The United States Presidential Election occurred on Tuesday, November 2. It concluded with a historic victory by former Vice President Joseph Biden and Senator Kamala Harris. The election of Harris to Vice President-Elect is groundbreaking as the first woman, the first woman of color, the first black woman, and the first Asian elected as Vice President.
As the United States celebrates this historic achievement of Vice President-Elect Harris, many supporters are asking, why did it take so long in the first place? American Politicians have historically not been representative of the demographics of the country at large. This trend is reflected in the workplace. In data science, the need for diversity and inclusion initiatives is clear. Women are underrepresented. Only 18% of data science roles were held by women in 2019, with women comprising 31% of data related positions .
Small actions can help to address this gender gap, such as diversifying thought leadership and content. On social media, follow women in data science and other data science professionals in underrepresented communities. Help to eliminate information silos.
Link to Women in Data Science: https://twitter.com/i/lists/1323647732020555776/members
Link to Women in Data Science: https://twitter.com/i/lists/1323647732020555776/members
1. Jenny Bryan
@JennyBryan
Software engineer @rstudio
Bio: Do you work in R? @JennyBryan shares resources that are helpful to beginners and experienced Data Scientist. She provides excellent resources for anyone working with R, sharing current issues faced by colleagues. She shares articles and blog posts on relevant issues in Data Scientist when working with R, and R packages such as tidy-r. Read her articles and check out her courses.
2. Meg Risdal
@MeganRisdal,
Product at Kaggle
Meg Risdal works at Kaggle on Product. All data scientists understand the value of hearing from team members at Kaggle. Risdal provides announcements, new features, highlights competition winners, and upcoming competitions. For beginning data scientists, this is a feed to watch as you build your portfolio because Kaggle projects are a great way to learn and share data science skills.
3. Emily Robinson
@robinson_es
Senior data scientist @WarbyParker. Robinson is a great resource for building a data science portfolio and professional development in data science. She authored a book, Build a Career in Data Science, which is available at http://datascicareer.com
4. Mine Dogucu
@MineDogucu
Stats & data science teaching prof. @UCIrvine
For those looking to learn R or upskill, @MineDogucu contributes and links to a multilingual learning resource. Dogucu also provides pedagogical resources on her blog. If Bayesian modeling is in your toolbox or you want to learn more about Bayes in R, learn from Mine Doguru by following her on Twitter.
5. Rachel Tatman
@rctatman
Developer Advocate
Rachel Tatman specializes is Natural Language Processing, with a Ph.D. in Linguistics. She live codes twice a week, shares papers, and other resources.
6. Monica Rogati
@mrogati
Data Science & AI advisor; fractional CDO. Former VP of Data @Jawbone & @LinkedIn data scientist. Equity Partner @DCVC CMU CS PhD.
Rogati, Former VP of LinkedIn, offers great advice, including “being the algorithm”. Her experience with ML, AI, data science, and models. If you have more experience or work in ML, read Rogati’s feed.
7. Data Science Renee
@BecomingDataSci
Director of Data Science at @HelioCampus
Follow the host of “Becoming a Data Scientist” Podcast for tutorials and blog posts on predictive modeling and image classifiers. Her content will be most helpful for data scientists with some experience. However, she makes a purposeful effort to address the community of aspiring data scientists. #goals
8. Ganna Pogrebna
@decisionalysis
Prof of #Behavior & #DataScience, Lead 4 #BehavioralDataScience @turinginst, #AI expert, blogger, consultant
Porgrehna leads Responsible AI Network at the University of Birmingham is the Turing University Lead for the University of Birmingham, and the Lead for Behavioural Data Science at the Alan Turing Institute.
Porgrebna specializes in decision making under risk and uncertainty. On Twitter, she shares and creates content on predictive analytics and cybersecurity in addition to data science.
9. Chelsea Parlett-Pelleriti
@ChelseaParlett
Phd Student.
Chelsea posts original content in diagrams and videos to explain data science and stats concepts. These help beginers who are hearing the data science and stats concepts for the first time and data scientists looking to explain the concepts in jargon-free, digestible descriptions.
10. Ayushi Rawat
@ayushi7rawat
Software Developer and Technical Blogger.
Rawat produces videos on machine learning (ML) and Python. She just put out a list of Ultimate Python Resources. Her content provides resources for learning as a developer, professional development, and resources for data scientists at all stages in their careers.
11. Sara A. Metwalli
@SaraMetwalli
Researcher at Keio University Quantum Computing Center. Lead of @WWCodePython
Matwalli shares and curates helpful content for beginners. She works with Python, and shares tips on improving workflow. Additionally, Metwalli provides resources for skills that many data scientists will find helpful, such as data visualization.
12. Alison Presmanes Hill
@apreshill
Data Scientist & Professional Educator @RStudio @apreshill
Hill knows R and shares that knowledge on Twitter, with several resources on #rstats (stats), #rmarkdown, and tidyR. If you are in Business Intelligence or want to know more about data visualization, her threads on Shiny should make your list.
Alternative Intro
12 Data Scientist You Should Follow on Twitter
Heather Wigell
Keywords: Data Science, Twitter, R, Women in Tech
Build A Twitter List to Connect with Data Science Colleagues
Twitter is a great way to connect with colleagues in your field, find resources, and learn how other people are working. Twitter has a list feature, which displays users in a separate stream organized by time. The list creates a secondary feed. Lists can be created or followed. For more information on how to set-up a list, visit the Twitter Help Center below:. https://help.twitter.com/en/using-twitter/twitter-lists
The list feature is helpful to connect with other data scientists and extend your professional network. As you build your professional network, consider the importance of diversifying thought leadership and eliminating information silos.
Who Do You Follow on Twitter?
To build a quality list, prioritize users and content. Some factors to consider when prioritizing who and what content to follow including:
· To whom do you want to connect? What do they share or post?
· Who is working in a similar domain?
· Do you use Python, R, or both?
· Where are you in your career? A lot of content is created for Jr. Data Scientist or those interested in changing career paths. If you have a year or two of direct experience the content you will find helpful is very different.
· Are you a visual learner? Use the content and media formats that work for you.
· Do you work in product, consulting, non-profit, R&D, or academics? Follow users in similar industries or learn from those working on projects in other sectors.
· Do you want to see examples from users’ everyday experience? Do you prefer higher-level content?
· How do you define an expert or authority? Be honest about how you weigh their job title or employer, years in the fields, publication, portfolio, ability to explain a topic. Choose users that meet your definition of expertise.
