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Category:

programming

Data Science. Programming

1.

Data Science
Programming
an advocate of
concrete computing –
and HMC's mascot

2.

About myself
Who
Faisal Ahmed
Where
TalTech
What
Research Communication and Software
Engineering
Now
Narva College
Contact
[email protected]

Data?!
• Neighbor's name
• A place they consider home
• Are they working at a company now?
Where?
• How many U.S. states have they visited?
• Their favorite unhealthy food… ?
• Do they have any "Data Science" background?
(statistics, machine learning, CS)

4.

5.

• Neighbor's name
Data!
Zachary Dodds
• A place they consider home
Pittsburgh, PA
• Are they working at a company now?
Where?
• How many U.S. states have they visited?
• Their favorite unhealthy food… ?
Harvey Mudd
44
M&Ms
• Do they have any "Data Science" background?
(statistics, machine learning, CS)
mostly CS for me…
be sure to set up your login + profile for the submission site…

6.

Data Science concerns
Is "Data Science"
important or just trendy?

7.

Data Science concerns
Hmmm…

8.

the companies are expanding as fast as the data!

9.

There's certainly a lot of it!
Data, data everywhere…
1.8 ZB
8.0 ZB
800 EB
Data produced each year
161 EB
1 Exabyte
logarithmic scale
1 Zettabyte
5 EB
120 PB
100-years of HD video + audio
1 Petabyte
1 Petabyte == 1000 TB
1 TB = 1000 GB
60 PB
Human brain's capacity
14 PB
2002
2006
2009
2011
2015
References
(2015) 8 ZB: http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf
(2002) 5 EB: http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/execsum.htm
(2011) 1.8 ZB: http://www.emc.com/leadership/programs/digital-universe.htm
(2009) 800 EB: http://www.emc.com/collateral/analyst-reports/idc-digital-universe-are-you-ready.pdf
(life in video) 60 PB: in 4320p resolution, extrapolated from 16MB for 1:21 of 640x480 video
(w/sound) – almost certainly a gross overestimate, as sleep can be compressed significantly!
(2006) 161 EB: http://www.emc.com/collateral/analyst-reports/expanding-digital-idc-white-paper.pdf
(brain) 14 PB: http://www.quora.com/Neuroscience-1/How-much-data-can-the-human-brain-store

10.

I'd call it data,
not information
wisdom
knowledge
information
data

11.

Big Data?
I agree with this…

12.

Make data easier to use ~ by using it!
It may be true that
Data Science isn't a
science – but that
doesn't mean it's
not useful!

13.

IST 380 ~ the big picture
What?
Data Science
Programming
Why?
Data Rules
All of our insights – large and small, permanent and
ephemeral, natural and artificial – come about
through the integration of lots of data.
Data Science simply recognizes that the rules and
skills behind those insights are widely applicable…

14.

A few examples…
Make3d
Andrew Ng ~
Computers and
Thought award,
2009
How is this being done?
and how do we succeed?
… Data Science is at the heart of computer science

15.

A few examples…
Learning to
Powerslide
Stanford's
Autonomous
Vehicles project
(Thrun et al.)
… Data Science is at the heart of computer science

16.

A few examples…
Learning ground
from obstacles
"my summer was
finding that red line"
… Data Science is at the heart of computer science

17.

A few examples…
classification
segmentation
Learning ground from obstacles

18.

Insights beyond science

19.

Marketing

20.

Visualization
Motivation

21.

22.

Recommender Systems
predicting
movie ratings

23.

Netflix Prize
(I don't know this guy)
Bob Bell, winner of the "Netflix prize"
Napoleon Dynamite = 1.22
Batman Begins = .75
Some films are difficult to predict…
Finding Nemo = ??
Lord of the Rings = ??

24.

Netflix Prize
(I don't know this guy)
Bob Bell, winner of the "Netflix prize"
Napoleon Dynamite = 1.22
Batman Begins = .75
Finding Nemo = .67
Lord of the Rings = .42
Some films are difficult to predict… and others are easier!

25.

26.

Why IST 380 ?
Specific skills:
R statistical environment (and the S programming language)
Experience with several statistical analyses (descriptive statistics)
Experience with predictive statistics (modeling) and
machine learning algorithms
Broad background:
Final project ~ open-ended with datasets of your choice
You'll be confident and capable with whatever datasets you
encounter in the future – on your own or as part of a team.

27.

About IST 380 …

28.

Details
Web Page:
http://www.cs.hmc.edu/~dodds/IST380
Assignments, online text, necessary files, lecture slides are linked
First week's assignment: Getting started with R
Textbook
An introduction to Data Science
freely available online
jsresearch.net/groups/teachdatascience/
and many online resources…
Programming: R
www.r-project.org/
Grab both of
these now…

29.

Homepage
Go to the course page
Grab R and the text from
these two links…
http://www.cs.hmc.edu/~dodds/IST380/

30.

Homework
Assignments
~ 2-5 problems/week
~ 100 points
extra credit, often
Due Tuesday of the following week by 11:59 pm.
Assignment 1 due Tuesday, February 5.
1 week + 1 day…

31.

Homework
Assignments
~ 2-5 problems/week
~ 100 points
extra credit, often
Due Tuesday of the following week by 11:59 pm.
Assignment 1 due Tuesday, February 5.
Working on programs:
Submitting programs:
Today's Lab:
On your own or in groups of 2.
Divide the work at the keyboard evenly!
at the submission website
install software ensure accounts are working
try out R - the first HW is officially due on 2/5

32.

Outline
approximate!
Weeks 1-5
"Data Science"
Weeks 6-10
"Machine Learning"
Weeks 11-15
using R
descriptive statistics
predictive statistics
probability distributions
statistical modeling
support vector machines (SVMs)
nearest neighbors (NN)
random forests
No breaks?!
k-means algorithm
Final Project

33.

Grading
Grades
Based on points percentage
~ 800 points for assignments
~ 400 points for the final project
if score >= 0.95: grade = "A"
if score >= 0.90: grade = "A-"
if score >= 0.86: grade = "B+"
see the course syllabus for the full list...
Final project
• the last ~4 weeks will work towards a larger, final project
• there will be a short design phase and a short final presentation
• choose your own problem to study (I'll have some suggestions, too.)
• I'd encourage you to connect R and our Data Science techniques
to other datasets or projects that you use/need/like, etc.

34.

Academic Honesty
This course operates under CGU's (and all of Claremont Schools')
Academic Honesty policies…
•Your work must be your own. This must be true for the whole
team, if you're working in a pair.
•Consulting with others (except team members or myself) is
encouraged, but has to be limited to discussion and debugging
of problems. Sharing of written, electronic, or verbal
solutions/files/code is a violation of CGU’s academic honesty
policy.
•A reasonable guideline: Work is your own if you could delete
all of it and recreate it yourself.

35.

Thoughts?

36.

Getting to know…
R

37.

Getting to know…
http://lang-index.sourceforge.net/#categ
R
R is the programmer's toolkit for statistics; SAS, Stata,
SPSS are preferred by those in business intelligence

38.

Getting to know…
R
Free… and very well supported online…

39.

Getting to know…
R
R is responsive, up-to-date, and flexible: Data Science vs. Statistics

40.

Getting to know…
R
1) Find the IST 380 course webpage
www.cs.hmc.edu/~dodds/IST380/
2) Download and install R
3) Run R and try some basic commands at the prompt:
6 * 7
rnorm(10)
x <- 380

41.

Getting started!
1) Open Matloff's Why R? notes
2) Skip ahead to page 7, the "5 minute example session"
3) Try out the commands in section 2.2 to get started…
4) When you finish, save your session and submit it!
This is problem 1 this week

42.

Saving your session
1) Create a folder named hw1, perhaps on your desktop
2) Use the Save to file… (Windows) or Save as…
(Mac) in order to save your current console session into
hw1
3) Name that file pr1.txt
4) From your operating system, open up that file in
order to confirm it contains your whole session!
This is problem 1 this week

43.

Submitting your work
1) Zip up hw1 into hw1.zip
2) From the course webpage, click on the submission
site link.
3) Choose a submission site login name & let me know!
4) Once your account is made, login, change your password
to something you know, and submit hw1.zip
5) You can submit again – all copies are saved…
You've completed Problem 1!
troubles? email me!
This webserver can be
spacey -- I should know!

44.

Reflection
Assignment?
Creating a vector?
Printing?
Average and standard deviation?
Comments?
Comments?

45.

R types
You can use mode() to view the type of a variable.

46.

Where's the big data?
c ~ concatenate
Vectors are R lists of a single type of element

47.

Where's the big data?
c ~ concatenate
the colon : also
creates vectors
Vectors are R lists of a single type of element

48.

Analyzing vectors – try these…
Square brackets [] can "subset" (or "slice") vectors

49.

Analyzing vectors
you can use a
boolean vector
to subset
another vector
Square brackets [] can "subset" (or "slice") vectors

50.

NA
R uses NA to represent data that is "not available"
The function is.na( ) tests for NA
What is going on here?

51.

NA
R uses NA to represent data that is "not available"
The function is.na( ) tests for NA
What is going on here?
This uses subsetting to remove NA values!

52.

Data frames
R's fundamental data structures are data frames
The next tutorial will introduce them…

53.

Irises…
virginica
setosa
data() yields many built-in data files. This is iris

54.

Subsetting iris data
df[rows,cols]
As with vectors, you can "subset" data frames.

55.

Lab…
The 2nd part of each class meeting dedicated to lab work.
I welcome you to stay for the lab, but it is not required.
Today's lab:
Work through Santorico and Shin's Tutorial for the R
Statistical Package and submit the console sessions as
pr2_1.txt, pr2_1.txt, pr2_1.txt, pr2_1.txt, and pr2_1.txt.
This is a nice reinforcement of vectors, introduction to
data frames, and a look at the graphics that R supports.

56.

Homework
Problem 3: Challenge exercises in R
These will reinforce the "subsetting" and dataanalysis introduction from pr2's tutorial.
Problem 4: Introduction to Data Science, early chapters
This is a fuller background on R and the field
of data science
(submit your console session for both of these…)

57.

Lab !

58.

CS vs. IS and IT ?
greater integration
system-wide issues
smaller details
machine specifics
www.acm.org/education/curric_vols/CC2005_Final_Report2.pdf

59.

CS vs. IS and IT ?
Where will IS go?

60.

CS vs. IS and IT ?

61.

IT ?
Where will IT go?

62.

IT ?

63.

64.

The bigger picture
Weeks 10-12
Weeks 13-15
Objects
Final Projects
Week 10
Week 13
classes vs. objects
final projects
Week 11
Week 14
methods and data
final projects
Week 12
Week 15
inheritance
final exam

65.

66.

state reminders…

67.

68.

• Neighbor's name
Data!
Zachary Dodds
• A place they consider home
Pittsburgh, PA
• Are they working at a company now?
Where?
• How many U.S. states have they visited?
• Their favorite unhealthy food… ?
44
M&Ms
• Do they have any "Data Science"
(statistics, machine learning, CS)
background?
Harvey Mudd
mostly CS for me…
This class is truly
seminar-style:
we're devloping
expertise in this
field together.
be sure to set up your login + profile for the submission site…

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