful.c©Old Dominion University

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Text

Code samples

There are lots. And, they looklike this:

library(cluster.datasets)

data(all.us.city.crime.1970)

crime =

all.us.city.crime.1970

plot(crime[5:10])

Available in a separate file embedded in each presentation.

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Miscellaneous and necessary things

All things related to paper work.

Parking – front and backwithout permitsBreaks – yes we’ll have them.Lunch – yes places near by:right a main light to “fast food”Text books – recommended butnot necessary, have good ideas,techniquesNon-credit optionCredit option – two additionalassignment

Hours – 9AM to 5PM with abreak for lunchSunday access – yes, check inwith securitySoft copies – all presentations,and software are availableComputer logins and passwords– will be coordinatedBreak room – across hallBathrooms – around elevator

Other things as well.

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Miscellaneous and necessary things

Soft copies available from Internet

All information(presentations, scripts, anddata) is available on yourVM desktop (static)

All information is availablevia the I’net (dynamic)

Errata updated nightly

I’m not a web designer, nor do Iplay one on TV.

http://www.cs.odu.edu/

~ccartled/Teaching/

c©Old Dominion University

http://www.cs.odu.edu/~ccartled/Teaching/http://www.cs.odu.edu/~ccartled/Teaching/

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Miscellaneous and necessary things

Same image.

http://www.cs.odu.edu/~ccartled/Teaching/

c©Old Dominion University

http://www.cs.odu.edu/~ccartled/Teaching/

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How do Data Wrangling, Analysis, and Visualization fittogether?

Notionally, there are threedistinct phases in data analysis.

1 Data wrangling – getting theraw data into a usable form

2 Data analysis – evaluatingand understanding the data

3 Data visualization –presenting the analyticalresults in an intelligiblemanner

Management continues across allphases. The other phases mayoverlap.

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Q & A time.

Q: What is the square root of4b2?A: To be or not to be.

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What have we covered?

Where we are.Where we’re going.How we’ll get there.

Now!! On to exploring the world of Big Data!

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References (1 of 1)

[1] Vangie Beal, Big Data,https://www.webopedia.com/TERM/B/big_data.html,2017.

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https://www.webopedia.com/TERM/B/big_data.html

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Files of interest

1 Code snippets

c©Old Dominion University

library(cluster.datasets)data(all.us.city.crime.1970)crime = all.us.city.crime.1970plot(crime[5:10])

"Chuck Cartledge"

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Who am I?

Father

Husband (only 42 years, but it seemslonger)

PhD, Computer Science, 2014

CAPT, USN retired 2004 (31+ years)

Professional software developer (38 years)

A perennial student

1st computer: 1970, donated ICBMguidance computer, machine code,paper/mylar tape, and drum memory

Interests: autonomic systems, real–time applications, distributed processing,long-term preservation of digital data, Big Data

c©Old Dominion University

IntroductionThe global view

OverviewThe world from 50,000 feet.Text

AdministriviaMiscellaneous and necessary things

Process OverviewQ & AConclusionReferencesFilesVita