How to Avoid These 4 Things That Are Keeping You From Becoming a Data Science in 2023
These 4 things may seem beneficial, but they’re actually keeping you from becoming a data scientist this year
Becoming a data scientist is a goal that many of us built for ourselves over the last few years.
Many of us went the self-teaching route, others blended self-learning with more structured programs, and others went to university or bootcamps (where let’s face it, you’ll probably end up self-teaching yourself some of that anyways).
What I’ve discovered over the last three years of teaching myself data science concepts is that four things do a great job of standing in your way of actually achieving your goal.
You can be a self-teaching guru who knows it all: the importance of having a study plan, having each day planned out with what you’re going to learn and the resources you’re going to use, and having short and long-term goals to keep you on track and moving forward. Despite these best efforts, I’ve seen that there are four things that can keep you in a rut, which can result in it taking years before you become a data scientist.
Here’s how to avoid them.
Avoid: Paid data science courses
The theory that paying for a course will make you complete it is completely falsified when you think of how many people pay for a gym membership yet stop going after the first few weeks.
If a monetary investment is what it takes for you to finish a course, then all the power to you.
But for most of us, it doesn’t necessarily matter if we’ve sunk money into something — if we’re not motivated, we can’t even be forced to finish it.
Therefore, in 2023, we’re going to avoid paying for data science courses. Why? Because there’s no point paying for something you can get for free online. Not only is there no loss if you decide that a particular course isn’t working for you, but it also leaves you free to explore other options to complete your learning — which allows you to work towards becoming a data scientist more quickly.
What to do instead
From personal experience, you can find the equivalent of almost any university computer science, software engineering, or data science course on Youtube for free. Additionally, the instructor in the Youtube video is probably going to explain the concepts better than the university professor anyways.
Youtube has hundreds of videos to help you learn any of the many topics in data science. FreeCodeCamp can help you learn to code and do data analysis, Khan Academy, Professor Leonard, and 3Blue1Brown can help you learn mathematics, and Alex the Analyst can help you analyze data. There are many more great Youtube channels out there that provide free data science learning content in easily consumable formats.
If you still want to go for a more structured course, many universities (such as MIT) are now making some of their courses available for free on MOOC platforms, such as edX. There, you can find courses on machine learning, calculus, statistics, differential equations, data analysis, and more.
I prefer going the accessible route when it comes to learning data science because I’ve found that oftentimes, while the learning plan doesn’t change, the learning resources will. Nothing will keep you from becoming a data science more than trying to force your way through learning resources that just aren’t doing the trick for you. I’ve found that you’ll learn much quicker if you’re able to jump around and use sources that fit the bill for each specific topic.
Avoid: Practicing interviews with an unskilled interviewer
We’ve all been there where we’ve been interviewed by our moms, our goldfish, or even our cactus. Unfortunately, unless your mom, goldfish, or cactus has been working in HR for 30 years or has extensive data science career experience, these interviews aren’t as fruitful as they could be.
When you practice with an untrained entity, you don’t get feedback on your responses that could help you interview better. At best, you’ll get a supportive “that was great!” from your mom or a seemingly somewhat interested fin wave from your fish. Interviewing is such an important skill for the entirety of your career as a data scientist, that it shouldn’t be taken lightly. You need to practice with someone or something that can give you concrete advice on how you can be better.
Learning how to interview well and practicing these skills is arguably the most important thing you’ll do during your time becoming a data scientist. Everything else is important as well, but your interview is what will seal the deal and make all of your hard work worth it.
So, in 2023, we’re avoiding practicing interviews with unskilled interviewers.
What to do instead
I like to use ChatGPT to practice interviewing for data science positions.
The first way I like to use ChatGPT is to see what kind of responses it would give to both soft skills and technical questions. First I’ll type in any of the hundreds of questions you’ll find in data science question banks and then I’ll see what it responds with. Then, I compare ChatGPT’s response with what I would answer to the question. If I’m missing anything critical, I’ll add it to my response in a relevant way.
The next way I’ll use ChatGPT is to make sure I really understand what I’m talking about. I’ll use the program to define terms that I’m unsure of, and I’ll ask it to explain concepts to me to make sure that my understanding matches.
You can also ask ChatGPT to provide you with mock interview questions, such as those you would see in a technical or live coding interview. You can ask it to provide you with technical interview questions, such as those about data structures and algorithms, and you can ask it to provide you with coding questions, which it will pull from LeetCode. The latter is a great way to practice your coding skills, both while you’re learning to code, and also while you’re preparing for an interview. You can try using ChatGPT to simulate a live coding interview using this prompt from NodeFlair:
I want you to act as an interviewer. I will be the candidate and you will ask me coding interview questions for a Junior Software Engineer position.
I want you to only reply as the interviewer. Do not write all the conversation at once. I want you to only do the interview with me. Ask me the questions and wait for my answers. Do not write explanations. Ask me the questions one by one like an interviewer does and wait for my answers.
Ask me a random { Easy / Medium / Hard } Leetcode question and evaluate my solution based on correctness, and the time and space complexity.
Avoid: Trying to understand why the math works
People usually enter the data science field because they have a particular proficiency in math. However, sometimes, a brave few decide to enter the field without having extensive backgrounds in the subject. The problem these individuals have is they get wrapped up in understanding why the math works the way it does, which keeps them from advancing in their data science studies.
While some data science jobs are extremely theoretical and will require this type of in-depth understanding, not all positions are as such. Some positions will be happy with you knowing calculus, statistics, and linear algebra and that’s it.
Therefore, depending on the type of data science position you’re looking for, you can save some time and headaches by avoiding trying to understand why the math works the way it does.
What to do instead
Understand when to use the math and what information the math provides you with.
Focus your mathematical learning on:
- Identifying situations when you need to use specific mathematical concepts;
- What information those calculations will provide you with; and
- How you can use them to solve problems or draw conclusions.
From personal experience, you’ll find that you can progress much quicker through your math courses when you focus on these three important concepts. This is extremely helpful when working through calculus and other more advanced forms of mathematics that can be very theory heavy while burying the practical applications somewhere in the subtext.
My favorite trick is to create a mind map at the end of every unit of learning. There, I aggregate all of the important concepts, such as definitions, formulas, tables, charts, and more. This provides a quick reference for what is important and helps you continue to learn by disseminating the practical from the theoretical.
Avoid: Learning more than 4 coding languages
How many data scientists use more than 3–4 coding languages in their daily work? Not many.
So why waste your precious time trying to learn many coding languages that you probably won’t use once you land a job as a data scientist? Why not learn the coding languages you need and then move on to learn the next thing?
What to do instead
Pick 2–4 coding languages to learn that are most relevant for the type of data science job you want to do — no more, no less.
Start by taking a moment to browse data science jobs that interest you and note down the coding languages they’re looking for. After going through your 20th job ad, you’ll be able to notice the same 2–4 languages recurring at a higher rate than the rest. Focus on learning these languages and discard the rest.
There will be time after you’ve got a job as a data scientist to learn additional languages that have piqued your interest, but your energy is best focused on learning the ones that will get you a job immediately. It’s 2023 — we don’t have time for things that will delay our future career happiness and success.
The coding languages you’ll probably be focusing on learning will be Python or R and SQL, followed by maybe one other language such as JavaScript, MATLAB, or Scala (you’ll also want to know Excel, but that’s not entirely a coding language, more so a program that makes your data cleaning life that much better). Luckily for data scientists, Python and R have so many features packed into them that you can do pretty much everything you’ll ever need as a data scientist using one of those languages (along with SQL).
In my experience of studying data science jobs and the evolution of the data science profession, knowing Python or R and SQL will make you qualified for most data science positions out there. From there, you can decide what else you think you need or want to know — but remember: put your time and effort where the immediate reward is, the rest can be learned later.
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