ThinkData Analytics Boot Camp: Cohort 10 Research Project Wage Comparison Study Watch the presentation: https://www.youtube.com/watch?v=UQKT8Ff7BqU Conducted in Spring 2020 in Birmingham, Alabama

1. In the event of
technical issues

2. It all starts Day 1
Tech and Soft Skills Development

3. Class Research Project
Cohort 10 - ThinkData Analytics Bootcamp

4. Hours of work
cleaning & analyzing
to produce findings

5. Microsoft PowerBI Desktop
Tableau Public

6. NOUN difference in size, degree,
circumstances, etc.; lack of equality. RACE|GENDER|AGE

7. In my experience the differences in
wages are directly relatedto
knowledge in the field and
commitment to progress.
I find it very hard to believe
that there's a significant
difference in males vs females
compensation.
I don't think gender is the
actual issue in 2020
Opportunityis where you look.
People need to look harder for it.
Pay is not based on any of these
Knowing salaries of people who
have worked for me, I can say
that neither race nor gender
play a role at all.
Actual responsesfrom respondentswho disagree with inequality and shared their feedback.

8. I see more inequalityin opportunitiesbased
on gender more than any other bias.
In my previousemployer, all of these were an issue.
This is one of the great
issues within the US
Males make more than females
in my profession at my work.
I no al a abo pa Discrimination
also happened with mobility.
Actual responsesfrom respondentswho agree with inequality and shared their feedback.

9. Let's meet Data
Cohort 10

10. Demographics
Overview of who are the respondentsto the survey

11. Education
Education levels and various information on the respondents

12. Inequality Research
Wage and Promotion Inequality

13. Inequality Research
Seniority Levels, Promotions, and Inequality Questions

14. Inequality Research
Inequality Questions:Demographics and Education

15. Review & Support All Teams
Steve Rucinski, Sheldon Winks & Erik Giddens

16. Wage Comparison Study
Cohort 10 - ThinkData Analytics Bootcamp

17. Demographics
Locations, Age, Gender, Industry, Education, Salary and Benefits

18. Claudia Powell
Data Cleaner for Demographics
Mapping Visualizationsin Power BI and Tableau

19. 315 Total Valid Responses from 20 States and DC

20. Valid responses include:
VALID WORKPLACELOCATION
POSTAL CODES
3 ANSWERED INEQUALITY
QUESTIONS

21. 227
Alabama
Responses

22. Brian Boyd
Data Visualizations in Power BI

26. Aaron Barnes
Data Visualizations in Power BI

27. What is your current industry and what type of business do you work for?

28. How do you classify your current employment?

30. Parker Morrow
Analyzed respondents' compensationdata
utilizing Power BI

31. Source for Alabama Median Income:https://www.census.gov/quickfacts/fact/table/AL/PST045219#
Source for UnitedStates Median Income:https://www.census.gov/library/stories/2019/09/us-median-household-income-not-significantly-different-from-2017.html

32. 145
39
106

36. Education
Age, Race or Ethnicity, Gender, Salary, Promotions, and
Years in Current Role as it relates to Education

37. Fatimah McCarther
Data Visualizations in Power BI

38. Degree Persona
Degree and Non Degree

40. Ian Griffin
Analyzed respondents education level with
data visualizations from Power BI

41. Education by Age

42. Age and Degrees

43. Jerrett Gunn
Comparing salaries by Education using the data
we received from our survey utilizing Power BI
visualizations.

47. Joe Hill
Data Visualizations in Power Bi

48. Salary Pattern of Three Highest Respondent Subgroups
Greatest number of response
are in this salary range
40k – 59,999 annually

49. Patternsof Salary by Race
WHITE SUBGROUP
BLACK SUBGROUP
HISPANIC SUBGROUP

50. SubgroupSalary Calculationfor Bachelor'sandMaster's DegreeHolders.
WHITE
BLACK
HISPANIC

51. Jeff Rose
Data Cleaner
Data Visualizations in Power Bi

55. Never been promoted
• Filtered by never been promoted by racial subgroup
• Then crossed with time in current role

56. Inequality Research
Wage and Promotion Inequality

57. Joseph Chatman
Inequality Study
Promotions by Demographics

61. Bianka Jimenez
Data Cleaner for all Inequality teams
Data visualizations in Power Bi

63. 64% of female respondents earn in mid-range salaries.
MALE
FEMALE
Salary Range Percentage

64. Industry vs Wage Fairness

65. Violeta Sanchez Yañez
Promotion Fairness

66. Education Level and Years of Industry Experience: Effect on Promotion

67. Non-DegreeDegree
45% of both degree
and non-degree
holding respondents
with 10+ years of
industry experience
have been promoted
at least once

68. "Do you Feel that Promotion Decisions are Fair at yourCompany?"
Degree, Never Promoted NonDegree, Never Promoted

69. Degree, Promoted at least Once NonDegree, Promoted at least Once

70. Inequality Research
Seniority Levels, Promotions, and Inequality Questions

71. Michele Patrick
Power Query Transformation
Data visualization in Power BI

72. Seniority Level Comparisons
Mid-Level Positions
34% Females
13% Males

76. Wade Kwon
Data visualization in Power BI

77. Wh bee m ed? Full-timers.

78. There is inequality in wage based on ____.
AGE
GENDER
RACE
AGREE
DISAGREE
AGREE
DISAGREE
AGREE
DISAGREE

79. The e i ine ali in age ba ed on Age
Gende
Race
*CUMULATIVE
AGE
GENDER
RACE

80. There is inequality in wage based on race.
PEOPLE
OF COLOR
WHITE
BLACK
Native American or Alaska Native

81. AGREE
AGE
GENDER
RACE
DISAGREE
I feel that my compensation is fair,
relative to similar roles at my company.
Inequality in
wage based on:
AGREE
DISAGREE
AGREE
DISAGREE
AGREE
DISAGREE

82. AGREE
AGE
GENDER
RACE
DISAGREE
I feel that my job performance is
evaluated fairly.
Inequality in
wage based on:
AGREE
DISAGREE
AGREE
DISAGREE
AGREE
DISAGREE

83. Inequality Research
Inequality Questions:Demographics and Education

84. Sam Sorrell
Power Query in Excel
Data Visualization in Tableau

90. Scott Lontine
Power Query in Excel
Data Visualization in Tableau

91. Is there Wage Inequality basedon Age, Gender, or Race?
DEGREE RESPONDENTS
DISAGREE that there is wage inequalitybased on AGE, GENDER, or RACE.
NON DEGREE RESPONDENTS
DISAGREE that there is wage inequalitybased on AGE and RACE
EVEN in their agreement/disagreement responses for GENDER.

92. DEGREE PERSONA RESPONSE TO THREE WAGE INEQUALITY QUESTIONS

93. The Non Degree Persona responses to all three wage inequalityquestions.
NON DEGREE PERSONA RESPONSE TO THREE WAGE INEQUALITY QUESTIONS.

94. DEGREE AND NON DEGREE PERSONAS: BLACK OR AFRICAN AMERICAN AND WHITE

95. DEGREE AND NON DEGREE PERSONAS: BLACK OR AFRICAN AMERICAN AND WHITE

96. FEMALE response to inequality
based on GENDER:
• DEGREE FEMALES were ALMOST EQUAL
in AGREEMENT/DISAGREEMENT
• NON DEGREE FEMALES have a 16% difference
in AGREEMENT/DISAGREEMENT
• Could the attainmentof a DEGREE eliminate
the GENDER biasin wage levels?

97. MALE response to inequality
based on GENDER:
• DEGREE and NON DEGREE MALES responded
OVERWHELMINGLY in DISAGREEMENT
• Both responded STRONGLY DISAGREE as
majority sentiment level
• DEGREE MALES responded 2:1 in
DISAGREEMENT/AGREEMENT
• NON DEGREE MALES have a 17% difference in
DISAGREEMENT/AGREEMENT

98. Thank You
DATA ANALYTICS BOOTCAMP
COHORT 10