Exploratory Data Analysis In Data Science & AI
Exploratory Data Analysis In Data Science & AI
Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,
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What you'll learn
- Exploratory Data Analysis Mindmap and Concept, Theory as well as Practical
- EDA in Machine Learning
- EDA in Data Science & AI
- Hands on tutorial on industry relevant skills and also the theory and concepts clarity
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Without a proper EDA, Machine Learning work suffers from accuracy issues and many times, the algorithms won't work. What is exploratory data analysis? Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods.
Setting the context
Before you start a machine learning project, it's important to ensure that the data is ready for modeling work. Exploratory Data Analysis (EDA) ensures the readiness of the data for Machine Learning. EDA ensures that the data is more usable. Without a proper EDA, Machine Learning work suffers from accuracy issues and many times, the algorithms won't work.
What is exploratory data analysis?
Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions.
EDA is primarily used to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate.
Why is exploratory data analysis important in data science?
The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, and find interesting relations among the variables.
Data scientists can use exploratory analysis to ensure the results they produce are valid and applicable to any desired business outcomes and goals. EDA also helps stakeholders by confirming they are asking the right questions. EDA can help answer questions about standard deviations, categorical variables, and confidence intervals.
