5 Common Data Science Mistakes and How to Avoid Them

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Have you ever wondered why your data science project seems disorganized or why the results are worse than a baseline model? It’s likely that you are making 5 common, yet significant, mistakes. Fortunately, these can be easily avoided with a structured approach. 

In this blog, I will discuss five common mistakes made by data scientists and provide solutions to overcome them. It’s all about recognizing these pitfalls and actively working to address them.

1. Rushing into Projects Without Clear Objectives

If you are given a dataset and your manager asks you to perform data analysis, what would you do? Usually, people forget the business objective or what we are trying to achieve by analyzing the data and directly jump into using Python packages to visualize the data and make sense of it. This can lead to wasted resources and inconclusive results. Without clear goals, it is easy to get lost in the data and miss the insights that truly matter.

How to Avoid This:

• Start by clearly defining the problem you want to solve.
• Engage with stakeholders/clients to understand their needs and expectations.
• Develop a project plan that outlines the objectives, scope, and deliverables.

2. Overlooking the Basics

Neglecting foundational steps like data cleaning, transforming, and understanding every feature in the dataset can lead to flawed analysis and inaccurate assumptions. Most data scientists don’t even understand statistical formulas and just use Python code to perform exploratory data analysis. This is the wrong approach. You need to pick what statistical method you want to use for the specific use case. 

How to Avoid This:

• Invest time in mastering the basics of data science, including statistics, data cleaning, and exploratory data analysis.
• Stay updated by reading online resources and working on practical projects to build a strong foundation.
• Download the cheat sheet on various data science topics and read them regularly to ensure your skills remain sharp and relevant.

3. Choosing the Wrong Visualizations

Does picking a complex data visualization chart or adding color or description matter? No. If your data visualization does not communicate the information properly, then it is useless, and sometimes it can mislead stakeholders.

How to Avoid This:

• Understand the strengths and weaknesses of different visualization types.
• Choose visualizations that best represent the data and the story you want to tell.
• Use various tools like Seaborn, Plotly, and Matplotlib to add details, animation, and interactive viz and determine the best and most effective way to communicate your findings.

4. Lack of Feature Engineering

When building the model data, scientists will focus on data cleaning, transformation, model selection, and ensembling. They will forget to perform the most important step: feature engineering. Features are the inputs that drive model predictions, and poorly chosen features can lead to suboptimal results. 

How to Avoid This:

• Create more features from already existing features or drop low-impact full features using various feature selection methods. 
• Spend time understanding the data and the domain to identify meaningful features.
• Collaborate with domain experts to gain insights into which features might be most predictive, or perform Shap analysis to understand which features have more impact on a certain model.

5. Focusing More on Accuracy Than Model Performance

Prioritizing accuracy over other performance metrics can lead to biased models that perform poorly in production environments. High accuracy does not always equate to a good model, especially if it overfits the data or performs well on major labels but poorly on minor ones. 

How to Avoid This:

• Evaluate models using a variety of metrics, such as precision, recall, F1-score, and AUC-ROC, depending on the problem context.
• Engage with stakeholders to understand which metrics are most important for the business context.

Conclusion

These are some of the common mistakes that a data science team makes from time to time. These mistakes cannot be ignored. 

If you want to keep your job in the company, I highly suggest improving your workflow and learning the structured approach of dealing with any data science problems. 

In this blog, we have learned about 5 mistakes that data scientists make on a regular basis and I have provided solutions to these problems. Most problems occur due to a lack of knowledge, skills, and structural issues in the project. If you can work on it, I am sure you will become a senior data scientist in no time.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.