Newbie Career Roadmap towards Data Science

A smart career transition journey from Business Development to Data Science

There is no doubt that data science is now one of the most demanding jobs in the world today. However, it has become increasingly hard and overwhelming for new data enthusiasts to join this path. Don’t worry, been there too and oh boy!!

I know you are wondering how this article is different from all the countless that pop up on your screens and those you have already read. Well, stick with me to find out. I am not here to give you an ultimate list of learning paths, and resources that you must master to successfully change your career to data science. I am here to share an approach on my journey that is helping me maneuver, with the hope that it will help you too.

Brief Background

The first time I encountered data science was as an Account Manager at a training and consultancy firm. I got the opportunity to spearhead the product development for the new ICT training curriculum. I was working towards the 4th Industrial Revolution (4IR) technologies when I came across the big data module, which piqued my interest in the field. Having worked as a business development professional, I was exposed to some aspects of big data, mostly on sales analysis, forecasting, and reporting. At this point, the only tools I relied on were Excel and Salesforce.

Upon research, it became clear that my progress in business development was being hindered by my lack of knowledge on more efficient tools for data analysis. This now became the basis of my career switch to data science.

Learning Experience

“The world is your oyster. It’s up to you to find the pearls.” ― Chris Gardner

The internet is indeed your oyster. But for a beginner like me, the number of resources and amount of information on where to start, what to read, and how to build a portfolio, can be overwhelming. To overcome these hurdles I signed up for a Switching to Big Data Career Coaching Program at Predictive Analytics Lab. Here, I underwent a 1-month coaching program that gave me an understanding of the field and how my career journey would look like. Having had a clear laid out structure, I signed up for the Introduction to Data Science class and later enrolled in some MOOCs from Udemy and Coursera to perfect that which I already learned.

Part of growing your skills in data science also requires networking. It is good to be part of a tech community and groups of like-minded individuals. I am currently a member of Women Who Code Data Science, Nairobi Women in Machine Learning & Data Science, and She Code Africa communities.

Mentorship Steered Roadmap

Being part of the She Code Africa tech community has offered immense growth to my data science journey. The community offers a Levelled Membership program that allows one to boost their skills and career growth while interacting with other members.

In January 2021, I took the step towards applying for the SCA Mentorship Program. The program was very competitive, but I was fortunate to be selected to be part of Cohort IV (Data Science Track) which kick-started in March 2021. You can read more about what the mentorship program entails here.

screenshot from my email

As I write this, I have just completed my first month as a Data Scientist Mentee at She Code Africa. You can read all about my experience so far here. The journey has been challenging but overall, the structured weekly learning paths are insightful and a breathe of fresh air, especially if you are a data science newbie.

Coaching & Mentorship Benefits

Could a mentorship-steered learning path with a dedicated mentor be the best option in navigating a data science journey as a newbie? Well, I think this new exploration between mentorship and protégé is a successful learning curve that many ought to try out. Here’s why:

✵ A mentor steers you through a learning path that is best suited for your career goals and market needs.

✵ Mentorship helps you develop a roadmap on the relevant skills and resources o focus on that would give you the best results in your journey.

✵ Through mentorship, you can get constructive feedback on your areas of strength and how to tackle weak areas that require improvement.

✵An engagement with a mentor enables you to seek answers, get advice, and share ideas or thoughts with zero judgment.

✵ Mentorship is free. You get a dedicated mentor to support you in your journey. How cool is that?

In conclusion

The truth is, starting the transition towards a data science career can be daunting, hectic, and overwhelming. So far, I find that apart from data science resources out there, working with a mentor is what it takes to build the foundation to get there. This is because a professional data scientist has been in your shoes, knows the job market, and knows what it takes to get there while avoiding some pitfalls they might have made. Through their expert opinions, you easily curate your own “ultimate” learning path towards being an industry-ready professional.

This month, I officially mark one year since I started this career change journey. Indeed, it has been tough and I have almost given up several times. Something I have learned along the way is that some may say there are no shortcuts around any journey. I agree based on what resources one needs to cover or skills one needs to gain. But when it comes to avoiding pitfalls and building that foundation, a mentor might just be your key.

I am yet to master this field, but I now take comfort in knowing that I am continuously evolving and that I am now one step ahead to achieving my dreams.

Thanks to She Code Africa and Predictive Analytics Lab for their mentorship and for taking a chance on me. Thank you for your part in my career change journey.

I hope this article becomes a stepping stone to your new data science career journey. Please remember to clap and share with anyone who will find this useful in their journey. Feel free to connect with me on LinkedIn or Twitter.

☲☲☲☲☲☲☲ Cheers to a successful career switch! ☲☲☲☲☲☲☲