As I reflect on my journey and share some dos and don’ts, it’s important not to overlook the power of creating content such as blog posts or social media shares. These platforms not only allow you to showcase your skills but also provide networking opportunities within the data science community and potentially lead to job opportunities.
Five years ago, I found myself at a crossroads. I had recently abandoned my pharmacy studies to pursue a tech opportunity. I faced the decision of returning to university for computer science, charting a different course, or tackling the financial challenges of continuing my education. The prospect of shelling out £9,000 per year for university tuition in the UK didn’t quite appeal to me, especially considering I only had funding for two more years after completing two years in pharmacy.
While the opportunity seemed like a dream come true, I encountered a setback due to the intense curriculum that required additional hours or quick learning capabilities.
In conclusion, my top tips from my data science journey are as follows:
The timeline ahead seemed daunting, but I realized it was ideal for those easing into part-time data science or choosing a slower pace to avoid falling behind due to haste. Taking the time to master the necessary skills was more important than rushing through the process.
If I could start over, I would leverage platforms like LinkedIn and Medium to share my experiences in data science, inviting peer reviews, guidance, and mentorship to enhance and refine my skills.
It appears that many individuals are eager to enter the tech industry, whether by learning to code, exploring product management, or pursuing other avenues. While I may have only accumulated five years of experience in the tech field, I have noticed that some aspiring professionals stress about breaking into the industry without traditional degrees.
It was crucial to set practical goals tailored to my personal journey, rather than comparing myself to others. As I juggled work and delved into mastering Python and machine learning, I passed the challenges but didn’t feel like a data science prodigy.
After establishing your roadmap, it’s crucial to apply your knowledge effectively. Many individuals dive into job applications prematurely, assuming they are ready. However, true readiness comes from honing your skills through hands-on projects.
When I searched for cost-effective courses online, I came across a data scientist bootcamp that offered a compelling deal: nine months of part-time full-time learning that aligned perfectly with my job. I dedicated my days to work and studied until late into the night.
Nisha Arya, a data scientist, freelance tech writer, and editor and community manager at KDnuggets, is passionate about providing advice, tutorials, and theoretical content related to data science careers. Through her work, Nisha explores various topics and aims to demonstrate how AI can enrich human life while enhancing tech insights and writing skills to support others.
Nine months of focused learning proved to be more valuable to me than four years of accumulated debt amounting to £36,000. The best part was that I would only repay a portion of my salary once I secured a job.
I hope this piece offers guidance to those embarking on their data science journey. Beginning anew can be challenging, but my advice is to do it right from the start to avoid retracing your steps.
If you’re unsure how to plan your roadmap, you can refer to “The Complete Data Science Study Roadmap” article. Additionally, here are some resources to assist you in approaching data science projects effectively:
Creating a comprehensive data science plan became vital, focusing on essential components such as a programming language (preferably Python), understanding data science and machine learning principles, statistics, math, and eventually honing expertise in data science, machine learning, and AI. Developing projects was essential to exposing weaknesses and impressing potential employers during interviews.
