NEWSLETTER
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To develop a successful career in data science, it is necessary to strengthen what I consider 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 will also need to perform well at the interview stage.
Too many aspiring data scientists think it’s all about skills and neglect networking. Or you rely on a network contact to get you the job interview, but you stumble under the pressure and don’t do justice to your skills.
None of these sections are really optional, but this is probably the most important of the six. You may find 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 basics
Every data science job requires a solid foundation in mathematics, statistics, and programming. Mastery of languages such as Python or R is essential. Almost all data science job descriptions will mention one of those two languages.
I also suggest that you consider learning SQL as a fundamental requirement. SQL databases are a fact of life for data scientists. And it is a comparatively easy language to learn.
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Machine learning and data manipulation.
It’s not just the recent rise of ai; Data scientists have always needed to master machine learning. You will need to gain experience in machine learning algorithms, data preprocessing, feature engineering, and model evaluation.
Data visualization
A data scientist’s findings are worthless unless they can communicate them to another. This is done with graphs, tables, and other types of data. You’ll need to master data visualization tools and techniques to effectively communicate data insights to your company’s key stakeholders.
I’ll talk about this a little more when I also talk about interpersonal skills: communication is a vital skill.
Big data technologies
Gone are the days when data scientists handled little data, if it ever existed. Today, you will need to be very familiar with big data and the necessary tools. Even if your company doesn’t deal with truly “big” data, it will aspire to do so.
Get familiar with tools like Hadoop, Spark, and cloud platforms to handle large data sets.
On to pillar two: your wallet.
As you probably know, there is a shortage of qualified data scientists. Bootcamp graduates rose to fill the void. That caused a new problem: lack of trust. Look, companies know that a degree is not necessarily a necessary qualification to do a good job. However, bad boot camps also gave aspiring data scientists a bad reputation, because many boot camps produced “graduates” who didn’t know a subquery join. Therefore, your personal portfolio is an opportunity to show that you know what you are doing. (It’s also worth noting that boot camps are very expensive, especially compared to the slightly less optimistic job prospects that currently exist.)
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This is what you need:
Personal projects
Work on personal projects that showcase your skills. These could be Kaggle contests, open source contributions, or your own data analysis projects. You can maintain a well-organized GitHub repository to showcase your projects, code examples, and contributions.
Blog or website
Consider creating a blog or personal website where you can share ideas, tutorials, and case studies related to data science. It is possible to cheat this system and hire someone to do it for you, but it is so expensive and time-consuming that few people try to fake it. A blog serves as a great portfolio of your knowledge.
Be prepared to explain your projects, methodologies, and problem-solving approaches. Review common data science interview questions and coding challenges.
Remember the golden rule of jobs, no matter the field: potentially as much as 70% of job offers are never advertised. This is an old statistic, but even if it’s 20 to 30 percent, it shows that who you know matters. That’s without even considering that as many as one third of the job postings posted are actually fake and designed to make a company appear more successful than it is. A personal network can help you avoid wasting time.
This is 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 networking can help you meet the right people, make waves in your industry, and stay up to date with current events.
Social media
More informally, you should also participate on platforms such as LinkedIn, Twitter and relevant forums to share your work and knowledge, and learn from others.
Remember, hard skills are only half the battle. This is why you need to make sure that your interpersonal skills are not neglected. I’m not saying that social skills are further important. Hard skills vs soft skills It’s a false dichotomy: both are important. But people don’t hire data science machines, they hire people. These are the areas I recommend focusing on:
Communication
Remember that data visualization skill? Data scientists need to effectively communicate complex technical findings to non-technical stakeholders. It’s surprising how much a data scientist’s job boils down to explaining why someone in marketing should understand the pretty graph.
Problem resolution
It’s almost a meaningless buzzword at this point, so make sure you really understand what “problem solving” really means. In the context of data science, solving problems is not just debugging. It’s also knowing when it makes sense to collaborate with different departments, when to readjust a project’s tech stack to meet new specifications or revise your model if it stumbles on the test data set.
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Critical thinking
Another almost buzzword that deserves deeper consideration. Critical thinking means the ability to analyze data from multiple angles, question assumptions, and think creatively to gain meaningful insights.
Teamwork
Data scientists don’t work in a vacuum. You will work with web developers, data analysts, business analysts, marketers, marketers, 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 is not flowing like it used to and companies are tightening their belts. It’s not a good time to be a generalist. You will 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 are naturally interested in or what you may already have additional knowledge of.
Domain knowledge
Gain domain-specific knowledge relevant to the industry you want to work in. This will help 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 not least: prepare for interviews. You can nail the first five pillars and still stumble at the finish line. This is how I recommend you prepare:
Explanations
Can know a concept without really power explain to others. For interviews, you will need to be prepared to explain your projects, methodologies, and problem-solving approaches.
Take the time to make sure not only that you have a complete understanding of what you did, why you did it, and why it works for all of your projects, but also that you are able to explain it well enough that a layperson can understand it. (This is also a great way to practice that “communication” soft skill.)
Coding preparation
The whiteboard is a famous mainstay of coding interviews, but many people panic when faced with that blank white surface. How much more practice interview questions If you do it in advance, the better you will perform under pressure that day.
It’s a bit presumptuous to even pretend that there is a single correct answer here, or that it could be explained in an article. Hopefully this blog post acts more as a roadmap than a comprehensive solution. Practice these six pillars of data science jobs and you’ll be well on your way to building a data science career that lasts as long as you want.
Nate Rosidi He is a data scientist and in product strategy. He is also an adjunct professor of analysis and is the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real questions from top companies. Connect with him on Twitter: StrataScratch either LinkedIn.
