5 Common Mistakes to Avoid for Data Science Beginners

This is a guest post written by Author Austin Chia from AnyInstructor.com

Are you a data science beginner? If so, you’re probably excited to get started in the world of machine learning and predictive analytics. However, it’s important to avoid common mistakes that can set you back in your studies.

In this blog post, we will discuss 5 mistakes that beginners often make in data science and how to avoid them!

Let’s have a look at them.

What Are 5 Common Mistakes Made by Data Science Beginners?

Here are five common mistakes made by data science beginners:

  1. Not Asking for Help
  2. Trying to Do Everything on Your Own
  3. Focusing Only on Complex Data Science Topics
  4. Not Understanding the Business Question
  5. Thinking Too Much About The Theory

Now let me share about each of them deeper and how to avoid them!

1. Not Asking for Help

One of the most common mistakes that data science beginners make is not asking for help when they need it. It’s important to realize that nobody knows everything and that it’s okay to ask for help when needed.

I’m personally guilty of being too closed up to ask for help as well! In my work as a healthcare data analyst, I had to take on a data analytics project which I knew was too tough for me to complete at an entry level. I’d have to admit – I was full of pride and didn’t want to appear lousy in front of my colleagues.

This mistake cost me 3 weeks, which forced me to extend the project deadline. That’s all because I was taking too long to clean and prep all that data myself without asking for help. I could have avoided it by simply being humble and asking for help from the manager.

Here are some solutions to help you avoid this mistake I made:

  • Join an online community or forum so you can ask for help
  • Find a mentor to help guide and support you as you learn data science
  • Use online resources such as courses, tutorials, books, and blog posts to supplement your learning in data science
  • Ask your manager/supervisor/senior questions when needed so that you can clarify any confusion quickly


Another great way to get help is by working with a mentor. A mentor can provide guidance and support as you learn data science and machine learning. If you don’t have a mentor, you can try to find one through online forums or communities like LinkedIn or Kaggle.

Additionally, there are many online resources that can be helpful when learning data science. Some of these resources include online data science courses, YouTube tutorials, books, and blog posts. Don’t underestimate the power of such resources, they can be a real gem when you’re stuck on a problem you can’t solve.

Finally, don’t be afraid to ask questions! If you’re unsure about something, make sure to ask so that you can clarify any confusion. Don’t make the same mistake as me and get your problem clarified as soon as possible to get your project moving.

2. Trying to Do Everything on Your Own

Another common mistake that data science beginners often make is trying to do everything on their own. This can lead to working long hours, burning out quickly, and becoming overwhelmed.

I made this mistake when I first started my career in data science. I would work long hours trying to finish a project or meet a deadline all by myself. It was one of my first few jobs and I wanted to prove I had a good understanding of the essential data science skills by taking the project on my own.

However, this led to me burning out quickly and becoming overwhelmed.

I realized that I needed to take a step back and ask for help when needed. Additionally, I started delegating tasks to other members of my team so that I wouldn’t be overworked.

Here are some solutions you can try to avoid making this mistake:

  • Delegate tasks to other members of your team
  • Set realistic deadlines for yourself and your team
  • Break down large projects into smaller tasks
  • Take breaks when needed so that you don’t burn out


If you find yourself in a situation where you’re trying to do everything on your own, it’s important to take a step back. Start by delegating tasks to other members of your team. 

Of course, I had understanding colleagues who have been through the same thoughts as I did and gracefully assisted me with some tasks. Ask your team members nicely and they will be willing to help if you explain your situation!

Additionally, set realistic deadlines for yourself and your team so that you don’t become overwhelmed. Data science may sound simple when stakeholders request it but you’ll be surprised how time-consuming the data validation phase actually takes!

Another way to avoid becoming overwhelmed is by breaking down large projects into smaller tasks. This will help you focus on one thing at a time and not feel as overwhelmed. This is a crucial tip for those who work in a small team and can’t delegate tasks.

What happens if you’re the only data scientist, in a small team? I’d recommend chopping up your project tasks into bite sizes and tackling them one at a time. Make sure to take breaks when tackling such tasks so that you don’t burn out too.

By following these solutions, you’ll be able to avoid making the mistake of trying to do everything on your own.

3. Focusing Only on Complex Data Science Topics

Another mistake that data science beginners make is focusing only on complex topics. This can lead to you chasing after what seems to be “cool” to do in a data science project that does not actually provide business/research value.

If you’re only chasing after projects on complex data science topics like deep learning, neural networks, and optimizing parameters right out of the bat, chances are, you’ll not be solving the main problem.

I made this mistake when I was first starting out in data science too. I would read all about the most complex topics from machine learning journals without understanding the basics. 

Going through all the technical complexities of machine learning and hyperparameter tuning led to many mismatched expectations of the data science job.

In fact, according to Anaconda’s 2021 State of Data Science survey, data scientists spend 39% of their time on data preparation and cleaning instead of model training and deployment! 

This means that understanding the basics of handling and cleaning data should come before all the complex model training.

Here are some solutions you can try to avoid making this mistake:

  • Start with the basics
  • Work on a variety of projects to gain experience
  • Consult with more experienced data scientists


The first solution is to start with the basics. Make sure you understand the basics of data science before moving on to complex topics. This includes understanding data cleaning, data transformation, statistics, probability, data visualization, linear algebra, and calculus. 

Good ways to begin this include online certifications, YouTube videos, boot camps, and data blogs.

The next solution is to work on a variety of projects to gain experience. By working on different types of projects, you’ll be able to learn different techniques and find out what works best for you.

Lastly, consult with more experienced data scientists. By talking to those who have been in the field for a while, you’ll be able to get insights into what topics are worth learning at the start.

By following these solutions, you’ll be able to focus on the right basic topics first and avoid becoming overwhelmed by complex topics.

4. Not Understanding the Business Question

Data science helps businesses answer questions or solve problems. As a data scientist, it is important that you understand the business question that you are trying to answer.

If you do not understand the business question, you will not be able to provide valuable insights from the data. This can lead to frustration from both you and your clients/stakeholders.

Although I did not personally encounter this mistake, it was a common point mentioned in an online certification I took: the Google Data Analytics Professional Certificate

I learned how there’s always a mismatch between the business and the data teams, mainly caused by a lack of business understanding by data scientists and data analysts.

Here are some solutions you can try:

  • Take some time to think critically about the business need
  • Ask stakeholders for clarification


To avoid this mistake, make sure to take the time to understand the business question before starting any data analysis. This includes understanding the context of the problem, what is needed to be done, and what are the deliverables.

If you’re still unsure about the business question, don’t hesitate to ask for clarification. By doing so, you’ll be able to avoid wasting time on data analysis that does not actually answer the question. 

Give them a chance and hear their problems and pain points before jumping right into your favorite data science IDE to code out your next machine learning model.

By taking the time to understand the business question, you’ll be able to provide valuable insights that can help solve the problem.

5. Thinking Too Much About The Theory

One of the mistakes that data science beginners make is thinking too much about the theory. While it is important to understand the theoretical concepts behind data science, it is also important to know when to apply them.

If you spend too much time understanding the theory, you might not have enough time to actually put your knowledge into practice. This can lead to a lack of experience and, as a result, make it difficult to find a job.

This mistake was made by me unknowingly too, while I was learning and preparing for my data science career. I was way too focused on getting to know about the theory of how the algorithms and models worked, without actually running them in my IDE in Python code on my laptop.

Now, don’t get me wrong – having good knowledge of data science theory is still very important! It is the foundation on which you will be able to build your practical skills. But, if you want to become a data scientist, you’re going to want to build up both theoretical and practical knowledge.

And with that, I decided to actually work on projects that could get me practical experience. These projects also make a good addition to present at data science interviews!

Here are some solutions you can try:

  • Focus on understanding the concepts that are most relevant to your project
  • Work on projects that interest you


The first solution is to focus on understanding the concepts that are most relevant to your project. This way, you’ll only learn the theories that are actually useful for your project. You’ll be able to get better in both the practical and theoretical aspects.

Secondly, work on projects that interest you. By working on projects that you’re passionate about, you’ll be more motivated to learn the theories behind them. Explore unique projects that you are naturally curious about and collect data from there. 

This will give you exposure to data prep, cleaning, and modeling practical work – curing all your imposter syndrome woes!

Project Examples to try:

  • Financial stock prediction
  • Hospital bed optimization for healthcare analytics
  • Text analytics for social media

Wrapping Up

By following these solutions, you’ll be able to find a balance between theory and practice. This way, you can gain the knowledge and experience necessary to be a successful data scientist.

These are five common mistakes to avoid for data science beginners. By following the solutions in this blog post, you’ll be able to focus on the right topics and gain the experience necessary to succeed in a data scientist career.

Thanks for reading!

Austin Chia Guest Bio

Author Bio

Austin Chia writes about tech, analytics, and software at AnyInstructor.com. After breaking into data science without a degree, he seeks to help others learn more about the data science and analytics field through content. He has previously worked as a data scientist at a healthcare research institute and a data analyst at a health-tech startup.

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