10 Mistakes You Should Avoid as a Data Science Beginner 2024

10 Data Science Learning Mistakes You Must Avoid!

if you got to this Blog that means you have probably watched a lot of videos and Blog about data science road map and how you can become a data scientist and this Blog 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 working in the data science domain and especially becoming a data scientist in a non-traditional way this is the first thing.

In this blog, I write a total of 10 mistakes you should read and avoid making.

mistake 1:  Starting With Coding

that you should avoid in the 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 and I've seen many people make this mistake where they will jump into coding start learning python start learning SQL and they think like this is the right way to approach it okay maybe it is the right way to approach it if you are very new to coding and you have no background and you just want to see like if coding is something that you even enjoy because it's such a big part of data science but what data science actually is statistics and machine learning that's the core knowledge that a data scientist need to know and coding is a tool that how a data scientist will apply statistics and machine learning so I'm going to 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 start learning the fundamentals in statistics and machine learning and math and see if this is something that you enjoy doing because if you start doing that and you realize that you don't enjoy it then I would want you to stop learning coding languages such as python SQL & R are very powerful languages that you can learn as a data scientist but these are just tools to apply data science second and this is one of my biggest pet peeve and I'm going to say it a lot.

mistake 2: Data analytics to data scientist

Many people believe that they need to become data analysts before becoming data scientists.
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, and I think this is such a big misunderstanding that a lot of people have. They will jump right into data analytics and learn all the skills that you need to have as a data analyst, whether that is like data analysis, learning SQL, or building dashboards. yes data scientists and data analysts have a lot of Concepts that they have in common but data science is more than that for example as a data analyst you're going to be spending a lot of time building dashboards as a data scientist you will like likely not be using that skill set additionally as a data scientist you require a lot 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 Concepts in statistics and machine learning to solidify your knowledge you could have used that time to build projects and data science you could have also used that time to get experiences whether that's internships personal projects and whatnot so if your end goal is to become a data scientist please reconsider your decision to become a data analyst first yes there are many people who will transition from data data analyst to data scientist completely fine and it's okay for people who don't know that they want to become a data scientist but later they realize they want to become a data scientist totally fine It's easier to make the transition from data analyst to data scientist; of course, it requires a lot of work, but if your end goal is to become a data scientist, then start with that road map. One of my recent videos talks you through the entire data scientist road map, so definitely watch that. I'm going to link it somewhere here. Number three is the mistake that I personally made when

mistake 3: Data Scientist is your only option.

I started in the data science domain, and I jumped right into the data science role at that time. 
I did not know that there were so many other roles that existed, including machine learning engineer. 
AI engineer, data analyst, and data engineer. Although I did work as a data engineer for some time, 
That's a different story. There are many other roles that exist in the data science domain, including a 
recent one, AI product manager, which is a booming topic lately, okay? So what I'm trying to say is 
Don't be me. Don't be Sundas. If you want to get into data science, don't just jump into one. career right away. Try to understand what different careers do. What does a data analyst do? What does a machine learning engineers do Maybe there is a role that you will learn about and realize, like, It actually sounds more interesting to me, and that's what I want to pursue, so like, do your research. before deciding and jumping into one, picking one, and moving forward with it because it's going to take a lot of work, and you want to be sure that this is exactly what you want to do before you put In a lot of work, the fourth mistake that I see a lot of people make when trying to learn data 

mistake 4: not working backward

Science is not following a plan, and this goes to my first mistake, where you just jump right. into learning to code, sure, that's fine; it's great it's better than doing nothing, right? But if you If you want to become a data scientist, just make sure that you are going forward with a plan. create a  plan, create a road map, and one of the things that I would highly recommend is for you to work backward This is something that I personally have done in my personal projects in my interview. preps, and in my job search and whatnot, figure out your target company, your target role, If you want to work, research what a data scientist, say at meta, looks like. Look at the job description for somebody who works at Meta, then look at the job description for an open role at meta for data scientist role understand what the requirements are then go to LinkedIn and Find somebody who is working as a data scientist at Meta or has worked as a data scientist at Meta. Look at their projects. Look at their education and try to understand what type of work they do. kind of background they have; this will give you a really good understanding and will help you define your road map that you would need to follow in order to become a data scientist, so make sure When you're starting to learn data science, you go with a plan that will help you make sure that You stay on track, and you follow it. Put it on your calendar. Whatever you need to do, allocate whatever time you need to allocate over the weekend after work during the day for whatever you need to do, like make sure you are creating a plan and following it on the topic of creating a plan. I found this intro to Python ebook, which is basically a beginner guide to learning Python for data analysis, is created by HubSpot, which is also sponsoring this portion of the video. The ebook covers libraries. such as Pandas, Numpy, and Matplotlib, which are some of the essential libraries for analyzing data with In Python, it also walks you through basic ideas and gives you coding knowledge. The next thing that you should be avoiding while learning to become a data scientist is the fifth.

mistake 5: Tacking job search lightly

mistake that you should avoid is expecting to land a job after learning these things over the In the last few months and years, the job market has become what is the right word? The stressful job market has become stressful, and it takes a lot more than just learning how to code to land a job I like. to think of Landing a job is a project in itself, just like learning these skills is a project in Let's say you're able to do regression and analysis using Python, but you go into coding. interview, you're in a time-pressure setting, there is another person sitting in front of you, and It's possible that you might forget, so in order to truly be successful in these job interviews, scenarios, you need to practice practice practice practice. You need to practice it as much as you can, and I like to break data science into three buckets. One is coding, which a lot of people spend a lot of  of time on it's one of the buckets there's other elements The second is behavioral questions, and the third The third, which, in my opinion, is very important, is statistics and machine learning fundamentals, You will get a lot of hypothetical scenario-based questions, so make sure you're treating job search as a project and fully dedicating time to building your portfolio and the projects that you can speak about in your interviews and also going into the interview with a lot of preparation. 

 Mistake 6: Avoiding Generative AI

mistake that you should avoid is my favorite generative AI-generative AI is one of the most  transformative technology that we have seen over the last decade in tech data science and Beyond that, don't avoid it while you're learning these skills. Make sure that you're leveraging generative AI to your advantage whether that is helping generative AI teach you new concepts or working on a project and having Chadt WR basic code for you. I have done a few video formats on Chadt. How to use generative AI, specifically Chat GPT and Bard and other tools for coding and data analysis. You can watch it somewhere here, so take generative seriously and make it part of your curriculum, then the number eight mistake is moving beyond regression. 

 

mistake 7: Move Beyond Regression

and this is going to counter the first thing I mentioned: regression is one of the most important Concepts in data science: it is often that we spend a lot of time on these concepts on 07.320 learning regression, learning classification, and so on, but there's a lot more to data science than that in order to be a great data scientist, you need to have good domain knowledge and good business Understanding good PMing understanding PMing as in, like, product management and product development life cycle and good communication, so regression is important. Learning these fundamentals and Concepts are important, but how do you put them into the real world where you're going to be? Working with a lot of different stakeholders in different domains, how do you communicate? your findings, your results your insights, or how do you even understand what the business needs, so having a good understanding of all of these things around the data science umbrella will help you be successful in your career, so when you're defining your curriculum, make sure that you're incorporating these things into your learning plan. Ninth, math is important, but not. 

mistake 8: Math It is important, but not really.

Really, okay, this might be controversial, but the reason I say this math is definitely important But that also depends on what kind of data scientist you're going to be if you're going to be a data scientist who is going to be developing custom machine learning models. Math is your friend. and you should know math in and out, but if you are going to be a data scientist who is using machine learning models that are already built, for example, regression analysis classification model, and you're not building your own custom machine learning model, math is still important. but not as much, so understand where you fall on the spectrum of how much math you need to know. You don't fall into that trap, and finally, my last and biggest tip is: do not fall into the tutorial. 

                                               

mistake 9: falling into the tutorial trap

trap when I say tutorials. I basically mean you're watching a YouTube video or you're watching a video. on the course Yes, those are all important, and it is often that when you watch it, for example, 
Maybe you're watching this video and you're like, Yes, I get it. I get all 10 points, but in order to apply them in practice. It's way more difficult, similarly, when you're watching these tutorials. Let's say you're watching a tutorial in Python. It looks very easy. It looks very straightforward. finished that tutorial, and you were like, Yes, I got it, I can do it, but when you actually started  Applying these to a real project on a real data set on your computer, the chances are that you're going to run into issues, so this is called the tutorial trap because you watched the video. and you moved on, don't move on right away. Spend some time doing the hands-on work. the concept in more detail before you move on, because the chances are that when you do it yourself, you're going to run into issues. when you run into issues. You learn more by running into Issues are actually not bad, so that's why you need to do more hands-on work so it sticks with you. Knowledge sticks with you, and you're not becoming the target of the tutorial trap. Okay, so these were the 10 things that I wanted to mention. Is there anything else that you would like to share? 

 

mistake 10:  Coding too many algorithms from scratch.

Pick up general-purpose machine learning libraries, such as Scikit-Learn (Python) or Caret (R).
If you do code an algorithm from scratch, do so with the intention of learning instead of perfecting your implementation Let me know in comments, and if you like this article, maybe I'll see you in the next one. Have a great day, bye.