Starting a Career in Data Science (10 Thing I Wish I Knew…)

• Focus on avoiding the most common mistakes while learning data science.
• Start with fundamentals in statistics and machine learning rather than coding.
• Don't feel the need to become a data analyst before stepping into data science.
• Follow a structured learning plan and leverage generative AI in your projects.
• Avoid getting trapped in tutorials; hands-on practice is crucial.

If you got to this video, that means you have probably watched a lot of videos about data science roadmap and how you can become a data scientist. In this video, we will focus on mistakes that you should be avoiding while learning to become a data scientist. These learnings are based on my experience in the industry over the last 10 years, working in the data science domain and especially becoming a data scientist in a non-traditional way.

This is the first thing that you should avoid in your journey of becoming a data scientist. There will be many people who will tell you to start with coding, whether that is Python or something else. I completely disagree with that advice. Coding is a tool to apply data science. It is not data science in itself. I've seen many people make this mistake, where they will jump into coding, start learning Python, start learning SQL, and think like this is the right way to approach it.

Okay, maybe it is the right way to start if you are very new to coding and have no background. But what data science actually is, is statistics and machine learning. That's the core knowledge that a data scientist needs to know. Coding is simply a tool for applying statistics and machine learning in data science. So, I will say this again: Coding is a tool to apply statistics and machine learning in data science. It is not data science in itself.

So what I would like you to do is to start learning the fundamentals in statistics, machine learning, and math, and see if this is something that you enjoy doing. Because if you start doing that and realize that you don't enjoy it, then I would want you to stop learning coding. Languages such as Python and SQL are very powerful, but they are just tools to apply data science.

Second, and this is one of my biggest pet peeves: a lot of people believe that they need to become a data analyst before becoming a data scientist. This is not true. You do not need to become a data analyst before becoming a data scientist. Okay, I'm going to say this again: you don't need to become a data analyst before becoming a data scientist.

This is such a big misunderstanding that a lot of people have. They jump right into data analytics, learning all the skills required for that role, such as data analysis, SQL, and building dashboards. Yes, data scientists and data analysts share many concepts, but data science is more than that.

As a data analyst, you're going to spend a lot of time building dashboards. As a data scientist, you will likely not use that skill set as much. Additionally, as a data scientist, you will require more statistics and machine learning knowledge.

If you choose to become a data analyst, you’re going to miss out on a lot of time that you could have spent on learning statistics and machine learning concepts to solidify your knowledge. Use that time to build projects in data science or gain experiences through internships or personal projects. So, if your end goal is to become a data scientist, please reconsider your decision to become a data analyst.

Yes, many people transition from data analyst to data scientist, and it’s totally fine if someone doesn't know they want to become a data scientist at first. It's easier to transition from data analyst to data scientist, but if your end goal is to become a data scientist, plan your roadmap accordingly.

One of my recent videos talks you through the entire data scientist roadmap, so definitely watch that.

Number three is the mistake that I personally made when I started in the data science domain. I jumped right into the data scientist role without knowing that many other roles exist, such as machine learning engineer, AI engineer, data analyst, and data engineer.

There are many roles in the data science domain, including the booming role of AI product manager. If you want to get into data science, don’t just jump into one career right away. Understand what different roles do—what a data analyst does versus what a machine learning engineer does. This research can help you discover a role that sounds more intriguing to you.

The fourth mistake that many make when learning data science is not following a plan. This ties back to my first mistake—just jumping into coding. Yes, that's better than doing nothing, but if you want to become a data scientist, make sure you are proceeding with a plan.

Create a roadmap! One method I highly recommend is to work backward. Identify your target company and role, understand what a data scientist at that company does, and then look at the job descriptions. Search LinkedIn for data scientists at that organization, observe their projects, and grasp their educational backgrounds. This research will help you define the plan you need to follow to become a data scientist.

So when you start learning data science, ensure that you have a plan to stay on track. Allocate time in your calendar over weekends or after work to adhere to that plan diligently.

Speaking of creating a plan, I found an Intro to Python ebook, a beginner’s guide to learning Python for data analysis, created by Hubspad, who sponsored this part of the video. The ebook covers essential libraries like Pandas, NumPy, and Matplotlib for analyzing data with Python. It also walks you through basic ideas and provides coding snippets for practical use. It is available to download for free, linked in the description below.

Now let's talk about the next thing to avoid while learning to become a data scientist. The fifth mistake is expecting to land a job after learning these skills. The job market has become stressful, and it takes much more than just learning how to code to land a job.

Landing a job is a project itself, just like acquiring the necessary skills. For instance, let's say you're able to do regression analysis using Python, but when you go into a coding interview under time pressure, you might forget everything. To be truly successful in these job scenarios, you need to practice. Practice, practice, practice!

I like to break data science into three buckets: coding (which many spend a lot of time on), behavioral questions, and thirdly, the fundamentals of statistics and machine learning, where you will face many hypothetical scenario-based questions. So treat your job search as a project, fully dedicate time to build your portfolio and projects, and go into interviews well-prepared.

The sixth mistake to avoid is the tutorial trap. Watching tutorials is important, but it often seems easy when you’re just watching a video. However, applying what you learn in practice is what really counts.

When you finish a tutorial, don’t just move on right away. Spend time doing hands-on work to understand the concepts in more detail before advancing. The chances are that when you implement it yourself, you will encounter issues, and running into issues is how you learn more. So, focus on hands-on work to retain knowledge and avoid falling into the tutorial trap.

So these were the 10 key points I wanted to share. Is there anything else you would like to add? Let me know in the comments. If you liked this video, maybe I’ll see you in the next one. Have a great day!