{"id":92544,"date":"2022-07-27T06:25:31","date_gmt":"2022-07-27T06:25:31","guid":{"rendered":"https:\/\/www.fita.in\/?p=92544"},"modified":"2023-11-21T04:52:25","modified_gmt":"2023-11-21T04:52:25","slug":"what-is-exploratory-data-analysis-in-data-science","status":"publish","type":"post","link":"https:\/\/www.fita.in\/what-is-exploratory-data-analysis-in-data-science\/","title":{"rendered":"What is Exploratory Data Analysis in Data Science"},"content":{"rendered":"

Exploratory data analysis (EDA) is a data science technique that helps researchers identify potential insights and patterns in their data. This Exploratory data analysis can be used for a variety of purposes, such as exploring relationships between variables or investigating the effects of a change on a specific set of data.\u00a0<\/span><\/p>\r\n

The key to successful EDA is using a systematic approach and tracking all the steps in your analysis.<\/span><\/p>\r\n

The term \u201cexploratory analysis\u201d refers to methods used to analyze data without having a specific hypothesis in mind. Often this type of analysis is referred to as exploratory because it gives you the flexibility to look at data without knowing exactly how it is going to be analyzed. For example, if you want to find out whether there are differences between men and women, you could compare the ages of people in each gender group. If you don\u2019t know about any potential differences, you\u2019ll have no way of knowing if you\u2019re seeing something significant or just random noise.<\/span><\/p>\r\n

In contrast, a hypothesis-driven approach involves identifying a particular question you\u2019d like to answer and gathering evidence to support or refute that idea. Once you\u2019ve identified a problem, you can use statistical tests to see if there are statistically significant differences between groups \u2014 meaning that the difference is likely due to chance rather than being caused by some factor related to the variable under study.<\/span><\/p>\r\n

If you\u2019re interested in learning more about Data Analysis, check out our <\/span>Data Science Course in Chennai<\/b><\/a> for an overview of the topic. <\/span>FITA Academy<\/b><\/a> helps you to build your skills in data science and analytics.<\/span><\/p>\r\n\r\n

Exactly what is exploratory data analysis?<\/strong><\/h2>\r\n

The idea of exploratory data analysis, or EDA as it is commonly referred to, is not new. In fact, it was first proposed by James Wilder Tuley in 1977. But, despite being around for over 40 years, EDA hasn\u2019t really taken off. This is mainly because of how different it is from traditional data analysis.<\/span><\/p>\r\n

EDA primarily starts with an objective or a particular business goal. Analysts then use the collected data to reach conclusions that support the business goals. For example, if you\u2019re trying to determine whether or not a certain product is selling well, you\u2019ll look at sales numbers to see what products are doing better than others.<\/span><\/p>\r\n

In contrast to traditional data analysis, where the analyst tries to answer questions like \u201cwhat does this mean?\u201d or \u201cwhy did this happen?\u201d, EDA focuses on answering questions like \u201chow do we interpret this data?\u201d or \u201cwhat conclusion can we draw from this?\u201d.<\/span><\/p>\r\n

When you gain knowledge through expert guidance with our <\/span>Data science Tutorial<\/b><\/a>,<\/span> you can obtain confidence in tackling complex situations by applying data science skills.<\/span><\/p>\r\n\r\n

Data Analysis Types<\/strong><\/h2>\r\n

Studies require data analysis as one of their most important components. You want to make sure that you do it correctly. If you don\u2019t analyze your data properly, you could end up getting incorrect conclusions. This is why there are different kinds of data analysis. These include univariate, bivariate and multivariate analysis. Taking a look at each type of data analysis will help us better understand it.<\/span><\/p>\r\n

1) Univariate- <\/b>Univariate analysis is the simplest type of data analysis. It involves analyzing data that has only a single independent variable. For example, you might want to know how many people visited your site during each month of the year. Or, perhaps you\u2019re interested in finding out what percentage of visitors come from mobile devices versus desktop computers. In both cases, you\u2019d use univariate analysis.<\/span><\/p>\r\n

A histogram is the most common way to visualize data collected via univariate analysis. A histogram displays the frequency distribution of values associated with your data set. You\u2019ll see examples of histograms throughout this course.<\/span><\/p>\r\n

2) Bivariate –<\/b> A scatter plot is a visual representation of data points where each data point represents a single observation. Bivariate analysis is performed by plotting the values of two variables against each other. In the case of a scatter plot, the value of one variable is plotted along the horizontal axis while the value of the second variable is plotted along the vertical axis. This allows you to see how the two variables correlate with each other.<\/span><\/p>\r\n

The most common type of bivariate analysis is the Pearson correlation coefficient. However, there are many others, such as Spearman rank correlation or Kendall tau correlation.<\/span><\/p>\r\n

3) Multivariate Analysis –<\/b> Multivariate analysis involves analyzing the relationships among several variables simultaneously. When you want to know how different factors affect one another, it makes sense to look at the entire picture at once. For example, imagine a person with a high IQ score, low body weight, and high blood pressure. You might wonder whether his high IQ score causes his low body weight or vice versa. A multivariate analysis helps answer such questions.<\/span><\/p>\r\n

The multivariate analysis includes looking at three variables at once. Suppose you wanted to see how well students in a class performed on exams compared to their grade point average (GPA). In this case, a Multivariate analysis would include both grades and exam scores.<\/span><\/p>\r\n

From the above Data Analysis Types, you can see that there are multiple combinations. From our <\/span>Data Science Online Course<\/b><\/a>, you can get a better understanding of each of these types and their importance.<\/span><\/p>\r\n\r\n

Key Components in Exploratory Data Analysis<\/strong><\/h2>\r\n

Exploratory data analysis (EDA) is a method used to analyze large amounts of data. In this process, you\u2019ll use statistical methods to discover patterns within the data. This helps you understand what information is most important to your audience. You might want to find out how many people like each type of product, where customers live, whether certain products sell better during different seasons, etc. There are several key components involved:<\/span><\/p>\r\n\r\n

An understanding of variables<\/b><\/h3>\r\n

Almost all data sets contain variables. A variable is anything that affects something else. Think about variables like the color of a shirt, the size of a room, or the speed of a train. Each of those things affects another thing. In this case, the \u201cthing affected\u201d could be the price of a house, the number of people living there, or the amount of money spent on groceries. These are examples of variables.<\/span><\/p>\r\n

The importance of variables varies depending on what type of analysis you want to do. If you want to find out whether certain types of houses sell faster than others, then you\u2019ll likely use variables such as square footage, age, and neighborhood. But if you want to know why some companies earn more profit than others, you\u2019ll probably look at variables such as sales volume, revenue per employee, and customer satisfaction scores.<\/span><\/p>\r\n\r\n

Dataset cleanup\u00a0<\/b><\/h3>\r\n