The primary goal of EDA is to maximize the analyst’s insight into a data set and into the underlying structure of a data set, while providing all of the specific items that an analyst would want to extract from a data set, such as: a good-fitting, parsimonious model. a list of outliers.

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 can also help determine if the statistical techniques you are considering for data analysis are appropriate.

Explanatory analysis is what you do once you have found something interesting and want to know more about it. During explanatory analysis, you focus on what has happened (information) and why it happened (knowledge).

The purpose of exploratory data analysis is to: Check for missing data and other mistakes. Gain maximum insight into the data set and its underlying structure. Uncover a parsimonious model, one which explains the data with a minimum number of predictor variables.

Steps in Data Exploration and Preprocessing:

1. Identification of variables and data types.
2. Analyzing the basic metrics.
3. Non-Graphical Univariate Analysis.
4. Graphical Univariate Analysis.
5. Bivariate Analysis.
6. Variable transformations.
7. Missing value treatment.
8. Outlier treatment.

Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics often plotting them visually. This step is very important especially when we arrive at modeling the data in order to apply Machine learning. It often takes much time to explore the data.

Exploratory Data Analysis (EDA) is the process of analyzing and visualizing the data to get a better understanding of the data and glean insight from it. Process the data.

In statistics, exploratory data analysis is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. EDA encompasses IDA.

EDA — Exploratory Data Analysis – does this for Machine Learning enthusiast. It is a way of visualizing, summarizing and interpreting the information that is hidden in rows and column format.

Python and R language are the two most commonly used data science tools to create an EDA. Python: EDA can be done using python for identifying the missing value in a data set. Other functions that can be performed are — the description of data, handling outliers, getting insights through the plots.

To improve your data analysis skills and simplify your decisions, execute these five steps in your data analysis process:

1. Step 1: Define Your Questions.
2. Step 2: Set Clear Measurement Priorities.
3. Step 3: Collect Data.
4. Step 4: Analyze Data.
5. Step 5: Interpret Results.

There are dress shoes, hiking boots, sandals, etc. Using EDA, you are open to the fact that any number of people might buy any number of different types of shoes. You visualize the data using exploratory data analysis to find that most customers buy 1-3 different types of shoes.

Four Types of Data Analysis

• Descriptive Analysis.
• Diagnostic Analysis.
• Predictive Analysis.
• Prescriptive Analysis.

Exploratory Data Analysis is valuable to data science projects since it allows to get closer to the certainty that the future results will be valid, correctly interpreted, and applicable to the desired business contexts.

Quantitative data is defined as the value of data in the form of counts or numbers where each data-set has an unique numerical value associated with it. Quantitative data is usually collected for statistical analysis using surveys, polls or questionnaires sent across to a specific section of a population.

Analyzing Quantitative Data

1. Step 1: Data Validation. The purpose of data validation is to find out, as far as possible, whether the data collection was done as per the pre-set standards and without any bias.
2. Step 2: Data Editing. Typically, large data sets include errors.
3. Step 3: Data Coding.

Qualitative data analysis requires a 5-step process:

• Prepare and organize your data. Print out your transcripts, gather your notes, documents, or other materials.
• Review and explore the data.
• Create initial codes.
• Review those codes and revise or combine into themes.
• Present themes in a cohesive manner.

The most common qualitative data analysis methods (at least the ones we see) are:

• Qualitative content analysis.
• Narrative analysis.
• Discourse analysis.
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Conclusion. Representation of audible and visible data into written form is an interpretive process which involves making judgments and is therefore the first step in analysing data.

This involves taking a body of text (qualitative data) such as a transcript of an interview or an observational record to analyse an existing theory, explanation or hypothesis (from the top-down). This involves organising the qualitative data into specific themes that were pre-identified by the existing theory.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words. It is used to understand concepts, thoughts or experiences.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.