ADV modeling

binam90
HwRequirenments.docx

Step 1: Read the data set description (from website and dataset itself), carry out data abstraction on the provided data set. (10 points total)

· in a Word document, write down the dataset type. (1 point)

· Write down the number of fields/attributes. (1 point)

· Analyze each field in terms of attribute abstractions: write down a concise description in domain-dependent language of the field’s meaning; decide the attribute type and write that down. (8 points)

 

Step 2: Analyze the cardinality (10 points total)

Write down the number of total items (1 point), and

· For each attribute, indicate its cardinality. (4 points)

· For categorical attributes, write down the number of unique levels. (2 points)

· For quantitative attributes, specify the range from min to max and note any other characterization that seems potentially useful (cyclic? Anything else?)  (2 points)

· For ordered attributes, consider whether it would be more useful to treat them categorical or quantitative, or to preserve them as ordered. (1 point)

 

Step 3: Write three questions you would like to answer with this data set, from the point of view of an aid worker reporting to the government of a country providing aid. (30 points total, 10 points for each question)

For each question, write the following information:

· Do you need a chart in order to answer this question? (1 point)

· If none of your questions require a chart, try to create a few new ones that might benefit from one.

· Which fields/attributes do you need to use to answer the question? (2 points)

· Do you need to transform the data in order to answer the question? If yes, what transformations are needed? (2 points)

· Do data set type and attribute type change when you need to transform the data? If yes, how do they change? (2 points)

· Do you have all the data you need to answer this question, or would you need additional data fields that are not provided here? (3 points)

Step 4: REFLECT/DISCUSS: What did you learn in this exercise? (5 points) How might this analysis be useful in visualization design? (5 points)

Step 1

: Read the data set

description (from website and

dataset itself), carry out data

abstraction on the provided data

set. (10 points total)

·

in a Word document,

write down the dataset

type. (1 point)

·

Write down the

number of

fields/attributes. (1

point)

·

Analyze each field in

terms of attribute

abstractions: write

down a concise

description in domain

-

dependent language of

the field’s meaning;

decide the attribute

type and write that

down. (8 points)

Step 2

: Analyze the cardi

nality (10

points total)

Write down the number of total

items (1 point), and

·

For each attribute,

indicate its cardinality.

(4 points)

·

For categorical

attributes, write down

the number

of

unique

levels. (2

points)

Step 1: Read the data set

description (from website and

dataset itself), carry out data

abstraction on the provided data

set. (10 points total)

 in a Word document,

write down the dataset

type. (1 point)

 Write down the

number of

fields/attributes. (1

point)

 Analyze each field in

terms of attribute

abstractions: write

down a concise

description in domain-

dependent language of

the field’s meaning;

decide the attribute

type and write that

down. (8 points)

Step 2: Analyze the cardinality (10

points total)

Write down the number of total

items (1 point), and

 For each attribute,

indicate its cardinality.

(4 points)

 For categorical

attributes, write down

the number

of unique levels. (2

points)