Inclusion Diversity and Data Science

Inclusion Diversity and Data Science

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The Digital Divide...You probably have heard about it, but let's talk more about it. The digital divide is where people of color don't have equal access to the internet as others races, and as we all know, the internet drives the world these times.

Yes, there is a digital divide, and we have to accept that the gap exists. However, there is new research coming out that reveals that African Americans and Black People do have access to the internet, particularly on their mobile devices.

In this article, we will paint the bigger picture of how inclusion and diversity have evolved over the years, and we'll also discuss key aspects of the same.

As we enlighten ourselves, we can proudly embrace the growing diversity of our global community. Together we can create an environment where women, people of color, LGBTQ+ individuals, and other underrepresented communities feel empowered to pursue different fields of data that were previously inaccessible.

What Are the Key Barriers To entry into the Data Field

The main reason we currently lack diversity and inclusion in the data ecosystem can be attributed to the following main players in the ecosystem:

  • Awareness
  • Assess.


Awareness is using data without being aware of the information's power. I consider this as a barrier since you can't understand the value of information if you don't have proper context. Great examples include the information that we share on a day-to-day basis with our doctors during checkups, our financial institutions, and even on the different websites that we log in to access services every day.


In the intro, I touched on the concept of the digital divide. This concept plays a great role in Access, where underrepresented communities can use the internet as well as its creative and innovative tools.

Data Science is a field that is very open. It has different definitions, threads, educational pedagogies' and structural strategies. The person delivering the information and the information's expertise matter when it comes to access.

There is currently a surge of boot camps. These boot camps can last for a week but are mostly between two to twelve weeks, and they can be costly. Does that make learning accessible to everyone? That I leave it to you to answer.

And if one decides to attend that boot camp, do they have the machinery they need to ensure they completely absorb content from the boot camp?

The above example gives us a picture that there are different stumbling blocks that can be transformed into opportunities. An opportunity for us to learn that there is a need for a better mechanism for access where educators meet learners where they are as opposed to where they want to teach from.

Support Media Existing for Underrepresented Persons

Instead of us just identifying the problem, we need to leave our comfort zones and make the change we want to see. Different people and organizations have started meaningful initiatives to support inclusion and diversity in Data Science.

They include:

  • She Code Africa
  • Empower Her Community
  • Black in AI
  • Women in ML

How Can We Do Better

We are trying to write off a wrong that started generations ago. So as a community, we need to be intentional about our thirst for inclusion and diversity. It has to be at the core of every engagement. We can achieve this by using the effective PAIR Principle;

P - Participation A - Assess I - Inclusion R - Representation

As a community, let's take implement these principles and ask ourselves the following questions.

  1. Who is participating in this event/boot camp?
  2. Who has access to the information?
  3. Who is included and who is excluded?
  4. Who is represented and who isn't?

    It all depends on where you look and if you are really looking, INTENTIONALLY. We all have a part to play, so let's be intentional.