5. exploratory data analysis · exploratory data analysis nam wook kim mini-courses — january @...
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Exploratory Data AnalysisNam Wook Kim
Mini-Courses — January @ GSAS 2018
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GoalLearn the Philosophy of Exploratory Data Analysis
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Exposure, the effective laying open of the data to display the unanticipated, is to us a major portion of data analysis…
It is not clear how the informality and flexibility appropriate to the exploratory character of exposure can be fitted into any of the structures of formal statistics so far proposed.
[The Future of Data Analysis, Tukey 1962 ]
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Nothing - not the careful logic of mathematics, not statistical models and theories, not the awesome arithmetic power of modern computers - nothing can substitute here for the flexibility of the informed human mind.
Accordingly, both approaches and techniques need to be structured so as to facilitate human involvement and intervention. [The Future of Data Analysis, Tukey 1962 ]
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Summary Statistics uX = 9.0 σX = 3.317 uY = 7.5 σY = 2.03
A B C DX Y X Y X Y X Y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.588.0 6.95 8.0 8.14 8.0 6.77 8.0 5.7613.0 7.58 13.0 8.74 13.0 12.74 8.0 7.719.0 8.81 9.0 8.77 9.0 7.11 8.0 8.8411.0 8.33 11.0 9.26 11.0 7.81 8.0 8.4714.0 9.96 14.0 8.10 14.0 8.84 8.0 7.046.0 7.24 6.0 6.13 6.0 6.08 8.0 5.254.0 4.26 4.0 3.10 4.0 5.39 19.0 12.5012.0 10.84 12.0 9.13 12.0 8.15 8.0 5.567.0 4.82 7.0 7.26 7.0 6.42 8.0 7.915.0 5.68 5.0 4.74 5.0 5.73 8.0 6.8
Linear Regression Y = 3 + 0.5 X
R2 = 0.67
Anscombe’s Quartet
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Topics• What is exploratory analysis • Stages of data analysis • Exploratory analysis with Tableau
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What is Exploratory Data Analysis?An philosophy for data analysis that employs a variety of techniques (mostly graphical):
1.maximize insight into a data set 2.uncover underlying structure 3.extract important variables 4.detect outliers and anomalies 5.test underlying assumptions
http://www.itl.nist.gov/div898/handbook/eda/eda.htm
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It’s Iterative ProcessAsk questions Construct graphics to address questions Inspect “answer” and assess new questions Repeat...
“Show data variation, not design variation” —Tufte
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Visualization is just one, although critical, to enable better interaction with data
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Visualization
Modeling
Integration
Cleaning
Acquisition
Presentation
Dissemination[J. Heer]
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Visualization
Modeling
Integration
Cleaning
Acquisition
Presentation
Dissemination[J. Heer]
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Visualization
Modeling
Integration
Cleaning
Acquisition
Presentation
Dissemination[J. Heer]
Data Wrangling
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I spend more than half of my time integrating, cleansing and transforming data without doing any actual analysis. Most of the time I’m lucky if I get to do any “analysis” at all.
—Anonymous Data Scientist [Kandel et al. ’12]
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Data Quality Hurdles Missing Data Erroneous Values Type Conversion Entity Resolution Data Integration
no measurements, redacted, ...? misspelling, outliers, ...?e.g., zip code to lat-londiff. values for the same thing? effort/errors when combining data
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https://www.trifacta.com/
A visual tool to quickly clean and prepare messy, diverse data
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Exploratory Analysis with Tableau
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IACS ComputeFest Workshop: Introduction to Tableau
Wednesday, January 11, 2017 12:00 PM - 2:30 PM
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What is Tableau?Software to rapidly construct visualizations of data and perform exploratory analysis of data
Download: https://public.tableau.com Dataset: http://www.namwkim.org/datavis/h1b_kaggle_sample.csv
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Dimension: Discrete categories
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Measure: Continuous quantities
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Marks: Visual encoding
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Rows & Columns: Create a table of visualizations below
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Where visualizations appear
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Analysis Example: H-1B Visa Petitions 2011-2016
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Dataset: H1B Visa Petitions (2011-16)
H1B is a Employment-based, non-immigrant visa category for temporary foreign workers
The raw data was published by The Office of Foreign Labor Certification (OFLC)
The data was cleaned by Sharan Naribole, featured on Kaggle: https://www.kaggle.com/nsharan/h-1b-visa
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CASE_STATUS (N): “Certified” (means eligible not approved) “Denied”….
EMPLOYER_NAME (N) — Company submitting this petition
SOC_NAME (N) — Standard occupational name
JOB_TITLE (N) — Title of the job
FULL_TIME_POSITION (N) — Y = Full Time Position; N = Part Time Position
PREVAILING_WAGE (Q) — the average wage paid to similar workers in the company
YEAR (O): Year in which the H-1B visa petition was filed WORKSITE (N): City and State information of the foreign worker's intended area of employment
lon (Q): longitude of the Worksite
lat (Q): latitude of the Worksite
Dataset: H1B Visa Petitions (2011-16)
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CASE_STATUS (N): “Certified” (means eligible not approved) “Denied”….
EMPLOYER_NAME (N) — Company submitting this petition
SOC_NAME (N) — Standard Occupational Name
JOB_TITLE (N) — Title of the job
FULL_TIME_POSITION (N) — Y = Full Time Position; N = Part Time Position
PREVAILING_WAGE (Q) — the average wage paid to similar workers in the company
YEAR (O): Year in which the H-1B visa petition was filed WORKSITE (N): City and State information of the foreign worker's intended area of employment
lon (Q): longitude of the Worksite
lat (Q): latitude of the Worksite
Dataset: H1B Visa Petitions (2011-16)
3 million records of H-1B Visa Petitions 492MB!!
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CASE_STATUS (N): “Certified” (means eligible not approved) “Denied”….
EMPLOYER_NAME (N) — Company submitting this petition SOC_NAME (N) — Standard occupational name
JOB_TITLE (N) — Title of the job
FULL_TIME_POSITION (N) — Y = Full Time Position; N = Part Time Position
PREVAILING_WAGE (Q) — the average wage paid to similar workers in the company YEAR (O): Year in which the H-1B visa petition was filed
WORKSITE (N): City and State information of the foreign worker's intended area of employment City (N)
State (N)
lon (Q): longitude of the Worksite Tableau can infer this from worksite
lat (Q): latitude of the Worksite
Dataset: H1B Visa Petitions (2011-16)
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CASE_STATUS (N): “Certified” (means eligible not approved) “Denied”….
EMPLOYER_NAME (N) — Company submitting this petition SOC_NAME (N) — Standard occupational name
JOB_TITLE (N) — Title of the job
FULL_TIME_POSITION (N) — Y = Full Time Position; N = Part Time Position
PREVAILING_WAGE (Q) — the average wage paid to similar workers in the company YEAR (O): Year in which the H-1B visa petition was filed
WORKSITE (N): City and State information of the foreign worker's intended area of employment City (N)
State (N)
lon (Q): longitude of the Worksite Tableau can infer this from worksite
lat (Q): latitude of the Worksite
Dataset: H1B Visa Petitions (2011-16)
And removed rows of missing data and randomly sampled 40% of the whole data
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EMPLOYER_NAME (N) — Company submitting this petition
SOC_NAME (N) — Standard occupational name
JOB_TITLE (N) — Title of the job
PREVAILING_WAGE (Q) — the average wage paid to similar workers
YEAR (O): Year in which the H-1B visa petition was filed
City (N): City of the worksite
State (N): State of the worksite
Dataset: H1B Visa Petitions (2011-16)
~20MB
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HypothesesWhat might we learn from this data? Do petitions increase over time? Which company files petitions the most? What kind of job is the most applied? Which company offers the highest salary What kind of job is offered the highest salary? Which states/cities file petitions the most? What are differences in salaries across states & cities?
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Tableau Demo
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Load dataChange Year to String Type
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Do petitions increase over time?
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Do petitions increase over time?
Filtered by top 10 employers
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Which company files petitions the most?
Filtered by top 50 employers
Average line
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What kind of job is the most applied?
Filtered by top 50 jobs
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Petitions per job per company
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Which company offers the highest salary?
Filtered by top 50 employers
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What kind of job is offered the highest salary?
Filtered by top 50 jobs
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Which states/cities files petitions the most?
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What are differences in salaries across states & cities?
Big outlier in California removed
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Scatter Plot
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Storytelling with Data
NextTableau Story Points
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10 min break