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Participation over the years
Education Distribution of the Participants
Age distribution of participants
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Participation across countries
Designation Distribution of Participants
Women In STEM
Demographic, Education, Designation, and Salary.
Insights
- Close to 50% of the people who took this survey have a master’s degree.
- 47% of the women respondents have a master’s degree as opposed to 43% for men.
- Data Science and Software Engineer are the two most popular designations. Number of Students who took the respondents is nearly equal to the number of data scientists.
- 24% of the students are women as compared to 20% for men.
- There are more women who are Students, Statisticians, Product/Program Manager, Data Analyst, and Research Scientist as compared to men.
- More men are Data Scientists, SWEs, DBA/DB Engineers, and Data Engineers as compared to women.
- 6.87% women are unemployed as compared to 4.46% for men.
Machine Learning at Work.
Insights
- Majority of the respondents are exploring ML models and may put a model into production one day at Work. A close second is the number of people who’ve put a model in production in the last 2 years.
- More women (20.22%) do no use ML at work as opposed to men (18%).
- More men(19.69%) work in teams that have well established ML methods at work as opposed to women (16.05%).
- A large majority of the respondents work in Data Science teams of size 1-2 or 20+ ie. either a small exploratory team or a full fledged team.
- More women (24.48%) work in team of size 20+ as opposed to men (23.08%).
- More men (22.47%) work in team of size 1-2 as opposed women (19.44%).
Algorithms used in ML (NLP and Computer Vision).
Insights
- Word embeddings are the most popular NLP technique used followed by encoder decoder models.
- Automated Model Selection is the most popular tool used followed by Data Augmentation Techniques.
- Image classification is the most common computer vision method used.
Coding Experience and Recommendations.
Insights
- Close to 50% of the respondents have spent 0-2 years writing code to analyze data.
- More women (28.88%) have less than 1 year of experience than men (23.79%) in writing code to analyze data. More men have >1 year of experience in writing code as compared to women.
- Python is the most popular programming language used followed by SQL and R.
- Python is by far the most recommended language for beginners with over 50% of the respondents recommeding it. R is a close second.
- More men (79.8%) recommend Python as compared to women (73.38%). While more women (11.4%) recommend R as compared to men (8.94%).
R vs Python
Which are the most popular programming languages?
Insights
- Python is most popular programming language followed by SQL and R.
- A lot more people use only Python as compared using R.
Where is R/Python being used?
Insights
- United States and India are the countries where R and Python is used the most.
- United States has more R users while India has more Python users.
Who is using R and Python?
Insights
- People in the age group 25-29 use R/Python the most.
- Data Scientist use R and Python the most as compared to other designations. Software Engineers use Python way more than R users.
- Python users consistently all more salary across all salary ranges.
- Higher number of Statisticians use R as compared to Python.
- More people with 1-2 years of coding experience use Python while more people with 3-5 years of coding experience use R.
- People with Master’s degree use Python and R more as compared all other education degrees.
Algorithms used in ML (NLP, AutoML, and Computer Vision).