final project! (& wind sprints) - brown...

Final Project! (& wind sprints)

CSCI 0931 - Intro. to Comp. for the Humanities and Social Sciences 1

Final Project

Define Problem Find Data

Write a set of instructions

Solution

Final Project

Solution

ANYTHING Numerical Data

Textual Data Twitter Data

Geographical Data ...

Final Project

Solution

ANYTHING COMPUTATIONAL

Find Trends/Patterns Predict Outliers/Groups

Describe Anomalies Assess Someone’s Claims

Final Project

Solution

ANY TOOL Excel or Python

Probably...

Final Project

Solution

Probably...

ANY ANALYSIS Plots, Tables, KML Files, ...

Final Project

Solution

Probably...

ANY ANALYSIS Plots, Tables, KML Files, ...

Only Constant: you will use computers to process data automatically

• What do we wish we had known before?

• Example Final Projects

• General Project Description – Scope

– Timeline

• K-means in Python

What do we wish we had known before?

– Timeline

• Project 2 Workshop

Example 1: Where should I Live?

I want to buy a house somewhere near my hometown. Where should I buy?

Kasey Hass’s Project https://sites.google.com/a/brown.edu/house-hunting-mecklenburg-county/home

In Each Zip Code: Rent price: Red dollar sign Vacancies: For sale sign Income: Green dollar sign Marriage rate: Wedding rings

Example 2: Athletes & Geography

Malcolm Gladwell’s Outliers • Professional Canadian hockey players

– Mostly born in January. – Few are born in December. – Junior league’s cutoff is Dec. 31st

• Any association between professional players and where they were born?

Daniel Newmark’s Project https://sites.google.com/a/brown.edu/daniel-s-data/project-4--distribution-of-athletes

Football (NFL) Hockey (NHL)

Example 3: Growth of Top Digital Media Companies

How does the Growth Rate Compare in the Past 10 Years?

Example 3: Growth of Top Digital Media Companies

Nicholas Talbott’s Project https://sites.google.com/site/growthofdigitalmediacompanies/home

YouTube Video: http://www.youtube.com/watch?v=QDtDF6_mrkc

One KML File for Each Year Logo Size shows Growth

– Timeline

• K-means

Scope – Think Big! (& Have Backup Plans)

Solution

Utilize MANY data sources to test your claim.

Solution

Analyze the same dataset in DIFFERENT ways:

Make a number of related claims/hypotheses

Solution

GENERALIZE the Program Try to Implement something we

haven’t discussed

Solution

Display the Results in Different Ways

GENERALIZE the Program Try to Implement something we

haven’t discussed

– Timeline

• Project 2 Workshop

Timeline

Sun Mon Tues Wed Thurs Fri Sat

4/14 4/15 4/16 4/17 4/18

Final Project Out Project 2 Due

Guest and In-Class Work Time

4/21 4/22 4/23 4/24 4/25

Proposal Due

4/28 4/29 4/30 5/1 5/2

In-Class Work Time (Reading Period)

5/5 5/6 5/7 5/8 5/9

Final Project Due and Presentations

Meet With Staff At Least Once

Before You Begin...

Solution

Say you have a vague idea of what you want to do....

Before You Begin...

Solution

Where is the data? What does it look like?

What format will be easiest for you to analyze?

Before You Begin...

Solution

What is your claim? Are there any sub-claims?

What are your assumptions?

Before You Begin...

Solution

What do you want the output to be?

What analysis will best test your claim?

Before You Begin...

Solution

What do you want the output to be?

What analysis will best test your claim?

What are the steps? What tools are best for each step?

How can you GENERALIZE for a broader analysis?

– Timeline

• K-means

K-means

• Recall goal: given data that might be clustered...find the clusters

K-means

• Recall goal: given data that might be clustered...find the clusters

• Guess cluster-centers at random • Assign points to nearest “center” to build

clusters • Average points in cluster to update “center”

• Guess cluster-centers at random

• Assign points to nearest “center” to build clusters

• Average points in cluster to update “center”

• Repeat!

Code • A “point” is a (float, float) tuple. def kmeans(points, k): ''' point list * int -> [point list] take a list of n points in the plane, and a number k > 0 of groups to put them in. Return a list of points containing the k "center” points (the "means" of the clusters) '''

def kmeans(points, k): distant = findDistantPoint(points) means = initialMeans(points, k) clusters = findClusters(points, means) oldClusters = [] while (oldClusters != clusters) : oldClusters = clusters means = findMeans(clusters, distant) clusters = findClusters(points, means) return means

def findDistantPoint(points): ''' point list -> point compute a point very far from all the selected points, to use as a dummy "cluster center" when some cluster "dies". '''

def findDistantPoint(points): x0 = 0 y0 = 0 for p in points : x0 = max(x0, abs(p[0])) y0 = max(y0, abs(p[1])) return (3 * x0, 3 * y0) CSCI 0931 - Intro. to Comp. for the Humanities and Social Sciences 45

def initialMeans(points, k) : ''' point list * int -> point list Select k points at random from the input point list, and return a list containing those k selected points.'''

def findClusters(points, means) : ''' point list -> point list list Given n points and k "mean" points, form k lists; the ith list consists of all input points that are closer to the ith mean than to any other. Return a list of these k lists of "closest points".'''

def findClusters(points, means) : selectionList = [indexOfClosest(point, means) for point in points] k = len(means) return [selectList(points, selectionList, i) for i in range(0,k)]

def indexOfClosest(point, meanPoints) : ''' point * point list -> int Take a point P and a list of points Q0, Q2, ..., Qk-1 (the "meanPoint" list) and compute the distance to each of the Q points. If the distance to Qi is the smallest of these, return i. Thus if the distances are [2, 2, 1, 6, 3], we'd return 2, because the "1" is at position 2 (counting from zero!]''' CSCI 0931 - Intro. to Comp. for the Humanities and Social Sciences 49

def distance(p, q) : ''' point * point -> float return the distance between two points in the plane. ''' xdiff = p[0] - q[0] ydiff = p[1] - q[1] return sqrt( xdiff*xdiff + ydiff*ydiff)

def selectList(points, selectionList, index) : ''' (float, float) list * int list * int -> (float, float) list Given a list of points, and a selection list (a list of indices from 0 to k-1, for some k, such as (for k = 2) [0 1 1 0 0], and an index that's between 0 and k-1 (inclusive), return the points corresponding to the selected index. For instance, if the selection list is [0 1 1 0 0] and index = 0, you'd return a list of three points: the ones at index 0, 3, and 4 in the "points" list''' CSCI 0931 - Intro. to Comp. for the Humanities and Social Sciences 51

def findMeans(clusters, distant) : ''' (point list) list * point -> (point list) Given k clusters, each represented as a point list, find the mean (average) point for each cluster; if the cluster is empty, use "distant" as its mean''' return [findMean(cluster, distant) for cluster in clusters]

def findMean(cluster, distant): ''' point list * point-> point Given a nonempty list of n points, return the average of the points gotten by summing up the x-coordinates of all the points and dividing by n, to get a number x0, and doing the same with the y-coordinates to get y0, and returning the point (x0, y0). For an empty list of points, return "distant" instead.'''

Shared Skeleton Code

• https://drive.google.com/a/brown.edu/file/d/0B4mKyAS1wRwhSnZvcTdFMnlHQXM/view?usp=sharing

• Available from course page, too

• Person 1: create at least two tests def TestfindMeans(): clusters1 = [(1,2), (1, 4)] cluster2 = [(4, 5)] clusters = [cluster1, cluster2] u =findMeans(clusters, (10,10)) if (u != [(1,3), (4, 5)]) : print “error”

• Person 2: coder/typist • Person 3: editor

final project! (& wind sprints) - brown...

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