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To develop a successful career in data science, you need to strengthen what I consider to be the six main pillars of the area: technical skills, building a portfolio, networking, soft skills, and finally developing a niche specialty. Once you have all that, you also need to perform well at the interview stage.
Too many would-be data scientists think it’s all about the skills, and neglect the network. Or you rely on a network contact to get you the job interview, but stumble under the pressure, and don’t do your skills justice.
None of these sections are really optional, but this is probably the most important one of the six. You might stumble into a job if you don’t know the right people, or if your portfolio isn’t perfect, but if you don’t have the right skills, you won’t get the job. Or worse: you might get the job, but you’ll crash and burn. And get fired.
Here’s what you should focus on:
Learn the fundamentals
Every data science job requires a strong foundation in mathematics, statistics, and programming. Proficiency in languages like Python or R is essential. Almost every data science job description will mention one of those two languages.
I also suggest you consider learning SQL as a fundamental requirement. SQL databases are a reality of life for data scientists. And it’s a comparatively simple language to learn.
Image from r/datascience
Machine learning and data manipulation
It’s not just the recent rise of AI; data scientists have always needed mastery of machine learning. You will need to gain expertise in machine learning algorithms, data preprocessing, feature engineering, and model evaluation.
Data visualization
A data scientist’s findings are worthless unless she can communicate them to another. This is done with graphs, charts, and other types of data viz. You’ll need to master data visualization tools and techniques to effectively communicate insights from data with key stakeholders at your company.
I’ll get into this a little more when I talk about the soft skills, too – communication is a vital skill.
Big Data technologies
Gone are the days when data scientists dealt with little data, if they ever existed. Today, you’ll need to be extremely familiar with big data and the requisite tools. Even if your company doesn’t handle truly “big” data, they’ll aspire to it.
Familiarize yourself with tools like Hadoop, Spark, and cloud platforms for handling large datasets.
Onto pillar two: your portfolio.
There’s a dearth of qualified data scientists, as you probably know. Bootcamp grads rose to fill the gap. That caused a new problem: lack of trust. See, companies know a degree isn’t necessarily a needed qualification to do a good job. However, bad bootcamps also gave aspiring data scientists a bad rap, because many boot camps churned out “graduates” that didn’t know a join from a subquery. Hence, your personal portfolio is a chance for you to prove you know your stuff. (It’s also worth noting that boot camps are very expensive, especially compared to the slightly less optimistic job outlook currently.)
Image from r/ProgrammerHumor
Here’s what you need:
Personal projects
Work on personal projects that showcase your skills. These could be Kaggle competitions, open-source contributions, or your own data analysis projects. You can maintain a well-organized GitHub repository to showcase your projects, code samples, and contributions.
Blog or website
Consider creating a blog or personal website where you can share insights, tutorials, and case studies related to data science. It’s possible to cheat this system and hire someone to do it for you, but it’s so expensive and time-consuming that few people try to falsify it. A blog serves as a great portfolio of your knowledge.
Be ready to explain your projects, methodologies, and problem-solving approaches. Brush up on common data science interview questions and coding challenges.
Remember the golden rule of jobs, no matter the field: potentially as many as 70% of job listings are never advertised. This is an old stat, but even if it’s 20 to 30 percent, it proves that who you know matters. That’s not even considering that as many as a third of job openings posted are actually fake, designed to make a company look more successful than it is. A personal network can help you avoid wasting your time.
Here’s what you should do:
Join professional networks
Join data science communities, and attend meetups, conferences, and webinars to connect with other professionals in the field. This more formal approach to a network can help you meet the right folks, make a splash in your industry, and stay up to date with current events.
Social media
More informally, you should also engage on platforms like LinkedIn, Twitter, and relevant forums to share your work, and insights, and learn from others.
Remember, hard skills are only half the battle. That’s why you need to ensure that your soft skills aren’t neglected. I’m not saying soft skills are more important. Hard skills vs soft skills is a false dichotomy – they’re both important. But people don’t hire data science machines, they hire people. Here are the areas I recommend focusing on:
Communication
Remember that data viz skill? Data scientists need to effectively communicate complex technical findings to non-technical stakeholders. It’s amazing how much of a data scientist’s job comes down to explaining why someone in marketing should understand the pretty graph.
Problem-solving
It’s almost a meaningless buzzword at this point, so make sure you actually understand what “problem-solving” really means. In the context of data science, solving problems isn’t just debugging. It’s also knowing when it makes sense to collaborate with different departments, when to rejig a project’s tech stack to meet new specs, or going back over your model if it stumbles on the test dataset.
Image from r/DataScienceMemes
Critical thinking
Another almost-buzzword that merits deeper consideration. Critical thinking means the ability to analyze data from multiple angles, question assumptions, and think creatively to derive meaningful insights.
Teamwork
Data scientists don’t work in a vacuum. You’ll work with web developers, data analysts, business analysts, marketers, salespeople, and CXOs. Collaborate with cross-functional teams to understand business needs and align data-driven solutions.
Haven’t you heard? We’re in the middle of a tech winter for hiring. Venture capital money isn’t flowing like it used to, and companies are tightening their belts. It’s not a good time to be a generalist. You’ll need to specialize to survive.
Choose a niche
Data science spans various industries, such as healthcare, finance, e-commerce, and more. Specializing in a particular domain can make you more attractive to employers in that field. Look for what you’re naturally interested in, or where you might already have extra knowledge.
Domain knowledge
Acquire domain-specific knowledge relevant to the industry you want to work in. This helps you understand the nuances of the data and make more informed decisions. For example, if you want to work at Google, you’ll need to know the intricacies of search algorithms and user behavior.
Last, but certainly not least: prepare for interviews. You can nail the first five pillars and still stumble at the finish line. Here’s how I recommend you prepare:
Explanations
You can know a concept without really being able to explain it to others. For the interviews, you will have to be ready to explain your projects, methodologies, and problem-solving approaches.
Take the time to ensure you not only have a complete understanding of what you did, why you did it, and why it works for all your projects but that you’re able to explain it well enough that a layperson could understand. (this is also a great way of practicing that “communication” soft skill.)
Coding prep
The whiteboard is a famous pillar of coding interviews, yet so many people panic when faced with that blank, white surface. The more you practice interview questions ahead of time, the better you’ll perform under pressure on the day.
It’s a little presumptuous to even pretend there’s a single right answer here, or that it could be explained in an article. Hopefully, this blog post acts more like a roadmap than a comprehensive solution. Practice these six pillars of data science jobs, and you’ll be well on your way to developing a career in data science to last as long as you want.
Nate Rosidi is a data scientist and in product strategy. He’s also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Connect with him on Twitter: StrataScratch or LinkedIn.
