Women in Data Science: A Guide To Successfully Advancing Your Career

October 30, 2021

women in data science career

Advancing in your career is hard, and it’s even harder when you’re a woman working in an industry that men have dominated for decades upon decades.

If you’re starting, the notion of taking the first step may leave you dazed and perplexed, especially if you have no prior expertise. Luckily, there are some things you can do to help make the process easier and more successful.

This guide talks about how to advance your data science career as a woman while maintaining balance both at home and at work.

Women In Data Science  

According to a survey published by Boston Consulting Group in 2020, just 15% of data scientists are women. The study notes that lack of diversity is a significant problem: “AI algorithms are vulnerable to prejudice, therefore building them necessitates the inclusion of a diverse array of viewpoints and experiences on the team.”

Nobody has more confidence than the individual with data. Even if you have impostor syndrome, well-analyzed data is your confidence builder.

Despite some traditional tech perceptions and challenges that block or deter women and other underrepresented groups from entering data science and analytics fields, L. Sam Bishop, CEO of edx Head of Analytics, urges women to join the field.

“The most wonderful thing about data is that data is power,” says Bishop. “Nobody has more control over the data than the individual who holds it. “Whether you have impostor syndrome or not, well-analyzed facts are your confidence builder,” she explains.

women in the computer science field

Is Data Science For women?

Women may discover a lucrative career in Data Science at any age, whether they’re starting, considering a professional change, or thinking about re-entering the workforce. Despite this, far too few women take that step. Here’s why, and especially if you don’t have any technical knowledge, you should reconsider your options.

Women may discover a lucrative career in Data Science at any age, whether they’re starting, considering a professional change, or thinking about re-entering the workforce.

The major problem is that most women wonder if they are ready to do data science, especially at square one of their data science learning path. And they haven’t forgotten that every professional was once a beginner.

Why Is Data Science A Fantastic Career Choice For Women?

The widespread promises of high pay and job growth are the first and most obvious reasons why Data Science is a fantastic career choice for women. Their national average salary in the United States, according to Glassdoor, is $114K, compared to an overall average of $31K.

For job growth, if the expansion in open positions since 2012 hasn’t convinced you of the expanding possibilities in this profession, consider what LinkedIn has to say about it in their 2020 Emerging Jobs Report, where Data Science is ranked third with a percentage increase of 37 percent, after Artificial Intelligence Professional (74 percent) and Robotics Engineer.

Positive Job Growth For Women In Data Science

To begin, you may look at the proportion of women in tech. According to a Burtch-Works poll conducted in 2020, women in tech are gradually increasing but slowly. Only 15% of them were female in 2018, and the ratio has risen to 17% this year and will continue to rise to 18% next year.

However, there are still relevant gaps in the number of women in data science careers. These statistics suggest an increasing presence of women in data science and the start of female-led leadership.

There is, however, a significant need for development in the computing sector for women and other minority groups, particularly in the mid to higher-level leadership roles historically held by men.

What Education Do You Need To Become A Data Scientist As A Woman?

A university degree in computer science, IT, technology engineering, and mathematics will help you succeed in the profession. However, having comparable expertise in data and business analytics is also sufficient.

Fortunately, even though you lack a university degree and experience, you can still succeed in the data science profession. There are several options for learning, including taking qualifying courses or enrolling in a comprehensive online training covering everything from statistics and Excel to SQL, Python, and Tableau.

By doing so, you’ll have a better chance of receiving an internship at a well-known company and beginning your career in data science from the ground up. Perhaps starting as an entry-level data analyst isn’t how you pictured achieving your dream job. However, this may well be the most effective route to get to your dream job.

4 Essential Skills For Women In Data Science

Women in STEM, which is also referred to as (science, technology, engineering, and mathematics) have long been a minority. Only about 28% of jobs in these fields are held by women, while females occupy even fewer C-suite levels positions.

Women in data science should consider this field with many potentials, not one that has shut its doors on the female population. The following are the four vital abilities that every woman looking to study data science or pursue a career change into the field should master:


Whether it’s right or wrong, but women have been kept out of scientific fields for a very long time. Though the number of STEM role models for women is smaller, it certainly exists, and many schools are making deliberate efforts to encourage girls’ interest in STEM.

However, you need to join STEM programs at your school. If you’re searching for a new profession, learning about some of the many women who transformed science might help you overcome any negative preconceptions about your chosen field’s future.

Programming Skills

Data science requires a lot of time at a computer. Through on-the-job training is possible, having a baseline knowledge of a few current programming languages such as JavaScript and some database language understanding will enhance your chances of finding a job in a new field.

Before transitioning into data science, understanding the techniques and algorithms involved in data collection, such as random forests and ensemble methods, is also something to brush upon.


If you’re switching from a similar sector like cyber security or database administration, your math abilities should be sufficient. If an expert is required to develop new algorithms, linear algebra and some calculus forms would be icing on the cake.

However, having a firm grasp of business analytics is also essential. If you’ve looked at Google Analytics, you’ll see whether the numbers made sense to you or not. If they didn’t, it’s strongly suggested that you gain some baseline information there before going any further.


A strong background in a related field such as computers might help you get job offers in data science, and obtaining a university education is your best bet. Many data science professionals have master’s degrees, and some of them hold doctorates.

A bachelor’s degree in STEM fields like computer science, physical science, statistics, or mathematics is ideal for someone interested in pursuing a master’s in data science. In the same way, most machine learning solutions are comparable.

Whatever path you choose, there’s no longer any need to be afraid of the male-dominated STEM professions since the pay and job satisfaction are excellent, even if it is a bit steeper for women. Data Science provides security, enjoyment, and the potential for significant compensation.

How Much Does A Data Analyst Earn?

Glassdoor and PayScale were kind enough to provide some insights. If you’re starting your career path in data science, your payment will be around $57,000 (median annual salary of $68,000 and an average bonus of $4,705).

But if you are based in the United Kingdom, the median salary for data analysts with less than one year of experience is £23,870. If you’re a seasoned data analyst with 1-4 years of expertise, your yearly earnings will be £25,853. So it appears that becoming a data analyst may be worth it.

Closing the gender gap

How To Become A Data Scientist In Eight Easy Steps

There are lots of ways to become a Data Scientist. Although it is typically a high-level field, data scientists have traditionally been well educated, with degrees in mathematics, statistics, and computer science, among other disciplines. This, however, has begun to change.

Every person wants to learn how to be a data science expert. Perhaps not everyone literally, but this is unquestionably the most desirable profession in data science today, as demand for this profession is continuously increasing.

Data scientists are autonomous and effective, so if you want to be one, you’re probably wondering what data scientist responsibilities are, how to acquire the necessary skills to apply for jobs, and whether the salary pay will meet your demands. However, the following are the eight easy steps on how to become a data scientist: 

Develop The Right Data Skills

If you don’t have data experience, you can still achieve mastery in data science; however, you’ll need to develop an appropriate background to pursue a data science career. A Data Scientist is a high-level and critical position that requires extensive knowledge in many areas.

It’s essential to have a firm base of knowledge before specializing in a specific discipline. Mathematicians, engineers, statisticians, data analysis, programming, and IT are just a few options. Some Data Scientists began their careers in finance and baseball scouting before transitioning into the field of statistics.

Whatever area you start with, make sure it covers the basics: Python, SQL, and Excel. These skills will be critical in dealing with and organizing unprocessed data. It’s not a bad idea to be somewhat experienced with Tableau, a program you’ll frequently use to create visualizations.

Keep your eyes open for ways to assist you in embracing the mindset of a Data Scientist; the more this background allows you to work with data, the easier it will be for you to advance.

Men and women in the field

Learn Data Science Fundamentals

A data science program or Bootcamp may be a better way to learn or improve data science fundamentals. You’ll learn the foundations of collecting and storing data, analyzing and modeling it, and visualizing and presenting it using every tool in the data science toolkit, including specialized applications like Tableau and PowerBI—among others.

You should be able to develop models that analyze behavior and predict unknowns in Python and R and repackage information into user-friendly formats by the conclusion of your program.

Advanced degrees are often required for Data Science positions. Sometimes, it’s non-negotiable, but as demand outstrips supply, the evidence is becoming increasingly visible.

That is, the evidence of the needed abilities generally outweighs mere credentialism. It’s becoming more apparent to hiring managers that mastery of a subject doesn’t have to be demonstrated in traditional.

big data students

Learn Key Languages For Data Science

Data scientists use various specialized tools and programs built especially for data cleaning, analysis, and modeling. In addition to basic Excel programs, data scientists should be conversant with a statistical programming language such as Python, R, or Hive and query languages like SQL.

RStudio Server is one of a data scientist’s most critical tools, providing a server-based development environment for using R. Another popular tool is Jupyter Notebook, which includes statistical modeling, data visualizations, machine learning capabilities, and more in open-source form.

Machine learning is increasingly used in data science, which uses artificial intelligence to teach systems to learn and improve without being explicitly programmed.

The technology and types of tools utilized in machine learning are determined to a significant extent by the application – for example, whether you’re training a computer to recognize pictures or identify trends from social media posts.

Depending on their goals, data scientists might select from various tools, including h2o.ai, TensorFlow, Apache Mahout, and Accord.Net.

qualified women

Work On Data Science Projects To Develop Your Practical Data Skills

Once you’ve gotten the hang of the programming languages and digital tools data scientists use, you can start putting them to good use by practicing your new skills and extending them even further.

Take on tasks that combine several talents, such as using Excel and SQL to manage and query databases, Python and R to analyze data using statistical methods, build models that analyze behavior and provide new insights, and use statistical analysis to forecast unknowns.

As you get more comfortable with the process, try to touch on several phases, starting with company or market sector research and ending with data collection and cleaning and testing to maximize its usefulness.

Lastly, you have the option of creating your algorithms to interpret and model that information, which may be packaged into simple representations or dashboards for users to interact with and query. You might also practice giving a presentation about your research to boost your communication skills.

You’ll also want to practice working with various data types, including text, structured data, photos, audio, and even video. Every sector uses its own distinct set of data to assist decision-making by more informed executives.

You should know principles of as many types as possible because you’ll likely be focused on just one or two as a working data scientist. However, if you’re starting and building your skill set, it’s essential to understand the fundamentals of as many sorts as possible.

Handling more complicated projects will provide you the opportunity to experiment with a variety of data applications. You’ll be in the best position to use more complex statistical techniques like data mining, predictive modeling, and machine learning (ML) to predict future events or even produce recommendations once you’ve mastered descriptive analytics.

students looking for a new career path

Develop Visualizations And Practice Presenting Them

Use programs like Tableau, PowerBI, Bokeh, Plotly, or Infogram to practice creating your visualizations from the ground up. Find the best technique for allowing the data to speak for itself by practicing with platforms like these.

Even at this stage, Excel is useful: while the fundamental idea behind spreadsheets – merging data in their cells to make calculations or graphs – is simple, Excel has proven to be very valuable after more than 30 years and is almost unavoidable in the world of data science.

Creating beautiful graphics is only the beginning; as a data scientist, you’ll need to use these visuals to communicate your findings to an actual audience.

When it comes to expressing oneself, everyone has their limits. The ability to express oneself without being overly direct is an essential communication skill that may come naturally for you but can be improved with practice. Begin with a single friend or even your pet before progressing to a group situation if necessary.

Creating beautiful graphics is only the beginning; as a data scientist, you’ll also need to use these visuals to communicate your findings to an actual audience.

big data stem science

Build A Portfolio To Showcase Your Data Science Skills

Your next step is to create a polished portfolio that will get you the job of your dreams after you’ve completed your preliminary research, received training, and practiced your new abilities by building out an excellent portfolio full of projects.

Your portfolio may be the most crucial aspect of your job search. The Data Science Bootcamp at BrainStation, for example, is designed to provide a project-based curriculum that helps students construct an impressive portfolio of real-world accomplishments. It’s one of the most effective methods to get noticed in the job market.

Consider including your GitHub projects when applying for a data scientist job to (or instead of) your website. While simultaneously enhancing your profile in a public network, GitHub quickly displays your process, work, and results.

But don’t stop there. Your portfolio is your opportunity to demonstrate your communication abilities and show that you can do more than crunch the numbers.

It’s also beneficial to exhibit a variety of methods because data science is a broad field, implying that there are many ways to address an issue and many strategies you may bring to the table.

You should complement your data with a compelling narrative and show how you’re attempting to fix the problems you’ve identified so the employer understands your value. Rather than presenting your code in isolation, GitHub allows you to contextualize it within a larger context, making it easier to understand.

When you’re applying for a particular position, don’t submit your entire body of work. Just a few pieces out of your whole body of work should be highlighted to show off your range of abilities throughout the data science process – starting with a simple data set, identifying a problem, cleaning up the mess, developing a model, and finding an answer.

community support mentors

Raise Your Online Profile

A well-executed project that you accomplish on your own may be a fantastic method to showcase your skills and impress potential hiring managers. Choose something you’re genuinely interested in, ask a question about it, and try to answer it with facts.

You may also showcase your work on GitHub. Document your experience and reveal your findings, graphically represented with a straightforward explanation of your procedure, highlighting your technical skills and ideas.

To assist an employer in understanding your value, include a compelling narrative that details the issues you’ve addressed—highlighting your process and creative steps you’ve taken to ensure the employer understands you.

Another excellent approach to demonstrate your interest in the community, display your skills as a potential data scientist, and expand your knowledge and outreach is becoming a member of an online data science network like Kaggle.

Global image events

Apply To Relevant Data Scientist Jobs

There are several responsibilities in the data science field. People who have acquired the fundamental abilities specialize in particular sub-disciplines, such as data engineers, data analysts, artificial intelligence, or machine learning engineers.

Look into what a company values, what they’re focusing on, and whether it matches your skills, goals, and plans. Also, remember that cities like Boston, Chicago, and New York are experiencing a technical talent shortage; thus, opportunities abound.

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Best Data Science Jobs

Because data scientists work in so many fields and disciplines, the tasks they can perform are referred to under a variety of names, which includes:

  • Data scientist
  • Data analyst
  • Data architect
  • Data engineer
  • Statistician
  • Business Analyst
  • Researcher
  • Machine learning engineer
  • Quantitative analyst

There are several other positions out there, and they will continue to grow as data science becomes more popular. The good news is that the positions mentioned above are significantly in high demand. You’re at a distinct advantage for professional growth and career advancement if you have data science expertise and experience.

The Field Of Data Science Needs You

Of course, data science is a fantastic career for women isn’t the only reason it might be attractive to you. The field of data science and the basis of decision-making in both businesses and AI (artificial intelligence) has room for improvement in female presence, mainly because it’s so important.

Thus, the more different perspectives in a team, the better. Learn with curiosity and optimism, and don’t be afraid to make mistakes along the way. Work hard, always try your best, and things will come naturally.

a number of male value support

About the author: Joe Silk -

Joseph is a freelance writer who focuses on sharing our Startup Oasis team's abundant knowledge with the rest of the world. Our team has no secret for anything to do with Startups, SaaS software firms, technology, Web and mobile apps, IoT, UI/UX Design. View on Linkedin