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Overview

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 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. Originally developed by American mathematician John Tukey in the 1970s, EDA techniques continue to be a widely used method in the data discovery process today.

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, 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. Once EDA is complete and insights are drawn, its features can then be used for more sophisticated data analysis or modeling, including machine learning.

Exploratory Data Analysis Tools

Some of the most common data science tools used to create an EDA include:

  • Python: an interpreted, object-oriented programming language with dynamic semantics. Its high-level, built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for rapid application development, as well as for use as a scripting or glue language to connect existing components together. Python and EDA can be used together to identify missing values in a data set, which is important so you can decide how to handle missing values for machine learning.

  • R: an open-source programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians in data science in developing statistical observations and data analysis.

What is covered in this course?

This course will teach you the techniques and approaches in exploratory data analysis, which will help you to derive maximum value from the data. If you jump into machine learning without doing this EDA, you are setting yourself up for failure besides ending up with lower accuracy. This course is designed by an AI and tech veteran and comes to you straight from the oven!

Expected Outcomes

  1. You will learn everything you need to know about exploratory data analysis, a method used to analyze and summarize data sets.
  2. You will gain insights on what should be done with your data set before beginning the work on building ML models
  3. You will implement data cleaning and validation tasks to get your data ready for data mining activities
  4. You will learn to estimate parameters and figure the margins of error
  5. You can understand the structure and content of your data
  6. You will learn to identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, find interesting relations among the variables.
  7. You will learn to determine if the statistical techniques you are considering for data analysis are appropriate.