Do you ever wonder how scientists and researchers learn about the world around us? One way they do it is by collecting data and analyzing it to look for patterns and relationships. This process is called Exploratory Data Analysis, or EDA for short.
Exploratory Data Analysis (EDA) is a statistical technique that is used to analyze and summarize datasets. EDA is a way to explore data and try to make sense of it. It involves looking at the data from different angles, using graphs and charts to visualize it, and trying to identify any patterns or trends that might exist. The goal of EDA is to help researchers better understand the data and use it to make informed decisions.
Read more on “What is Data Analytics?“
Why is Exploratory Data Analysis Important?
Well, there are a few reasons. First, EDA can help researchers identify any problems with the data. For example, they might find missing or incomplete data, or they might notice that some data points are outliers (meaning they are very different from the rest of the data). By identifying these problems early on, researchers can correct them or account for them in their analysis.
Second, EDA can help researchers generate hypotheses about the data. For example, if they notice a pattern in the data, they might come up with a theory about why that pattern exists. They can then test that theory using other statistical methods.
Finally, EDA can help researchers communicate their findings to others. By visualizing the data in different ways, they can create graphs and charts that are easy to understand and that help to tell a story about the data.
How does Exploratory Data Analysis Work?
Well, it usually starts with a research question or hypothesis. For example, a researcher might be interested in whether there is a relationship between how much time a student spends studying and their grades in school. They might collect data on the number of hours each student studies per week and their grades in different subjects.
Next, the researcher would use EDA to explore the data. They might create a scatterplot, which is a type of graph that shows the relationship between two variables. In this case, they would create a scatterplot with the number of hours studied on the x-axis and the grades on the y-axis.
The scatterplot might show a pattern – for example, students who study more tend to have higher grades. The researcher might also calculate a correlation coefficient, which is a number that measures the strength and direction of the relationship between two variables. If the correlation coefficient is high (meaning there is a strong relationship), the researcher might conclude that there is a positive relationship between studying and grades.
Of course, EDA can be more complicated than this, and researchers might use a variety of different tools and techniques to explore the data. But the basic idea is to look at the data in different ways and try to make sense of it.
The Main Objectives of Exploratory Data Analysis
EDA involves exploring and visualizing data to gain an understanding of the patterns, trends, and relationships that exist within the data.
The main objectives of EDA are to:
Understand the data: EDA helps to identify the main features, patterns, and trends in the data. This includes examining the distribution of the data, identifying outliers, and understanding the relationships between variables.
Check assumptions: EDA is used to check whether the data meets the assumptions of statistical models that are being used for further analysis.
Identify potential problems: EDA is used to identify potential problems in the data, such as missing values, inconsistencies, or errors.
Generate hypotheses: EDA can be used to generate hypotheses or test theories about the data.
EDA typically involves the use of various statistical and graphical tools to analyze the data. Commonly used graphical tools include scatter plots, histograms, box plots, and heat maps. Statistical tools used in EDA include measures of central tendency, such as the mean and median, measures of dispersion, such as the standard deviation and range, and correlation coefficients.
EDA is a critical first step in any data analysis project. By gaining an understanding of the data through EDA, researchers can identify potential problems, generate hypotheses, and design appropriate statistical models for further analysis.
Conclusion
In conclusion, Exploratory Data Analysis is a powerful tool for researchers and scientists who want to learn more about the world around us. By exploring data and looking for patterns and relationships, researchers can generate new ideas and insights that help to advance our understanding of the world. So the next time you hear about a new scientific study or research project, remember that it all starts with data and the process of EDA!
If you’re based in India and interested in pursuing a career as a data analyst, there are many educational programs and resources available to help you get started. One option is the “Coding Invaders Data Analyst” course, which is designed to provide students with a strong foundation in data analysis skills and tools. This course covers a wide range of topics, including data cleaning and preparation, data visualization, and statistical analysis. Additionally, the course provides students with real-world case studies and projects to help them gain practical experience. One of the best parts of this course is the placement support it offers to students. After successfully completing the course, students will receive job placement support from the Coding Invaders team, which can be extremely helpful in jump-starting your career in data analysis.
