From the suffrages held in the US in the early 18th century to the setting up of “Paradigm of Parity” last year, women have come a long way. We now have 30 tech companies worldwide working towards bringing more women into the workforce to bridge the gender gap. There is a massive shift in the hiring space; companies are now dedicatedly focused on conducting more women-centric recruitment drives. Due to this reason, I, being a female techie, am often at the receiving end of taunts about the process being a cakewalk for “us” women.
What is There to Complain About
Unfortunately, it wasn’t until last year that the 30 top tech companies in the world including Accenture, Johnson & Johnson, and BASF, realized the need for higher traction in gender diversity. But hiring is based on an organization’s need and the roles in organizations have evolved over many decades and have become stereotyped. Therefore, what we need is a conscious effort to increase the representation of women among all types of roles across all areas and departments.
What Do the Statistics show
Let us consider the statistics in Figure 1 of assembly students who have taken several software courses over a period of 6 months.
The data above is a close approximation to the percentage of courses taken by students in several years.
Figure 2 shows how many women end up representing in the data science arena.
What Do the Statistics Signify
There are several conclusions one can draw from the stats shown:
Firstly, there is a fair representation of women in courses like Data Analytics, UX Design, Product Management, and JavaScript Development. But far fewer actually end up pursuing a career in these roles.
The numbers grow much more astonishing with only 13% of women in engineering and 25% in math and computer science fields.
Secondly, there is a far greater representation of women in courses and roles of Digital Marketing and far lesser in Data Science. So what drives women to take up the former and not the latter? One cannot even argue that digital marketing is more physically exhausting than data science – both fields require intelligence and commitment!
A friend of mine, working in the digital marketing for the past two years, when asked the same question said, “Digital marketing is all about constant innovation and being creative with content, whereas data science is much more intensive and requires more time to learn.”
This statement was the single most important motivation that I needed to write this blog, in the hopes of making a stronger case for data science and sharing a perspective from a “newbie” about why this field is worth pursuing.
Why Not Data Science
To answer my friend’s argument, yes, data science is quite math intensive and has a steeper learning curve than may be other software fields in the same horizon. Let’s not forget that Katherine Johnson was one of the first mathematicians to have made significant contributions to the Apollo 11 moon mission, and Margaret Hamilton made the onboard flight software for Apollo space mission. These contributions were made around 50 years ago, and it is quite safe to say that STEM has grown exponentially in the past five decades.
The best way to remove the stigma around the complexity of data science is to compare it to everyday chores. I think data science is a quite suited for women, as their natural disposition makes them easy to adapt to this field’s requirements.
Machine learning (ML) or artificial intelligence (AI) took its birth in a world similar to the human nervous system. There are neurons which gave birth to neural networks in the ML world. Any other task, like thinking, walking, and working needs to be trained by a neural network.
There is one particular term called “backpropagation” that helps us in situations like when we touch a hot plate and immediately retrieve our hand. In the similar way, the neural network learns that it’s a mistake, understands the error, and then tries to get the ideal or needed output of “taking your hand away”.
There are several reasons why I feel women are temperamentally suited to the field of data science. Women, for centuries, have been flawlessly managing their homes and in the last few decades, their work life too. I have seen my mother plan the day, the week, and even the entire month of household chores and duties. Therefore, I firmly believe that women’s innate nature is highly suitable for this field.
Before anyone comments on how my premise may be a stretch, let me present my case.
Take a look at the top level abstraction of what data scientists or machine learning researchers do.
Data Correlation: Firstly, machine learning research always has to deal with the relatability and understandability of data. Women are, in general, quite adept at characterizing and correlating data. They can spot elusive patterns in data due to their natural curiosity and their superior ability to communicate.
Planning of Data: The next important thing in data science is prediction of data. As mentioned earlier, women are better planners and data science is one such field that needs excellent planners. Their ability to multitask helps them have a better top view of all information and hence assists them in making better decisions around data.
Intuition: Lastly, even if we rely on neural networks for prediction, we should always try to make sense of the data. A probabilistic approach or results can only take us so far, and women are known to have better intuitive knowledge.
If you still aren’t convinced, data science is one field which has plenty of resources to begin your learning with.
So Where Can One Start?
I have made an extensive compilation of things one needs to learn to start with machine learning. It has all the necessary places one needs to go to on the Internet, including online courses, blogs, research papers, etc. Just click here to access the list. Already a data scientist? Saama is hiring!
I believe that Data Science is a field that is worth fighting for and the field that could use the sharpest of brains. Women can be the next group of go-getters that this field is looking for, so let us leap forward to it!
