Exploratory Data Analysis
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1 Exploratory Data Analysis Nam Wook Kim Mini-Courses GSAS 2018
2 Goal Learn the Philosophy of Exploratory Data Analysis
3 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 ]
4 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 ]
5 Anscombe s Quartet A B C D X Y X Y X Y X Y Summary Statistics ux = 9.0 σx = uy = 7.5 σy = Linear Regression Y = X R 2 = 0.67
6 A 15 B Y 8 Y X X C 15 D Y 8 Y X X
7 Topics What is exploratory analysis Stages of data analysis Exploratory analysis with Tableau
8 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
9 It s Iterative Process Ask questions Construct graphics to address questions Inspect answer and assess new questions Repeat... Show data variation, not design variation Tufte
10 Visualization is just one, although critical, to enable better interaction with data
11 Acquisition Cleaning Integration Visualization Modeling Presentation Dissemination [J. Heer]
12 Acquisition Cleaning Integration Visualization Modeling Presentation Dissemination [J. Heer]
13 Acquisition Cleaning Data Integration Wrangling Visualization Modeling Presentation Dissemination [J. Heer]
14 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]
15
16
17 Data Quality Hurdles Missing Data Erroneous Values Type Conversion Entity Resolution Data Integration no measurements, redacted,...? misspelling, outliers,...? e.g., zip code to lat-lon diff. values for the same thing? effort/errors when combining data
18 A visual tool to quickly clean and prepare messy, diverse data
19 Exploratory Analysis with Tableau
20 IACS ComputeFest Workshop: Introduction to Tableau Wednesday, January 11, :00 PM - 2:30 PM
21 What is Tableau? Software to rapidly construct visualizations of data and perform exploratory analysis of data Download: Dataset:
22 Dimension: Discrete categories
23 Measure: Continuous quantities
24 Marks: Visual encoding
25 Rows & Columns: Create a table of visualizations below
26 Where visualizations appear
27
28 Analysis Example: H-1B Visa Petitions
29 Dataset: H1B Visa Petitions ( ) 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:
30 Dataset: H1B Visa Petitions ( ) 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
31 Dataset: H1B Visa Petitions ( ) CASE_STATUS (N): Certified (means eligible not approved) Denied. EMPLOYER_NAME (N) Company submitting this petition SOC_NAME (N) Standard Occupational Name 3 million records of H-1B Visa Petitions JOB_TITLE (N) Title of the job FULL_TIME_POSITION (N) Y = Full Time Position; N = Part Time Position 492MB!! 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
32 Dataset: H1B Visa Petitions ( ) 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
33 Dataset: H1B Visa Petitions ( ) 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 And removed rows of missing data City (N) State and (N) randomly sampled 40% of the whole data lon (Q): longitude of the Worksite Tableau can infer this from worksite lat (Q): latitude of the Worksite
34 Dataset: H1B Visa Petitions ( ) 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 ~20MB
35 Hypotheses What 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?
36 Tableau Demo
37 Load data Change Year to String Type
38 Do petitions increase over time?
39 Do petitions increase over time? Filtered by top 10 employers
40 Which company files petitions the most? Filtered by top 50 employers Average line
41 What kind of job is the most applied? Filtered by top 50 jobs
42 Petitions per job per company
43 Which company offers the highest salary? Filtered by top 50 employers
44 What kind of job is offered the highest salary? Filtered by top 50 jobs
45 Which states/cities files petitions the most?
46 What are differences in salaries across states & cities? Big outlier in California removed
47 Scatter Plot
48 Next Tableau Story Points Storytelling with Data
49 10 min break
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