At the beginning of 2018, I really wanted to be a data scientist. I thought that the title sounded prestigious, I thought that building "machine learning models" sounded really neat, and it was a hot career to get into.
If any of the points above resonated with you, then I encourage you to keep reading.
I frequently get asked about how one can make a career switch into Data Science from something completely unrelated, like accounting or chemical engineering.
I’ve put a lot of thought into this question. One, because this is what I used to ask myself when I wanted to get into data science. And two, because I think there’s a strong misconception that because have about data science – which is the main purpose of this article.
The Truth About Breaking Into Data Science.
The honest truth (or truths) about data science is this:
1. Not every data scientist does "data science" work.
The term "data science" covers such a wide range of skills and job responsibilities that a data scientist at one company can look completely different from a data scientist at another company. From my experiences, there are barely any data scientists that purely spend their time building machine learning models. Many data scientists also play the role of a data engineer and/or data analyst.
2. Not every person who does "data science" work has the title of a data scientist.
On the other side of the coin, I know plenty of data analysts and data engineers that got to work on data science projects, like prediction models, anomaly detection models, and recommendation systems.
And so, there are a couple of implications that you can get out of this:
- Think about what part of a data scientist’s role interests you rather than the job title itself. Do you like the idea of building machine learning models? Do you want to dive into the world of statistics? Now, I realized that while my reasons for initially wanting to be a data scientist weren’t invalid, they also put me in the wrong mindset starting off – understanding this will give you more direction and meaning in your career.
- Any data-related job is a good start for breaking into data science. In addition to looking for "data science" jobs, I also recommend looking at Data Analysts positions, Python developer positions, Product Analyst positions, and so on. The reason being is that you’ll still develop core data science skills like SQL and Python, and it also opens the number of opportunities for yourself.
What’s Next? Start a Project.
Once you’ve figured out what about data science sparks your interest, the next thing to do is to start your own projects. I personally believe that completing a personal project is 100 times more valuable than completing an online certificate because it prepares you better for what a data scientist actually does.
I also understand that coming up with a project idea can be difficult, so I will leave some ideas below:
- If you’re a basketball fan, can you collect data and see what factors are the most indicative of a team winning a game? Is it the team with the highest 3 point percentage? Is it the home team? etc…
- If you like cooking, see if you can find a recipe dataset (or scrape your own data) and create a model that suggests a replacement ingredient for a missing ingredient.
- If you want to dive into the world of deep learning, can you create a model that detects whether someone is or isn’t wearing a mask? (There are many tutorials on YouTube!)
- For more project ideas, I would check out below:
If you’re not ready for a project…
If you feel like you’re not ready to start your own project, here are some tangible next steps that you can use to guide your learnings:
- Start with statistics. I think statistics is so important because most machine learning concepts and data science applications revolve around statistics. And if you dread statistics, data science probably isn’t for you. I’d check out Georgia Tech’s course called Statistical Methods, or Khan Academy’s video series.
- Learn Python and SQL. If you’re more of an R kind of guy, go for it. I’ve personally never worked with R so I have no Opinion on it. The better you are at Python and SQL, the easier your life will be when it comes to data collection, manipulation, and implementation. I would also be familiar with Python libraries like Pandas, NumPy, and Scikit-learn. I also recommend that you learn about binary trees, as it serves as the basis for many advanced machine learning algorithms like XGBoost.
- Learn linear algebra fundamentals. Linear algebra becomes extremely important when you work with anything related to matrices. This is common in recommendation systems and deep learning applications. If these sound like things that you’ll want to learn about in the future, don’t skip this step.
- Learn data manipulation. This makes up at least 50% of a data scientist’s job. More specifically, learn more about feature engineering, exploratory data analysis, and data preparation.
Lastly, here are a couple of resources that may help you get started:
A Complete 52 Week Curriculum to Become a Data Scientist in 2021
3 Ways to Get Real-Life Data Science Experience Before Your First Job
Thanks for Reading!
I hope you found this insightful! This is an opinionated article, but I think many would agree with me about the two truths of a data scientist:
- Not every data scientist does "data science" Work.
- Not every person who does "data science" work has the title of a data scientist.
As always, I wish you the best in your learning endeavors! 🙂
Not sure what to read next? I’ve picked another article for you:
Why You Should Consider Being a Data Engineer Instead of a Data Scientist.
and another one!
Want to Be a Data Scientist? Don’t Start With Machine Learning.
Terence Shin
- If you enjoyed this, follow me on Medium for more
- Interested in collaborating? Let’s connect on LinkedIn
- Sign up for my email list here!
