Skip to main content

Exploratory data analysis

 Exploratory data analysis (EDA) is an important technique in data analysis that involves examining and summarizing data in order to identify patterns, trends, and relationships between variables. It is often the first step in the data analysis process, and it helps to understand the data and the story behind it. In this article, we will discuss what EDA is, why it is important, and the methods and tools used in EDA.

What is Exploratory Data Analysis?

Exploratory data analysis is a process of analyzing data to summarize its main characteristics, including identifying patterns and trends, and discovering relationships between variables. The purpose of EDA is to gain an understanding of the data and identify potential outliers, missing values, and other data quality issues that may impact the accuracy of subsequent analyses.



Why is Exploratory Data Analysis Important?

Exploratory data analysis is important for a number of reasons:

  1. Helps to identify trends and patterns: EDA helps to identify patterns and trends in the data that might not be apparent at first glance.

  2. Helps to identify outliers: Outliers can have a significant impact on statistical analyses, and EDA helps to identify them so that they can be removed or dealt with appropriately.

  3. Helps to identify missing data: Missing data can also have a significant impact on statistical analyses, and EDA helps to identify missing data so that it can be imputed or removed as necessary.

  4. Helps to select appropriate modeling techniques: EDA helps to identify relationships between variables, which can inform the selection of appropriate modeling techniques.

Methods and Tools Used in Exploratory Data Analysis

There are several methods and tools that can be used in exploratory data analysis, including:

  1. Summary statistics: Summary statistics such as mean, median, and standard deviation provide a quick overview of the data and help to identify potential outliers.

  2. Visualization techniques: Visualization techniques such as histograms, scatterplots, and boxplots help to identify patterns and relationships in the data.

  3. Correlation analysis: Correlation analysis helps to identify relationships between variables, which can be used to inform modeling techniques.

  4. Clustering analysis: Clustering analysis helps to group data points that are similar to each other, which can be used to identify patterns in the data.

Conclusion

Exploratory data analysis is a critical step in the data analysis process that helps to understand the data and identify potential data quality issues. EDA helps to identify patterns and trends, outliers, missing data, and relationships between variables, which can inform subsequent analyses and modeling techniques. By using methods and tools such as summary statistics, visualization techniques, correlation analysis, and clustering analysis, analysts can gain a deeper understanding of their data and make more informed decisions.

360DigiTMG delivers data science course in Hyderabad, where you can gain practical experience in key methods and tools through real-world projects. Study under skilled trainers and transform into a skilled Data Scientist. Enroll today!

For more information

360DigiTMG - Data Analytics, Data Science Course Training Hyderabad  

Address - 2-56/2/19, 3rd floor,, Vijaya towers, near Meridian school,, Ayyappa Society Rd, Madhapur,, Hyderabad, Telangana 500081

099899 94319

Labels:

Comments

Post a Comment

Popular posts from this blog

Hacks To Earn Money When You Feel Totally Broke In School

  Most of the literature we find on machine learning talks about two types of learning methods – supervised and unsupervised. This means we have already got data from which to develop models utilizing algorithms corresponding to Linear Regression, Logistic Regression, and others. With this mannequin, we are in a position to make additional predictions like given knowledge on housing costs, and what's going to the worth of a house with a given set of options. This means that even if some of the center pixels are lit up, our perceptron cares much less about these pixels. After all of the depth values have been acquired, the weighted sum of the intensities is calculated in the switch function. Thus, the pixels of our interest have more influence on the result of the transfer operation than the others. Finally, the end result of this switch operation is passed into an activation performed. Once you do, seek the related dataset from which you may be able to apply. You can use Google’s l
Post a Comment
Read more

Data Science Course In Hyderabad

  I set to work on some fascinating tasks and case studies as a Data Science Intern. The mentors in the institute are talented, skilled, yet pleasant. It has been a great experience and I am sure I will profit lots from it in my profession. There is a severe shortage of Data Scientists with wonderful analytical expertise and deep quantitative skills who can analyze huge knowledge throughout all industries. The vertex and edge are the nodes and connections of a community, learned in regards to the statistics used to calculate the value of every node within the community. You may even be taught in regards to the google web page rating algorithm as a part of this module. Understand the means to carry out testing of those assumptions to make decisions for business issues. Learn about several sorts of Hypothesis testing and its statistics. This permits organizations to build efficiencies, oversee prices, distinguish new market openings, and raise their market benefit. Data science is the ac
Post a Comment
Read more

Introduction to Databases for Data Scientists

  Data scientists work with large amounts of data on a regular basis, and databases are essential tools for managing and analyzing that data. A database is a structured collection of data that is organized and stored in a way that allows for efficient access and retrieval. In this article, we will introduce some of the key concepts and terminology related to databases that data scientists should be familiar with. Types of Databases There are several types of databases, including relational, NoSQL, and object-oriented databases. Relational databases are the most commonly used type of database, and they store data in tables with rows and columns. NoSQL databases, on the other hand, are designed to handle unstructured data, such as documents and multimedia files. Object-oriented databases store data in objects, which are similar to the objects used in object-oriented programming. Structured Query Language (SQL) Structured Query Language (SQL) is a programming language used to manage relat
Post a Comment
Read more