Currently working in Data, Marsha has seen first hand how Data and AI roles have skyrocketed with the evolution of Gen-AI. Here she gives insight into STEM (Science, Tech, Engineering and Maths), the importance of Data and the most-commonly held Data roles, and why she is so passionate about mentoring.
Words by Marsha Castello
For the love of all things Tech!
Probably much like you I have always had a love for technology and the way things work! Growing up one of my favourite programmes was a TV show called “Click” which gave the rundown on all new technological devices and inventions shaping our world.
Yet when I was a teen and deciding on my academic options, I never even considered studying computer science, despite my obvious love of this field. Why? Well, much like today it was mostly boys who chose Computer Science. If you looked at any tech or engineering class at college or university, you’d be lucky to find one girl, and that seemed very lonely and intimidating to me. Plus, for some reason I thought that it must be incredibly hard to learn, would require expertise at maths, and therefore would be boring.
On top of all these assumptions, there was little information about the thousands of different roles in tech and STEM in general, or that maths is not required for all of them and neither, in fact, is coding! Girls were often not encouraged to study this field. There was an absence of freely available and accessible information and support to explore this unknown and seemingly intimidating territory.
So, I left well alone and studied Economics and International studies for my undergraduate degree, and International Business and Development. I have many passions, including international affairs and development, but if I could send a message to my younger self, it would be – do not let fear of the unknown deter your path.
In fact, there is only one approach you should take to the unknown, and that is to explore more about it and learn just how capable you are. I have managed to do this most recently! I have been certified in a range of STEM areas from Data Analytics and Project Management to Software Development/Engineering. I can now code in a wealth of computer languages, and do you want to know a secret? It really isn’t that hard! In fact, anything that someone else knows, you can learn too.
How to Get Started
Just start off small and at your comfort level, and keep consistently building on your knowledge - don’t expect to know everything at once. Master one thing at a time. For example, Python is a great coding language for beginners as it is the one most similar to the English language. It will teach you the fundamentals of coding and data structures, and once you are proficient in it, other coding languages become easier to follow!
There are also so many free courses and communities to support you in exploring different areas of STEM, that simply were not available when I was at school. Some are just for girls! Check out: Girls into Coding, Stemettes, InnovateHer, Bright Network, FreeCodeCamp, Motivez, and Girls in Data!
Coding Black Females occasionally offer free events and courses for teens too!
Popular STEM careers include careers in Data, AI, Software Development/ Engineering, Mechanical/Robotics Engineers, Cybersecurity, Scientists, Researchers and many more! Explore more at Bright Network’s STEM hub and in this fantastic STEM Guide!
Data, Data Everywhere!
And why is Data so important you may ask? Well did you know that human beings generate 2.5 quintillion (that’s 2.5 followed by 18 zeros!) bytes worth of data each day? There are currently over 44 zettabytes (that’s 44 000,000,000,000,000,000,00 bits) of data in the entire digital universe! You will agree that that’s a lot of data!
Data professionals like me, are needed to collect, clean, analyse, explore, and make sense of all this data. All jobs, activities, products, services, and processes generate data and this data needs to be processed and analysed. Data professionals are in high demand and data skills are invaluable to have.
The emergence of Generative-AI also has an impact on the demand for data, as data is needed to feed into AI models so that they can work and fairly represent the whole of society.
There are several job roles and careers in data, many of which have different titles yet intersect. I am trained in both Data Analytics and Data Science for example. The main data roles are Data Analyst, Data Engineer, and Data Scientist. There are several differences and overlap between these roles, as shown in the Venn diagram below:
Essentially, a Data Analyst interprets data and presents their insights. Data Analysts extract, transfer and store data, removing any errors (cleaning) to ensure quality, reliability, and scalability, before examining the data to create reports and visualisations needed to make informed business decisions.
For example, I currently produce several reports assessing the performance of my organisation’s products and the efficiency of the teams processing those products so that we can easily identify areas we are doing well and areas where improvement is needed.
This is similar to if you wanted to assess which type of content goes viral on TikTok. You would collect data on the type of reels which generate the most views, likes, saves, comments, shares and downloads. You might ask what the viral reels had in common with each other - was it the length, content, content creator, time uploaded, trends followed, music included and so on. You would look for positive correlations between these variables and resulting engagement. You would present your findings in a report explaining your conclusions and recommendations based on your research on how to make a TikTok reel go viral. Your company could then use this information to sell/market products!
Skills needed to be a Data Analyst include Advanced Excel, and in some cases coding languages such as SQL and Python to collect and process the data, along with data visualisation tools such as Tableau or PowerBI to illustrate your insights with charts and diagrams.
A Data Engineer may be responsible for the design, building, and upkeep of databases and processing systems that support software applications or systems which rely on data. They are responsible for getting data onto platforms that Data Analyst and Data Scientists can use. Skills needed for a Data Engineer include coding languages such as Python and / or Java, big data technologies like Hadoop, or Spark, and database technologies such as SQL, and NoSQL.
A Data Scientist uses statistics, machine learning and coding to analyse data sets and develop predictive models. They explore data, and build and deploy machine learning models (such as those used in Generative-AI). I have built AI models and trained these on samples of test data. Using the test data, the AI model is then able to predict future trends.
For example, using data from other viewers, Netflix uses AI models and algorithms to predict which other shows/movies you may like to watch and recommend these to you based on viewers who have watched and liked the same show as you and the other shows they also liked.
Data Scientists, similarly to analysts, extract insights to solve business problems. Skills needed include statistics, machine learning algorithms, coding languages such as Python and / or R, and data manipulation.
Why not explore the variety of career opportunities in Data and AI, and how to successfully enter this major and rewarding career field, by checking out Coursera’s free Data Guide. If you’d like to discover the wealth of current jobs available in Data and what’s involved, then also check out the Women in Data® job board!
The Importance of Mentoring - Our Journey:
I am very passionate about reaching girls like my younger self from underserved and often marginalised communities who did not have access to the wealth of information, resources and support that is so readily available today. Which is why I volunteer my time as a mentor on several programmes. As part of the Catalyst Collective, I have helped my 14-year-old mentee explore a variety of STEM careers, and learn all about constructing and setting SMART goals. This involved setting SMART goals together, and interactive quizzes to build engagement and consolidate knowledge.
Next, we explored personality tests and learning styles and the subjective validity of these, which included fun activities such as completing personality and learning styles tests and identifying our personalities according to Myers Briggs and learning styles according to the VARK (Visual, Auditory, Read/Write and Kinaesthetic learning styles) model.
I have guided my mentee through creating a vision board to bring SMART goals to life, to enhance motivation and ignite action and self-confidence.
This month I have introduced my mentee to a wealth of effective study and exam revision techniques, and I created an interactive study space on Notion for her which included a Pomodoro timer, study playlist, important links such as past exam papers to revise from, and sections to take notes, in addition to editable daily to-do lists and revision timetables. We will also look at confidence building, post school options and building an effective CV.
Most importantly I have provided space for my mentee to explore and discuss her ideas, aspirations, highlights, and challenges when it comes to planning her future, helping her to build confidence and build self-advocacy. All essential life skills in a world which socialises girls to be shy and self-effacing.
I have learnt that mentoring is never one way and I have been inspired by my mentee just as I hope I have inspired her. She is hugely intelligent and capable beyond her years, and I know her future is very bright. My hope for all participants on this programme is that you go boldly and fiercely in pursuit of your dreams, turn/ re-label fear into excitement and let this energy fuel you!
Marsha is a multi-award-winning Data Analyst, Software Developer/ Engineer, and Cloud professional with PRINCE2 Practitioner Project Management. Marsha has also been appointed UN Woman UK Delegate to the Commission on the Status of Women for a second year running and has been featured in several publications.
Both a STEM, and Women in Data® Ambassador, Marsha is passionate about closing gender equity gaps in STEM and informing women and girls of the many opportunities in these fields for an accessible, rewarding and socially mobile career. To this end Marsha has mentored over 20 girls in STEM and counting.
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