Navigating the Data Cosmos: My Odyssey from Graduating to Leading Data Strategies
In the vast universe of career possibilities, my journey from a fresh graduate with a double major in Computer Science and Applied Math to the Head of Business Intelligence at Property24 and AutoTrader has been nothing short of an odyssey. Join me as I recount the twists and turns that defined my trajectory, leading me to become a Senior Data Scientist & Analyst.
The Early Crossroads (2016–2017)
In 2016, armed with a BSc degree in Computer Science & Applied Math, my vision of the future was somewhat conventional. The obvious career paths — Software Development, App Development, Testing — seemed the only choices. However, destiny had other plans. A year later, pursuing my BSc (Hons) in Computer Science, I stumbled upon the captivating realms of Data Analysis and Machine Learning. A spark ignited, and I knew I had found my calling.
Diverging Paths (2018)
Reality hit hard in 2018 when I faced a tough decision. The most viable job offer presented itself in the form of a Java Development graduate program. Although not my dream role, I accepted it out of necessity. Just as I settled into this new environment, fate intervened. A call about a Data Scientist position at Discovery Ltd. turned the tide. Seizing the opportunity, I embarked on my Data Science journey, starting as a Junior Data Scientist at Discovery Health, focused on combating fraud.
Balancing Act (2019–2021)
Simultaneously, I juggled the demands of a part-time MSc in Computer Science, delving into the fascinating world of Computer Vision. My research focused on applying advanced deep learning techniques to revolutionise galaxy classification using Machine Learning. In 2021 I earned a distinction for my dissertation and, armed with newfound knowledge, I transitioned to Mr D Food as an Intermediate Data Scientist. Here, I honed my Machine Learning Engineering skills, training and deploying Machine Learning models using Amazon AWS to enhance the capabilities of the Mr D Food app.
The Evolution Continues (2022–2023)
Emboldened by my achievements, I embarked on a Ph.D. journey at the end of 2022, joining Opti-Num solutions as a Senior Data Science consultant. However, life threw a curveball, and health concerns prompted a temporary pause in my Ph.D. Amid this break, I realised the need for alignment between my expertise and research goals. In 2023, my trajectory shifted once again as I assumed the role of Head of Business Intelligence at Property24 and AutoTrader, steering data strategies to new horizons.
Epilogue
As I reflect on this winding journey, I am reminded that every twist, every turn, and even the setbacks have played a crucial role in shaping my identity as a Senior Data Scientist & Analyst. Each experience has been a stepping stone, bringing me closer to my current position, where I not only utilise my skills but also contribute to shaping the data landscape. The odyssey continues, and who knows where the next turn will lead.
Bonus: A roadmap for complete beginners to begin your journey into the realm of Data Science
- Build a strong foundation
1.1 Learn SQL: Structured Query Language (SQL) is a programming language used for storing and processing information in a relational database. In most Data roles, the company you work for will have their own database to store all their information, which makes SQL an important skill to learn.
1.2 Math: While not everyones favourite subject, learning the fundamentals of statistics, probability, and linear algebra will help position you as a strong Data Scientist or Data Analyst.
1.3 Python: While there are strong pro’s to learning either R or Python,
it is undeniable that Python remains the popular language for Data Scientists. Most job postings I’ve come across specifically state Python as a requirement. If you have never coded before, spend a few hours familiarising yourself with the basics of programming first, for example, Data Structures & Algorithms, Loops, If statements etc. There is an abundance of free platforms (e.g. Youtube, Coursera, Khan Academy etc) which will be able to provide you with this information. Once you have learned the fundamentals of programming (or if you already have some experience coding), dive into topics like “Python for Data Science” or “Data Analysis using Python”, as this will ready you to begin building your portfolio. - Data Analysis & Visualisation
Once you have built a strong foundation you can now start learning about Data Analysis & Visualisation (using Python). As a Data Scientist or Data Analyst, this will form part your day-to-day activities. Dive into topics like Python for Data Science or Data Analysis using Python, as this will ready you to begin building your portfolio.
Outside of Python, dash-boarding tools like PowerBi and Tableau are also commonly used in the corporate world. It would be wise to spend some time familiarising yourself with these tools to give your CV an edge when the time comes to apply for jobs. - Statistics & Machine Learning
Although some companies are constantly pushing the boundaries of Deep Learning, most business problems can be solved using statistical or machine learning methods. Search Youtube and other platforms for topics like statistical modelling using Python and Machine Learning using Python and get a strong understanding of the fundamentals. When learning about Statistics, you want to keep the following items in mind:
- Regression
- Distributions
- Moving Averages
- SciPy and Statsmodels (Package designed for statistical analysis using Python)
When learning about Machine Learning, keep the following topics in mind:
- Regression vs Classification
- Linear Models (e.g. Linear Regression, SVM)
- Tree-based models: Decision Trees, Random Forests, Gradient Boosted Machines
- Scikit-Learn : A great Python package designed to make Machine Learning “easy”. As a beginner, this is a great place to start building machine learning models. - Get your hands dirty
If you have made it through Steps 1–3, you are now ready to start tackling some projects and building your portfolio. What do I mean by “building a portfolio”? I simply mean creating a record showing proof that you are able to actually do the things you say you can do. This is something I didn’t do early on in my career but it is a strong way to show companies what you’re capable of.
To build your portfolio I recommend using GitHub which is a platform that allows you to store and manage your code, and will be accessible to potential employers who want to see a record of what you have worked on. To get started with GitHub you need to learn the basics of Git. It may be a bit intimidating at first, but platforms like GitHub Desktop provide a user interface that makes it a lot easier — especially for beginners.
To find datasets and ideas for projects to start working on, Kaggle is going to be your best friend. It is an online platform for data science and AI which provides datasets for data scientists to practice on. Once you get really good at solving data science problems, Kaggle also has competitions where the person/team who builds the best-performing machine learning model wins a cash prize (sometimes up to the value of thousands of dollars!).
Zindi is a platform similar to Kaggle, that has been designed for Data Scientists in the African continent. - Certifications
If you have (or are studying towards) a STEM degree, you could probably start applying for entry-level Data Scientist or Data Analyst jobs, especially if you have followed Step 4 and built a strong portfolio showcasing your skills and abilities. Once you get a job, you could start doing some certifications to enhance and grow your skillset while working, which in most cases your company will be happy to pay for. If you’re struggling to find a job, however, it may be wise to do one or two certifications to boost your CV.
If you don’t have (or are not studying towards) a STEM degree, don’t be disheartened. A STEM degree may allow you to get a foot in the door, but it certainly does not entitle you to a seat at the table — this must still be earned through hard work. If you have followed Steps 1–4 and have built a strong portfolio, getting some certifications to your name will make you a strong candidate for entry-level Data Scientist or Data Analyst positions.
Because certifications are paid for, I will not recommend any in this article, but if you’ve made it this far and are serious about becoming a Data Professional, feel free to reach out to me and I will gladly advise what kind of certifications you should look at.
It may seem overwhelming at the beginning, but if you stay consistent and dedicate yourself to following the steps I’ve laid out for you, you will be ready to apply to your first Data Science or Data Analysis position in no time! In the beginning of this article I shared my personal journey into becoming a Senior Data Scientist & Analyst to highlight that it’s not easy and there will be times when things don’t work out the way you had hoped, but if you commit to the process and follow my future articles for more advice and tips I am confident that you will be able to achieve your goals. Muhammad Ali once said:
If my mind can conceive it, and my heart can believe it - then I can achieve it.
Thank you for reading! Please follow and join me in exploring the boundless possibilities that a career in Data Science can offer. The journey is challenging, but the destination is worth every step. You can also find me on LinkedIn and Instagram.
