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Have you ever wondered why your data science project seems disorganized or why the results are worse than a reference model? Chances are you're making 5 common, but important mistakes. Fortunately, these can be easily avoided with a structured approach.
In this blog, I will discuss five common mistakes data scientists make and provide solutions to overcome them. It’s all about recognizing these obstacles and actively working to address them.
1. Tackling projects without clear objectives
If you were given a dataset and your manager asked you to perform data analysis, what would you do? Typically, people forget the business objective 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 inconclusive results. Without clear objectives, it is easy to get lost in the data and miss out on the insights that really matter.
How to avoid this:
- Start by clearly defining the problem you want to solve.
- Interact with stakeholders/customers to understand their needs and expectations.
- Develop a project plan that describes objectives, scope, and deliverables.
2. Overlooking the basics
Neglecting fundamental steps like data cleaning, transformation, and understanding every feature of 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 choose which 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 cleansing, and exploratory data analysis.
- Stay up to date by reading online resources and working on hands-on projects to build a solid foundation.
- Download the cheat sheet on various data science topics and read it regularly to ensure your skills stay up-to-date and relevant.
3. Choosing the wrong visualizations
Does it matter whether you choose a complex data visualization chart or add color or description? No. If the data visualization doesn't communicate information properly, it's useless and can sometimes confuse stakeholders.
How to avoid this:
- Understand the strengths and weaknesses of different types of visualization.
- Choose the visualizations that best represent the data and the story you want to tell.
- Use various tools such as Seaborn, Plotly, and Matplotlib to add detail, animation, and interactive visualization and determine the best and most effective way to communicate your findings.
4. Lack of feature engineering
When creating model data, scientists will focus on data cleaning, transformation, model selection, and ensemble. They will forget to perform the most important step: feature engineering. Features are the input data that drives model predictions, and poorly chosen features can lead to suboptimal results.
How to avoid this:
- Create more features from existing features or remove entire low-impact 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 the most impact on a given 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 equate to a good model, especially if it overfits the data or performs well on primary labels but poorly on secondary labels.
How to avoid this:
- Evaluate models using a variety of metrics, such as precision, recall, F1 score, and AUC-ROC, depending on the context of the problem.
- Engage with stakeholders to understand which metrics are most important to the business context.
Conclusion
These are some of the most 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 recommend you improve your workflow and learn the structured approach to tackle any data science problem.
In this blog, we have learned about 5 mistakes that data scientists commonly make and I have provided solutions to these problems. Most of the problems occur due to 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 who loves building machine learning models. Currently, he focuses 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 create an ai product using a graph neural network for students struggling with mental illness.