Navigating the Data Seas: My Year-Long Voyage into the World of Data Science
It all started by pure chance during a period of unforeseen circumstances. I had just completed 200 level and my university was on a year-long strike and I suddenly found myself with an abundance of free time and a strong desire to explore new horizons. Up until then, the term “data science” had been a complete mystery to me. My tech journey had yet to begin properly, and my comfort zone consisted of design tools like Photoshop and Figma. While design was a passion, I yearned for something more.
Then, I stumbled upon Python, online, a programming language that intrigued me. But little did I know, this seemingly random discovery would set the stage for a profound transformation in my life. As fate would have it, my path to data science was further shaped by social media. My Twitter feed, which used to be filled with posts about my favorite sport, football, went through a significant shift. Suddenly, posts about data science began appearing on my timeline, and I found myself drawn to them. I started liking and retweeting these data science posts with a genuine sense of curiosity. What happened next was nothing short of remarkable — football-related tweets slowly but steadily disappeared from my timeline and data science started appearing more and more (I later got to understood why this was happening later on anyway). This made me curious, and I decided to find out what data science was all about.
To get a grasp of this new language, I turned to online resources and got to take Udacity’s free Python programming course. Each day was a step forward, and I slowly began to understand the basics of python, functions and scripting. Thereafter, I proceeded to learn how to harness the power of Python for data analysis where I was introduced to libraries and modules such as Pandas, NumPy, and Matplotlib, which are fundamental to data analysis. I then enrolled in the Google Data Analytics Professional Course and got a very solid understanding of what data analytics entails where I got introduced to the tools widely used in the field including Google Sheets & Microsoft Excel, SQL, Tableau and the R programming language. I also got acquainted with the way data professionals approach and solve business problems. All of this gave me a very solid background in the data space and paved the way for deeper exploration within this exciting field.
Next on, I took the Machine Learning Specialization course curated by the influential computer scientist, Andrew Ng. This was a game-changer as it introduced me to the world of machine learning, the heartbeat of AI. The course provided me with a deep understanding of Machine Learning principles & techniques where I also familiarized myself with further toolkits used by data scientists including the web-based jupyter notebook.
Eager to apply all I had learnt, I created a GitHub account and started building projects. It became a platform for showcasing my growing skills and knowledge. I started with small projects including the popular “Hello World” project of ML- The Titanic Survival Prediction and then gradually tacking more complex challenges. I then began regularly committing my codes and projects to GitHub. This hands-on experience was vital in refining my understanding and having practical skills in the field of data science and machine learning.
Lest I forget, I successfully enrolled in the Udacity Data Analyst Nanodegree programme via a scholarship from ALX. Here, I delved deeper into advanced concepts in data analytics. I gained expertise in various critical areas including data wrangling, data cleaning, data investigation, data visualization, effective communication with stakeholders and presenting findings. This intensive program broadened my horizons and equipped me with the necessary tools needed to excel in the world of data science.
This is not to say everything was smooth sailing. Along this journey, I encountered my fair share of challenges and encountered some hurdles. Was it when I was finding it difficult to install anaconda at the start and python modules? Or trying to find my way around jupyter notebook or even the times when I just encountered bugs that cause me much irritation that will at times tempt me to fling my laptop away. There were even times I had to first make a long prayer before I run a line of code that I shouldn’t encounter any form of errors or bugs. Lol, crazy times. Well, each of these hurdles just turned out to be a lesson in disguise teaching me resilience, patience and the importance of perseverance in this evolving field of technology.
As I honed my skills, I started applying my knowledge to real-world projects. From building predictive models that could effectively gauge customer churn behavior to scraping data from the web to build projects and also scraping data via APIs, I dived into practical applications in data science. Over the course of a year, I accomplished a multitude of projects that hold immense value in real-world settings. One significant project involved creating predictive models capable of effectively gauging customer churn behavior. By analyzing various factors, these models could predict which customers were likely to leave a service or product, allowing for proactive strategies to retain them.
Another area I delved into was data scraping, both from websites and through APIs. This allowed me to collect data crucial for my projects, opening doors to a lot of possibilities for analysis and insights. On the application side, I successfully deployed a sentiment prediction model, trained on student feedback data (from my colleagues at the University of Ilorin). This model provided a valuable gauge of student perceptions and sentiments towards their educational experience. It was a practical demonstration of how data science can significantly impact educational experience. Also, I developed an application capable of analyzing WhatsApp chats. This project offered a unique perspective into the potential of data science in understanding communication patterns and extracting valuable insights from conversations. In addition, I also built a book recommender system app trained on Google Books where I obtained data via API calls. This system was capable of suggesting books to users based on their preferences.
In fact, the list of projects I completed within this short span is extensive, each offering great value in a real-world context. These projects aren’t just a reflection of my skills; they represent the potential data science holds to create meaningful applications that can positively influence various aspects of our lives.
The interesting part of my journey is that I was essentially self-taught, learning things on my own. I didn’t get to attend any bootcamps nor do I have a formal mentor or guide to show me the way, but I had unwavering support from my parents, which proved to be my strongest motivation. My parents were my constant source of encouragement. Their belief in my abilities and their unwavering motivation propelled me to explore and learn, even when I encountered challenges. They were always there, reminding me that I could achieve anything I set my mind to, and this belief in me gave me the confidence to continue my data journey.
Their guidance was not rooted in complex technical knowledge, but rather in the simple but powerful act of believing in my potential. Their support was a driving force that kept me going, and I learned that the right encouragement could make a world of difference, even in a self-taught journey like mine. As I continued to learn, I realized that mentorship and guidance can come in many forms, and the belief and motivation of loved ones can be just as influential as formal guidance from experts.
During this journey, I’ve had the incredible opportunity to connect with individuals from around the world who share a passion for data science. This experience has broadened my horizons, opening my mind to the possibilities beyond my immediate surroundings. The connections I’ve made have been more than just networking; they’ve been windows into a global community of data enthusiasts. I’ve received numerous calls and messages from folks who recognized my potential and were eager to collaborate on various projects. It’s been a testament to the power of a global, interconnected world and the opportunities it can bring.
Working on projects for different people has been a fulfilling aspect of my growth. I’ve taken on various assignments and delivered with an excellent success rate, which not only added to my knowledge but also brought in a valuable source of income. Despite the impressive growth I’ve experienced, I remain committed to pushing myself even further. The passion I have for data science has transformed it into more than just a career — it’s become a hobby. When you’re passionate about something, it’s not work; it’s a calling. This passion has driven me to be consistently gritty, determined, and focused on my goals and I’m excited to see where it will lead me next.
My biggest vision in this career path is to leverage my skills to create a positive impact on humanity. I’m driven by the desire to use data science to enhance lives and contribute to making the world a better and safer place for all. Looking ahead, I have exciting plans that I want to take on. I aim to delve deeper into the fascinating world of Generative AI and focus on building large language model applications. This field holds incredible potential for pushing the boundaries of what’s possible in artificial intelligence, and I’m eager to be a part of this journey.
Moreover, I aspire to be more than just a solo explorer. I want to actively engage with communities worldwide and strengthen my physical presence in the global network of data science practitioners. Building strong connections and collaborating with like-minded individuals is a vital part of my vision. One of my core objectives is to assist newbies in seamlessly transitioning into the field of data science. I believe that by sharing my knowledge and experiences, I can help others navigate the complexities of this exciting domain with greater ease.
As I reflect on this first year in data science, the wise words of pioneering statistician, John Tukey ring true — “An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem.”
My sojourn has shown me the importance of framing the right questions, not just pursuing technical perfection. I’m humbled by how much I’ve grown, and energized by how much there is still to learn on this journey. The road ahead promises to be challenging but full of purpose as I continue seeking out the human stories hidden within the data.
