12 things I wish I’d known before starting as a Data Scientist - by Jason Goodman
What I Wish I’d Known about Data Science
1. ‘Data science’ is a vague term, so treat it accordingly
Data science can cover virtually any quantitative work. Two data scientists at different companies, or even within the same company, could do totally different types of work. The field has gradually been fracturing into more specific job titles, such as data engineer, data analyst, machine learning engineer, and so on. This process of specialization will certainly accelerate in the future.
Figuring out what deliverables you’ll be responsible for is often better than reading actual job descriptions, since job descriptions tend to get written to attract a broad range of candidates for a role rather than really detail what the job will entail.
2. Imposter syndrome is a normal part of the job
All you need to do in order to be a good data scientist is to find a way to use data to be useful.
There are lots of different ways to do that. It’s fine to feel imposter syndrome from time to time. Just know it’s normal, and don’t let it get you down. Instead, try to embrace situations where you have something new to learn as exciting growth opportunities, and remember to keep that feeling in mind the next time you encounter someone else who doesn’t know something you do.
3. You’ll never have to know all the tools
Fortunately, you can safely ignore 99% of the data science tools out there. Eventually, your company will have its own set of tools. Everyone at the company will get good at using those tools, and be completely clueless about most of the others.
You just need to know enough to pass an interview. Pick a small set of tools that work for you.
4. However, learn your basic tools well
You don’t have to know every tool, but you should go deep on the basic tools you use daily.
You’ll never regret learning the boring parts of whatever SQL dialect your company uses, like how to write an optimized query. If you use Python, try to really understand pandas, numpy and scipy. Also, git.
5. You’re an expert in a domain, not just methods
Data science came about as a compromise between research science roles and business analyst roles. The former used powerful methods but only indirectly influenced business decisions while the latter directly influenced business owners but wielded limited tools to do so. Data scientists make the most impact when they combine both sides together, mixing deep domain knowledge with the right statistical and engineering tools to make better decisions or useful data products.
Knowing your domain is half the battle, so invest time there, just like you do for your ‘hard skills.’
6. The most important skill is critical thinking
It’s worth actively spending time thinking about the broader context of your work.
What to do as a student to become a Data Scientist
7. Take relevant classes — not just technical classes
Anything that gets you practice thinking critically and making written arguments, such as philosophy, history, or English, can be useful, since that’s a lot of what you do in data science. Social science subjects such as economics or quantitative psychology can be great for gaining experience making causal inferences. A class I think back to often is the persuasive speaking class I took, which I invoke regularly at my job. Take your fair share of technical classes, but learn broadly and follow your interests.
8. Practice communication — written, visual, and verbal
Your impact can only be as good as your communication skills since you need to persuade others to make decisions or help build products based on your analyses.
9. Work on real data problems
The best way to prepare for a job as a data scientist is to use real data to answer real questions.
Find something you’re interested in and get your own data. Scraping data off the Internet is much easier than most beginners realize with packages like BeautifulSoup, Scrapy, and rvest. Wikipedia and Reddit are good targets if you need inspiration, but the best choice is something that you’re genuinely excited about exploring. Then, ask some questions that interest you and see how well you can answer them. Clean the data, make some graphs and models, and then write up your conclusions somewhere public.
10. Publish your work and get feedback however you can
The only way to get better at anything is to get feedback. These days, it’s so easy to post notebooks to Github or personal websites. If you write about a topic your friends are interested in, you can learn a lot from how they respond.
11. Go to events — hackathons, conferences, meetups
Doing so will give you a better understanding of the realities of the field and give you a head start for networking.