🚀Entering Tech #63: Data science isn’t for everyone

As you probably know given Meta’s updates to WhatsApp, Facebook and Instagram, AI is here to stay. Powering AI is underlying skills such as data science. AI systems are powered by data and data scientists play a vital role in collecting, cleaning, and preparing this data. And without data science, AI systems would be unable to learn, improve, or generate valuable results. However, there exists a shortage of data science talents globally.

But experts say not everybody can fill the boots of a data scientist. 

Last year, we attended Blue Chip’s Data and AI summit, one of the talking points was how not everyone can pursue a career in data science.😲 Why? Well, panelists at the event acknowledged that data science requires a combination of rare technical and non-technical skills. 

Adewale Salami, the CTO of First Bank argued that a combination of having the conviction to solve human-related problems and a hold of mathematics, algebra and statistics skills is the right mix to becoming a data scientist. Tough requirements, you might say.

But you don’t have to be deterred by any of those requirements. Take Olarewaju Oyinbooke, who didn’t have a background in statistics and went on to achieve senior roles in data science at Microsoft and is now a top data management voice on LinkedIn.

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To become a badass data scientist, strong programming knowledge—of either Python or R—is important. However, that’s not all. Olarewaju emphasises curiosity as an important skill for budding data scientists. 

Without it, it will be difficult to deliver great business value. 

You need curiosity to start with data in different forms, “torture” it until a pattern solid enough for business value emerges. Curiosity also helps you clarify several assumptions that could impact the quality of your work.

If you are not naturally curious, you can learn it by applying “the first principle of thinking”, which demands that you ask “why” five times when faced with a problem. 

A proper mix of other soft skills like communication (as you will need to keep stakeholders informed through the different phases of your work), stakeholder management, teamwork, and time management are essential for your career as a data scientist. 

While R and Python are essential programming languages for learning data science, don’t break a sweat on deciding what programming language to stick with. Olarewaju says what matters is what you can do with them. Instead of debating on which of these tools to use, Olarewaju says you should consider:

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Here’s what the typical career path of a data scientist looks like: Data Scientist ➡️ Senior Data Scientist ➡️ Data Science Manager ➡️ VP/Head of Data Science

Now that you have learned the basic skills to become a data scientist, it’s time to land your first role. This requires a strategic approach. 

Olarewaju says your job search will be easier if you have a strong portfolio to showcase your skills. This will help you stand out and open doors to new opportunities.

Additionally, tailor your CV to each role, as generic applications may put you at a disadvantage. Volunteering your skills or training others can also provide valuable experience and interview preparation. Also, working on a project related to a trending topic or industry can help you get noticed. 

Attending job fairs and career events can also help you network, get job placement and learn about industry trends. Similarly, setting up job alerts to stay informed about new openings does the trick. 

To stay up to date and learn about new tools and their applications in data science, Saheed Niyi, the runner-up at Bluchip’s data competition suggests joining data science communities. Here’s a list of communities you should join:

You should also follow thought leaders like Gift Ojeabulu, Bayo Adekambi, Wura Oyewusi, Victor Dibia, Wole Fagbohun, Prince Canuma, Derrick Mwiti, Zoumena Keita, Wes Kembale, Sharon Ibejih, Marlene Maghami, Jessica Ayodele, Bunmi Akinremi, and Olanrewaju Oyinbooke—in the industry is also a way to stay updated.

As much as data talents are in high demand globally, Olarewaju warns against thinking of a career in data science as a get-rich-quick scheme. I’ll add that no career in tech should be seen as a get-rich-quick scheme. 

Due to the plethora of available online learning resources, Olarewaju asks to be wary of consuming too many contents simultaneously without achieving the learning goals for each. Another trap to avoid falling into is the beginners’ loop where you constantly learn without implementing what you have learned.

We hope we have been able to stir you on the path of becoming a badass data scientist. To connect with Olarewaju, you can reach him on LinkedIn and on Twitter. If you’d like to learn about data engineering, our next episode caters to that. See you around! 

P.S: You can also share feedback—or a shoutout—with us about this edition by sending an email to [email protected]. Did we leave something out? Is there something else you’d like to read? Let us know! It helps us write even better editions.

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