Common Mistakes to Avoid in Data Science Code
What is Data science?
Data science is a field that involves extracting meaningful insights from the large set of data given. Various algorithms and techniques can be used to extract hidden information.
What is Exploratory Data Analysis?
EDA is the first step in data analysis. To understand the data better Analysts and data scientists generate summary statistics, create visualizations, and check for patterns. EDA aims to gain insights from the underlying structure, relationships, and distributions of the variable.
What is Bias-Variance Trade-Off?
It is the balance between model simplicity(bias) and flexibility(variance). Lower bias and higher variance lead to model overfitting. Higher bias and lower variance will make the model underfit.
How does cross-validation help in data science?
Cross-validation techniques such as k-fold cross-validation will help in detecting and solving overfitting problems. It does it by evaluating the model on multiple subsets of data and provides a more robust method for generalization.
What are inline comments in documentation?
Inline comments are like a little message the developer can include in the code. The inline comments provide extra information, context, or explanation wherever needed. Inline comments should be in plain language and they should give the descriptions in a human-friendly manner. It provides clarifications to the tricky parts of the code. We can also include reminders for future modifications or enhancements.
6 Common Mistakes to Avoid in Data Science Code
As we know Data Science is a powerful field that extracts meaningful insights from vast data. It is our job to discover hidden secrets from the available data. Well, that is what data science is. In this world, we use computers to solve problems and bring out hidden insights. When we enter into such a big journey, there are certain things we should watch out for. Those who like playing with data know the tricky part of understanding the data and the possibility of making mistakes during the data processing.
How can I avoid mistakes in my Data Science Code?
How can I write my Data Science code more efficiently?
To answer all your questions, In this article, you get to know Six common mistakes to avoid in data science code in detail.
Table of Content
- Ignoring Data Cleaning
- Neglecting Exploratory Data Analysis
- Ignoring Feature Scaling
- Using default Hyperparameters
- Overfitting the Model
- Not documenting the code
- Conclusion