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Have you ever wondered why your data science project seems disorganized or why your results are worse than your baseline model? You’re probably making 5 common but significant mistakes. Fortunately, they’re effortless to avoid with a structured approach.
In this blog, I’ll discuss five common mistakes data scientists make and provide solutions to overcome them. It’s about recognizing these pitfalls and actively working to fix them.
1. Taking on projects without clear goals is a rash decision.
If you are given a dataset and your manager asks you to do data analysis, what will you do? People tend to forget about the business goal or what we are trying to achieve by analyzing the data and jump straight to using Python packages to visualize the data and make sense of it. This can lead to wasted resources and ambiguous results. Without clear goals, it is effortless to get lost in the data and miss the insights that really matter.
How to avoid it:
- Start by clearly defining the problem you want to solve.
- Collaborate with stakeholders/customers to understand their needs and expectations.
- Develop a project plan that outlines goals, scope, and deliverables.
2. Skipping the basics
Neglecting basic steps like data cleaning, transformation, and understanding every feature in the dataset can lead to flawed analysis and incorrect assumptions. Most data scientists don’t even understand statistical formulas and just employ Python code to perform exploratory data analysis. This is the wrong approach. You need to choose the statistical method you want to employ for your specific employ case.
How to avoid it:
- Take the time to master the fundamentals of data science, including statistics, data cleaning, and exploratory data analysis.
- Stay current by using online resources and working on practical projects to build a solid foundation.
- Download this cheat sheet on various data science topics and read it regularly to ensure your skills stay advanced and up to date.
3. Choosing the wrong visualizations
Does choosing a convoluted data visualization chart or adding color or description make a difference? No. If a data visualization does not convey information correctly, it is useless and can sometimes mislead stakeholders.
How to avoid it:
- Learn the strengths and weaknesses of different types of visualizations.
- Choose visualizations that best represent your data and the story you want to tell.
- Operate a variety of tools such as Seaborn, Plotly, and Matplotlib to add detail, animations, and interactive visualizations, then determine the best and most effective way to communicate your findings.
4. Lack of feature engineering
When building a model, data scientists focus on data cleaning, transformation, model selection, and ensemble. They forget to do the most essential step: feature engineering. Features are the inputs that drive the model’s predictions, and poorly chosen features can lead to suboptimal results.
How to avoid it:
- Create fresh features based on existing features or remove low-impact features using different feature selection methods.
- Take time to understand the data and domain to identify essential features.
- Collaborate with subject matter experts to learn which features may be most predictive, or run a Shap analysis to understand which features have a greater impact on a particular model.
5. Focus 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 equal a good model, especially if it overfits the data or performs well on major labels but poorly on minor ones.
How to avoid it:
- Evaluate models using different metrics such as precision, recall, F1 score, and AUC-ROC, depending on the problem context.
- Collaborate with stakeholders to understand which metrics are most essential in the business context.
Application
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 at an enterprise, I highly recommend streamlining your workflow and learning a structured approach to solving data science problems.
In this blog, we have come across 5 mistakes that data scientists make regularly and provided solutions to these problems. Most of the problems are due to lack of knowledge, skills and structural issues in the project. If you can work on this, I am sure you will become a senior data scientist in no time.
Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. He currently focuses on content creation and writing technical blogs on machine learning and data science technologies. Abid has a Masters in Technology Management and a Bachelors in Telecommunication Engineering. His vision is to build an AI product using Graph Neural Network for students struggling with mental illness.