Data
Diversity_in_Tech_Companies

Diversity_in_Tech_Companies

active ARFF Public Domain (CC0) Visibility: public Uploaded 31-05-2024 by Iwo Godzwon
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Description: The "Diversity in Tech Companies" dataset offers an insightful exploration into the ethnic and gender composition of the workforce within major technology firms over recent years. This dataset comprises data spanning from 2014 to 2018, presenting a compelling overview of diversity trends within the tech industry. It serves as a valuable resource for researchers, policymakers, and industry professionals striving to understand and enhance diversity and inclusion within the technology sector. Attribute Description: - Year: The calendar year the data was collected, ranging from 2014 to 2018. - Company: Names of the tech companies included in the dataset (e.g., Cisco, Yahoo!, Nvidia, Microsoft, Netflix). - Female %: Percentage of the workforce identified as female. - Male %: Percentage of the workforce identified as male. - % White: Percentage of the workforce identified as White. - % Asian: Percentage of the workforce identified as Asian. - % Latino: Percentage of the workforce identified as Latino. - % Black: Percentage of the workforce identified as Black. - % Multi: Percentage of the workforce identified as belonging to two or more races. - % Other: Percentage of the workforce identified under categories not explicitly listed. - % Undeclared: Percentage of the workforce that has not declared their ethnic background. Use Case: This dataset is primed for analysis aimed at uncovering insights into how diversity within the tech industry has evolved over the specified years. It can support a broad range of applications, from academic research focusing on workplace diversity to strategizing corporate policies aimed at fostering a more inclusive environment. Additionally, it could aid in societal discourse on the importance of diversity in innovation-driven sectors, helping stakeholders to benchmark progress and identify areas requiring concerted efforts and improvements.

11 features

Yearnominal5 unique values
0 missing
Companystring23 unique values
0 missing
Female %numeric28 unique values
0 missing
Male %numeric27 unique values
0 missing
% Whitenumeric27 unique values
0 missing
% Asianstring34 unique values
0 missing
% Latinostring16 unique values
0 missing
% Blacknominal14 unique values
0 missing
% Multistring9 unique values
0 missing
% Otherstring8 unique values
1 missing
% Undeclarednominal6 unique values
0 missing

19 properties

94
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
1
Number of missing values in the dataset.
1
Number of instances with at least one value missing.
3
Number of numeric attributes.
3
Number of nominal attributes.
0
Percentage of binary attributes.
1.06
Percentage of instances having missing values.
0.1
Percentage of missing values.
Average class difference between consecutive instances.
27.27
Percentage of numeric attributes.
0.12
Number of attributes divided by the number of instances.
27.27
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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