Four key mistakes to steer clear of during Data Exploration Analysis
In the realm of data science, Exploratory Data Analysis (EDA) plays a crucial role in understanding data before delving into machine learning modeling. This process involves discovering trends and patterns in data using graphical representations and summary statistics [1]. Here are some key tips to ensure that your EDA yields actionable insights and avoids shallow or misleading findings.
Define Clear Objectives
To focus your analysis and select appropriate methods, it's essential to define clear objectives upfront [3]. This step ensures that your EDA aligns with the project's goals and provides valuable insights.
Collaborate with Domain Experts
Leveraging collaboration with domain experts helps gain a deeper contextual understanding of the data [3]. This collaboration can provide insights that might otherwise be overlooked, leading to richer, more meaningful results.
Use a Combination of Techniques
To thoroughly explore data patterns and quality issues, consider using a combination of traditional statistical and modern automated techniques [3]. This approach ensures that you're not missing any crucial aspects of your data.
Thorough Data Cleaning and Validation
Ensure the dataset is consistent and accurate by conducting thorough data cleaning and validation [2][3]. This step prevents misleading findings caused by errors or inconsistencies.
Visualize Data Early and Often
Visualize data early and often using simple plots like histograms and line charts to detect outliers, trends, or anomalies during exploration rather than after analysis [4].
Powerful Data Aggregation and Reshaping
Use powerful data aggregation and reshaping functions like groupby(), agg(), and pivot_table() to reveal meaningful summary statistics and relationships within the data [4].
Document Every Step
Document every step taken to enable reproducibility and error tracing, ensuring that insights are well-supported and can be reviewed or refined by others [1][3].
Seek Feedback
Seek feedback from colleagues or experts to identify potential blind spots and validate interpretations for richer insights [1].
By following these structured and collaborative exploration methods, while staying current with advanced tools and automating repetitive tasks, you can improve the depth and reliability of insights gleaned from EDA [1][3].
However, it's important to be aware of other pitfalls in EDA. Shallow insights can occur when what seems like a great insight to the data practitioner may be obvious to the stakeholders [1]. To avoid this, it's essential to get feedback early from stakeholders, clarify requirements as soon as possible, and treat EDA as an iterative process requiring frequent cycling back to stakeholders [1].
Moreover, data practitioners may arrive at wrong conclusions due to a lack of domain knowledge, treating correlation as causation, and ignoring confounding variables [1]. To avoid such errors, expanding your knowledge of the business area, sharpening your statistics skills, and consulting with business stakeholders during analysis are all valuable practices [1].
Lastly, bad visualizations can lead to misinterpretations. Common issues include bad choice of graphs, misleading axis scale, using too many colors, insensitivity to colorblind people, and displaying wrong units [1]. To create effective visualizations, it's essential to understand the data and the audience, and to use clear, straightforward representations.
[1] Data Science Central [2] Towards Data Science [3] Analytics Vidhya [4] DataCamp
Data-and-cloud-computing technology can aid in conducting Exploratory Data Analysis by offering powerful data aggregation and reshaping functions, enabling efficient data exploration [4]. By collaborating with technology experts, deeper insights about the data infrastructure can influence the EDA process, providing richer results [3].
