Neglecting Exploratory Data Analysis
In the field of data science, Exploratory Data Analysis helps us to understand data better before making assumptions and decisions. It also helps in identifying hidden patterns within the data, detecting outliers, and also to find the relationship among the variables. Neglecting EDA may miss out on important insights, which makes our analysis misguided. 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.
Causes of Neglecting Exploratory Data Analysis
Example: Not identifying customer purchase patterns. Let’s consider the analysis of customer purchase data from an online store. The goal is to identify trends and optimize marketing strategies. Imagine that no EDA has been performed. This may miss out on seasonal trends of the product, customer demographic patterns, etc. Consequently, this will lead to suboptimal marketing strategies, and missed opportunities for increased sales.
Key Aspects of Exploratory Data Analysis
- Data Visualization: Data Visualization is the art of presenting data in the form of graphs and other visuals. They are used to represent complex information in a more accessible and understandable manner. The common types of visualizations are Histograms, Scatter plots, and box plots.
- Descriptive Statistics: It gives a concise summary of key features and characteristics of a dataset. Central Tendency helps to understand the average behavior of the dataset. Measures like range, variance, and standard deviation help to measure the dispersion of data. Skewness and kurtosis offer insights into the shape and symmetry of the data distribution. The correlation coefficient measures the strength and direction of the relationship between two variables.
- Pattern Recognition: Pattern Recognition identifies meaningful relationships, trends, and structures within the data. Using EDA we can uncover recurring shapes, behaviors, and arrangements of data points. This helps in identifying underlying patterns and trends. Patterns such as seasonal patterns, cyclic patterns, trends, time series patterns, and spatial arrangements can be captured by EDA.
- Hypothesis Generation: Hypothesis Generation in EDA refers to the initial assumptions and guesses about relationships, patterns, and trends. It proposes potential explanations for observed phenomena, leading to further investigation—identified patterns, correlation, spatial arrangements, and outlier detection help to justify the assumption.
Practical Tips
- By using the data visualization tools like Matplotlib and Seaborn to quickly explore and visualize key patterns in the data.
- Incorporate hypothesis generation during EDA to guide subsequent analyses and model building.
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
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