Common Mistakes Beginners Make in Data Science and How to Avoid Them

Prepared to venture into the data science cosmos? Then, it may feel like an infinite horizon of ML models, Python scripts, and a gargantuan (yes, absolutely gigantic) amount of data!

Feels exciting, right? Yes. It certainly is! But honestly, tripping up during your data science journey, particularly as a beginner, is much easier than you think!

So, whether a self-taught maverick or someone who has pursued a data science course in Pune for beginners, you are likely to commit some common mistakes.

Pursuing a course can minimize the chances of mistakes. However, knowing some of the most common ones can help you avoid them consciously. Let’s look at five of them.

Venturing into Modeling Without Understanding the Problem

As a data scientist, people will look up to you as a problem-solver. But what if you aren’t clear about the problem? You won’t be able to understand it, let alone resolve it!

Now, that’s what many beginners in data science do. They jump into modeling without comprehending the requirements, pain points, and the problem.

However, where exactly do novices go wrong? They rush to build models without grasping the business problem or mindset. It is like solving a jigsaw puzzle without looking at the picture you are expected to create. You may do something but that won’t be right.

So, how to avoid this? Simple. Begin with understanding the domain and ask the right questions. For instance, you must understand what problem you are trying to solve, the users who will benefit from or use the insights and what is the ideal success vision.

Remember, asking the right questions and seeking answers are foundational practices that will guide your model to a significant extent.

Neglecting Data Validation

Beginner data scientists usually feel happy when they create their first model to derive an output from it. It is because they’ve trained the model and it has started giving predictions. And they think they deserve a pat on the back! But without validating the predictions and checking if they even make sense?

That’s precisely the next mistake on this list. Imagine, a student giving a senseless answer to a question in the classroom. He responds to the question but his response is pointless! If the teacher doesn’t validate it, other students may consider it right and follow it.

Of course, data validation isn’t interesting at all.

But this is where the actual wonders take place and hence, that matters.

To avoid this, you must make data validation a part of your data science discipline. 

You must cross-validate, check confusion matrices, or even perform a sanity check to inspect the usefulness and accuracy of the predictions of the model.

Ignoring Data Cleaning and Preprocessing

Imagine beginning a heavy workout without warming yourself up! You may strain a particular muscle, injure yourself, or suffer some major damage.

That’s exactly what many new data scientists do on the data science playground.

They ignore basic steps like data cleaning, preprocessing, transforming, and comprehending every feature in the dataset.

Remember, a data science model is only as good as the data you train it on. 

Thus, a dataset that doesn’t undergo the basic processes will have poor data that can lead to flawed analysis and inaccurate assumptions.

Again, we understand data cleaning and preprocessing aren’t glamorous jobs. However, skipping these steps can lead to a messy model that can cause a major blunder.

As for you, you can prevent this in your case. How? Make it a habit to invest time handling missing values, encoding categorical data, removing outliers, and scaling features.

Learn the Core of Data Science – Enroll in Statistics for Business!

Clean and high-quality data is the foundation of a useful and trustworthy model. If you want to build one, ensuring clean and accurate data is one of the keys.

Focusing Only on Accuracy

We won’t blame data scientists here. The more accurate something is, the better. Since childhood, we’ve been taught this. So, often, all we do is chase accuracy! 

Of course, accuracy matters in data science. However, you cannot focus on it solely. 

As a data scientist, you would feel tempted to chase the highest accuracy score for your model. However, here, what most data scientists forget is they are preparing models for the real world where accuracy isn’t always the only or even the most crucial parameter.

As a result, they over-optimize their model for accuracy on a particular dataset without considering interpretability, business impact, generalizability, and the existing problem.

You can prevent this in your case with tips, including;

• Understanding the business context and being mindful of the real-world problem. Additionally, comprehending business goals, and addressing concerns like the implications of false positives and false negatives also helps.
• Considering different assessment metrics, including precision, AUC, recall, etc. You must select the one that best signifies and suits the project goals.
• Prioritizing interpretability as required and keeping generalization in mind.

Excluding Feature Engineering

As mentioned earlier, data scientists must not ignore basic steps like data cleaning, transformation, model selection, etc. However, in their pursuit, they often forget to perform feature engineering – a crucial step in data science.

Features are the inputs that drive the predictions of a model. The richer the features, the better the prediction. Similarly, poor features can result in suboptimal outcomes.

To avoid this, you must;

• Understand the data and the domain to identify impactful and suitable features.
• Build more features from the current ones.
• Work with domain experts to learn which features may be the most predictive.
• Conduct Sharp analysis to comprehend the more effective features.

Learn and Become a Prepared Data Scientist with Ethan’s Tech!

Data science is a welcoming spectrum of opportunities. 

However, performance matters here as much as it does everywhere else. Thus, the more you prepare while learning, the more sensibly you can work as a data scientist.

So, join Ethan’s Tech. We are a tech training center with a comprehensive data science course in Pune with placement. Our course helps students gain practical exposure, allows them to explore, commit mistakes, learn from them, and master the data science craft through experiences.

Our experienced faculty members share their real-world experiences to help you avoid the various common mistakes we discussed above and many beyond them as well.

Looking to visit our center in Wakad? Call us at +91 95133 92223 and book an appointment with our counselors to have a detailed discussion.