Article published: November 18, 2020

Summary of Article

The article discusses the data science industry and addresses why there are not more women in data science. The author talks about how females and males are conditioned differently growing up and how this impacts their perception of jobs they are fit for. For example, men are raised to be risk-takers and encouraged to fail and make mistakes. On the other hand, females are often conditioned to be perfect,follow instructions, and get good grades. However, data science is a field that requires trial and error and failures in order to learn. Thus women are oftentimes discouraged by these failures. The author also talks about how women do not apply to roles they don’t feel they are 100% qualified for. And even when they get into these roles there are not enough female mentors to keep these women in these roles.

Female Data Science Statistics

Statistic Proportion
Proportion of UVA Data Science Master Students who are Female 31%
Proportion of all Graduates who are Female 55%
Females who desire to pursue a career in STEM 74%
Women who Hold STEM Related Degrees 19%

Key Quotes

  1. “There is a difference between the way girls and boys are conditioned. When they are young, boys are often risk-takers. They are the adventure seekers, who are allowed to fall and get their hands dirty. There is always room for making mistakes, and getting back up.”
  2. “Women only apply for jobs when they feel like they meet 100% of the job requirement. On the other hand, men apply when they think they meet just 60% of the requirement.”
  3. “When entering college and the workforce, the only way to learn is by doing. By making mistakes, falling over, and then getting up again.”

Author Information

Biography

Natassha Selvaraj is a data scientist, creator, and writer. She is a self-taught data scientist who learned her data science skills from online platforms like Coursera and EdX. She now writes articles advising people on how they can teach themselves data science.

Article Highlights

Why are there not enough women in data science?

  1. Not enough female role models in the industy.
  2. Women conditioned to take less-risks, get discouraged by the trial and error proccess of data science.
  3. Women feel underqualified, which discourages applications to data science positions.

USArrests Data & Figures

Map

Scatterplot

Data

Murder Assault UrbanPop Rape
Alabama 13.2 236 58 21.2
Alaska 10.0 263 48 44.5
Arizona 8.1 294 80 31.0
Arkansas 8.8 190 50 19.5
California 9.0 276 91 40.6
Colorado 7.9 204 78 38.7
Connecticut 3.3 110 77 11.1
Delaware 5.9 238 72 15.8
Florida 15.4 335 80 31.9
Georgia 17.4 211 60 25.8
Hawaii 5.3 46 83 20.2
Idaho 2.6 120 54 14.2
Illinois 10.4 249 83 24.0
Indiana 7.2 113 65 21.0
Iowa 2.2 56 57 11.3
Kansas 6.0 115 66 18.0
Kentucky 9.7 109 52 16.3
Louisiana 15.4 249 66 22.2
Maine 2.1 83 51 7.8
Maryland 11.3 300 67 27.8
Massachusetts 4.4 149 85 16.3
Michigan 12.1 255 74 35.1
Minnesota 2.7 72 66 14.9
Mississippi 16.1 259 44 17.1
Missouri 9.0 178 70 28.2
Montana 6.0 109 53 16.4
Nebraska 4.3 102 62 16.5
Nevada 12.2 252 81 46.0
New Hampshire 2.1 57 56 9.5
New Jersey 7.4 159 89 18.8
New Mexico 11.4 285 70 32.1
New York 11.1 254 86 26.1
North Carolina 13.0 337 45 16.1
North Dakota 0.8 45 44 7.3
Ohio 7.3 120 75 21.4
Oklahoma 6.6 151 68 20.0
Oregon 4.9 159 67 29.3
Pennsylvania 6.3 106 72 14.9
Rhode Island 3.4 174 87 8.3
South Carolina 14.4 279 48 22.5
South Dakota 3.8 86 45 12.8
Tennessee 13.2 188 59 26.9
Texas 12.7 201 80 25.5
Utah 3.2 120 80 22.9
Vermont 2.2 48 32 11.2
Virginia 8.5 156 63 20.7
Washington 4.0 145 73 26.2
West Virginia 5.7 81 39 9.3
Wisconsin 2.6 53 66 10.8
Wyoming 6.8 161 60 15.6