Breaking Into Data Science: A Journey from Postroom to Data Analyst
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Chapter 1: The Leap from Postroom to Data Science
After completing an intensive learning period, I felt prepared to transition from my postroom position into the world of data. It was early 2019, and I had diligently checked off several learning milestones: brushing up on Linear Algebra, Statistics, and Calculus, mastering Python, and finishing Andrew Ng's Machine Learning course on Coursera. I even completed a portfolio project. However, there was a nagging voice in my head, a comment from a former colleague that echoed: "Anyone who stays in the postroom for over five months usually remains stuck there." His words fueled my determination to escape.
Having spent six months in the postroom, the statistics were against me. The thought of spending the next 53 years sorting mail for the city of London was daunting. Realizing that a brute-force approach—applying to numerous positions—wasn't viable, especially without a degree or relevant experience, I sought an alternative strategy.
One day, a colleague introduced me to an app called Meetup, a platform for organizing events for individuals with shared interests. This app proved to be a game-changer in my quest for a data science career.
I dedicated my Tuesday and Thursday evenings to attending data-related events. My breakthrough came at an IBM meetup, where I arrived early and sat near the front. During a practical session, a gentleman next to me struggled with a tool. I offered assistance, and he was impressed by my knowledge, leading to an exchange of contact information.
A few weeks later, he reached out, mentioning that he had informed his CTO and CEO about me, and they were interested in discussing my projects. I was both excited and nervous, having never spoken to a CEO before. I took a moment to gather my thoughts before responding affirmatively.
The presentation didn't go exactly as planned, as the CEO was unavailable, but the CTO was supportive and made the experience less intimidating. After reviewing my projects, he asked about my salary expectations, marking a pivotal moment in my journey.
Lessons Learned: Evidence of Capability
Breaking into data science may seem challenging, but it can be simpler than anticipated. Companies seek candidates who can provide value, whether through cost savings or efficiency improvements. By developing a portfolio, I demonstrated my ability to add value and showed that I was a proactive learner, which made me an attractive candidate.
Upon reflection, I realized that the most challenging aspect of entering data science isn't securing a job but rather acquiring a solid foundation of knowledge. This journey requires commitment, resilience, and discipline. Once equipped with the necessary skills, the possibilities are vast, as data is readily accessible online.
The determination to tackle problems and seek solutions is crucial. While unpaid projects may not offer immediate financial rewards, they serve as invaluable training grounds for entering the field.
Networking: The Key to Opportunities
My entry into the tech world was largely due to my network. It opened doors for my first full-time position, podcast appearances, and freelance opportunities. The key takeaway? Networking is essential!
"Who you know often outweighs what you know, though having knowledge is still important."
I emphasize genuine interest in others as the cornerstone of networking. This can take many forms:
- Engaging with others' content meaningfully
- Sharing helpful resources
- Occasionally offering assistance to someone in need
Merely connecting on LinkedIn isn't sufficient; meaningful interactions leave a lasting impression. Building relationships takes time, but it's important to communicate your goals without appearing desperate.
Final Thoughts: A Reproducible Journey
While my path may be unique, many of the steps I took are replicable for anyone willing to be patient. Despite the challenges posed by the ongoing pandemic, it remains possible to cultivate meaningful connections via social media platforms like Twitter and LinkedIn.
Would you like to see a guide on effective networking in the future? Connect with me on LinkedIn and Twitter for insights on Data Science, AI, and freelancing.
In the video "Why Is It SO HARD to Get a Data Science Job?", the challenges faced by aspiring data scientists are explored, shedding light on the complexities of job hunting in this field.
The video "Why You Should NOT Be A Data Scientist" offers a critical perspective on the realities of a career in data science, discussing potential pitfalls and misconceptions.