Data exploration is a critical initial step in the data analysis process, where analysts examine large datasets to uncover patterns, outliers, and relationships before formal modeling and analysis occur. This stage, called exploratory data analysis (EDA), utilizes various statistical techniques and powerful data visualization tools to understand the data’s key characteristics, quality, and structure. Popular open-source tools like Python and R and software like Tableau enable robust data visualization during exploration of data through methods like histograms, scatter plots, box plots, and more.

Effective data exploration allows early detection of data quality issues, identifies variables and relationships of interest, and guides the direction of subsequent predictive modeling and machine learning workflows. Data Analysts can make data-driven decisions by fully understanding the raw data, optimizing their analysis approach, and extracting maximum insight from the available information. Careful exploratory analysis is, therefore, a crucial foundational step for any successful data science or analytics project. This guide will explore the key stages, statistical methods, and data exploration techniques skilled data analysts and scientists use.

**Overview: **

- Learn about the data analysis process to understand large datasets’ key characteristics, quality, and structure.
- Gain an understanding of the steps of data exploration.
- Understand the importance of data exploration, such as understanding data, identifying issues, guiding analysis, enhancing data quality, and facilitating data-driven decision-making.
- Learn different statistical methods and visualization tools (like Python, R, and Tableau) and their applications in EDA.

Data exploration is a critical step in data analysis, where data scientists and analysts examine large datasets to understand their main characteristics before further analysis. This stage, often called Exploratory Data Analysis (EDA), involves using various statistical techniques and data visualization tools to uncover patterns, relationships, and outliers within the data. Tools like Python, R, and Tableau are commonly used for this purpose, enabling data visualization through graphs, histograms, scatter plots, and box plots.

**Data Collection**begins with gathering raw data from various sources. This data can be structured, semi-structured, or unstructured and is often stored in SQL databases or spreadsheets.**Data Cleaning**: This involves addressing missing values, removing duplicates, correcting errors, and ensuring the dataset is high quality. Data preparation is crucial for accurate analysis.**Data Transformation**: Data scientists transform the data into suitable formats for analysis, including normalizing and creating new variables or features. This step can involve using algorithms to preprocess the data.**Data Visualization**: Data analysts create visual representations of the data using tools like Tableau, Excel, and Python libraries. Scatter plots, bar charts, histograms, and box plots help identify trends and outliers.**Statistical Summary**: Analysts compute basic statistics, including mean, median, mode, and standard deviation, to summarize the data. Univariate and bivariate analyses are performed to understand relationships between variables.**Hypothesis Generation**: Based on the insights gained from the previous stages, analysts generate hypotheses and identify areas for further analysis. This step guides subsequent data mining and machine learning workflows.

**Understanding of the Data**: Exploration of data provides a comprehensive understanding of the dataset, including its structure, distribution, and anomalies. This foundational knowledge is crucial for effective data analysis and decision-making.**Identifying Issues**: Early detection of data quality issues, such as outliers and missing values, ensures that these do not negatively impact the results of further analysis.**Guiding Analysis**: By identifying key patterns and trends, data exploration helps prioritize areas for detailed analysis and model development, ensuring the efficient use of resources.**Enhancing Data Quality**: Cleaning and transformation processes improve the overall quality of the dataset, leading to more reliable and accurate analytical outcomes.**Facilitating Decision-Making**: The insights gained from data exploration inform business intelligence efforts, enabling data-driven decision-making. Effective exploratory data analysis (EDA) can significantly enhance predictive modeling and other advanced data analytics tasks.

Remember, the quality of your inputs decides the quality of your output. So, once your business hypothesis is ready, spending a lot of time and effort here makes sense. With my estimate, data exploration, cleaning, and preparation can take up to 70% of your total project time.

Below are the steps involved in understanding, cleaning, and preparing your data for building your predictive model:

- Variable Identification
- Univariate Analysis
- Bi-variate Analysis
- Missing values treatment
- Outlier treatment
- Variatransformationtion
- Variable creation

Finally, we will need to iterate over steps 4 – 7 multiple times before we develop our refined model.

Let’s now study each stage of data exploration in detail.

First, identify the **Predictor** (Input) and **Target** (output) variables. Next, identify the data type and category of the variables.

Let’s understand this step in data exploration more clearly by taking an example.

For Example, suppose we want to predict whether the students will play cricket (refer to the data set below). Here, you need to identify predictor variables, target variables, data type of variables, and category of variables. Below, the variables have been defined in different categories:

At this stage, we explore variables one by one. The method to perform univariate analysis will depend on whether the variable type is categorical or continuous. Let’s look at these methods and statistical measures for categorical and continuous variables individually:

**Continuous Variables:** In the case of continuous variables, we need to understand the central tendency and spread of the variable. These are measured using various statistical metrics visualization methods in data exploration as shown below:

**Note:** Univariate analysis is also used to highlight missing and outlier values. In the upcoming part of this series, we will examine methods for handling missing and outlier values.

**Categorical Variables:** For categorical variables, we’ll use a frequency table to understand the distribution of each category. We can also read the percentage of values under each category. It can be measured against each category using two metrics: Count and Count%. A bar chart can be used as a visualization.

Bivariate analysis in data exploration refers to finding the relationship between two variables. Here, we look for association and disassociation between variables at a pre-defined significance level. We can perform a bivariate analysis for any combination of categorical and continuous variables, such as Categorical and categorical, Categorical and continuous, and Continuous and continuous. Different methods are used to tackle these combinations during the analysis process.

Let’s understand the possible combinations in detail:

In a bivariate analysis of two continuous variables, we should look at a scatter plot. It is a nifty way to determine the relationship between two variables. The pattern of the scatter plot indicates the relationship between variables, which can be linear or non-linear.

A scatter plot shows the relationship between two variables but does not indicate its strength. To find the strength of the relationship, we use Correlation, which varies between -1 and +1.

- -1: Perfect negative linear correlation
- +1: Perfect positive linear correlation
- 0: No correlation

This correlation can be derived using the following formula:

**Correlation = Covariance(X,Y) / SQRT( Var(X)* Var(Y))**

Various tools have functions or functionality to identify correlations between variables in data exploration. In Excel, function CORREL() returns the correlation between two variables, and SAS uses procedure PROC CORR to identify the correlation. This function returns the Pearson Correlation value to identify the relationship between two variables:

In above example, we have good positive relationship(0.65) between two variables X and Y.

To find the relationship between two categorical variables, we can use the following methods:

**Two-way table:**We can start analyzing the relationship by creating a two-way table of count and count%. The rows represent the category of one variable, and the columns represent the categories of the other variable. We show the count or count% of observations available in each combination of row and column categories.**Stacked Column Chart:**This method is more of a visual form of a two-way table.

**Chi-Square Test:**This test derives the statistical significance of the relationship between the variables and tests whether the evidence in the sample is strong enough to generalize the relationship to a larger population. Chi-square Test is based on the difference between the expected and observed frequencies in one or more categories in the two-way table. It returns the computed chi-square distribution probability with the degree of freedom.

**0 Probability:**It indicates that both categorical variables are dependent**Probability of 1:**It shows that both variables are independent.**Less than 0.05 Probability:**It indicates that the relationship between the variables is significant at 95% confidence.

The chi-square test statistic for a test of independence of two categorical variables is found by:

Where O represents the observed frequency. E is the expected frequency under the null hypothesis.

From the previous two-way table, the expected count for product category 1 to be of small size is 0.22. It is derived by taking the row total for Size (9) times the column total for Product category (2) and then dividing by the sample size (81). This procedure is conducted for each cell. Statistical Measures used to analyze the power of relationship are:

- Cramer’s V for Nominal Categorical Variable
- Mantel-Haenszed Chi-Square for ordinal categorical variable.

To explore the relation between categorical and continuous variables, we can draw box plots for each level of categorical variables. The plots will not show statistical significance if the levels are small in number. We can perform a Z-test, T-test, or ANOVA to examine the statistical significance.

**Z-Test/ T-Test:**Either test assesses whether the mean of two groups is statistically different. If the probability of Z is small, then the difference between the two averages is more significant. The T-test is very similar to the Z-test but is used when the number of observations for both categories is less than 30.**ANOVA:**It assesses whether the average of more than two groups is statistically different.

**Example:** Suppose we want to test the effect of five different exercises. For this, we recruited 20 men and assigned one type of exercise to 4 men (5 groups). Their weights are recorded after a few weeks. We need to determine whether these exercises’ effect on them is significantly different. This can be done by comparing the weights of the 5 groups of 4 men each.

*Also Read: Difference between Z-Test and T-Test*

Now, we will examine the methods for treating Missing values. More importantly, we will also examine why missing values occur in our data and why treating them is necessary.

Missing data in the training data set can reduce the power/fit of a model or lead to a biased model because we have not analyzed the behavior and relationship with other variables correctly. Missing Value Treatment can also lead to wrong predictions or classifications in data exploration.

Notice the missing values in the image above: In the left scenario, we have not treated missing values. The inference from this data set is that males’ chances of playing cricket are higher than females’. On the other hand, if you look at the second table, which shows data after treatment of missing values (based on gender), we can see that females have higher chances of playing cricket than males.

We looked at the importance of treating missing values in a dataset. Now, let’s explain the reasons for these missing values. They may occur in two stages:

**Data Extraction**: The extraction process may have problems. In such cases, we should double-check for correct data with data guardians. Some hashing procedures can also be used to ensure correct data extraction. Errors at the data extraction stage are typically easy to find and can be corrected easily.**Data collection**: These errors occur during data collection and are more challenging to correct. They can be categorized into four types:**Missing completely at random**is when the probability of missing a variable is the same for all observations. For example, respondents in the data collection process declare their earnings after tossing a fair coin. If a head occurs, the respondent declares his / her earnings and vice versa.**Missing at random:**This is when a variable is missing at random, and the missing ratio varies for different values/levels of other input variables.**Missing that depends on unobserved predictors:**This is a case when the missing values are not random and are related to the unobserved input variable. For example, in a medical study, if a particular diagnostic causes discomfort, there is a higher chance of dropping out.**Missing that depends on the missing value itself:**This is a case when the probability of a missing value is directly correlated with the missing value itself. For example, people with higher or lower incomes are likely to provide non-response to their earnings.

It is of two types: List Wise Deletion and Pair Wise Deletion.

- In list-wise deletion, we delete observations where any variable is missing. Simplicity is one major advantage of this method, but this method reduces the power of the model because it reduces the sample size.
- In pair-wise deletion, we analyze all cases where the variables of interest are present. This method has the advantage of keeping as many cases available for analysis as possible. One disadvantage is that it uses different sample sizes for other variables.
- Deletion methods are used when the nature of missing data is “Missing completely at random.” Otherwise, non-random missing values can bias the model output.

Imputation is a method of filling in the missing values with estimated ones. The objective is to employ known relationships that can be identified in the valid values of the data set to assist in evaluating the missing values. Mean / Mode / Median imputation is one of the most frequently used methods. It consists of replacing the missing data for a given attribute with the mean or median (quantitative attribute) or mode (qualitative attribute) of all known values of that variable. It can be of two types:-

In this case, we calculate the mean or median for all non-missing values of that variable and then replace the missing value with the mean or median. In the above table, the variable “Manpower” is missing, so we take the average of all non-missing values of “Manpower” (28.33) and then replace the missing value with it.

In this case, we calculate the average of non-missing values for gender “Male” (29.75) and “Female” (25) individually and then replace the missing value based on gender. For “Male, ” we will replace the missing values of manpower with 29.75 and for “Female,” with 25.

The prediction model is a sophisticated method for handling missing data. Here, we create a predictive model to estimate values that will substitute the missing data. In this case, we divide our data set into two sets: One set with no missing values for the variable and another with missing values. The first data set becomes the training data set of the model. In contrast, the second data set with missing values is the test data set, and the variable with missing values is treated as the target variable. Next, we create a model to predict the target variable based on other attributes of the training data set and populate missing values of the test data set. We can use regression, ANOVA, Logistic regression, and various modeling techniques to perform this. There are two drawbacks to this approach:

- The model-estimated values are usually more well-behaved than the actual values.
- If there are no relationships between attributes in the data set and the attribute with missing values, then the model will not be precise for estimating missing values.

In this imputation method, the missing values of an attribute are imputed using the given number of attributes most similar to the attribute whose values are missing. The similarity of the two characteristics is determined using a distance function. It is also known to have certain advantages & disadvantages.

**Advantages:**- K-nearest neighbor can predict both qualitative & quantitative attributes
- The creation of a predictive model for each attribute with missing data is not required
- Attributes with multiple missing values can be easily treated
- The correlation structure of the data is taken into consideration

**Disadvantage:**- The KNN algorithm is very time-consuming when analyzing an extensive database. It searches through all the datasets, looking for the most similar instances.
- The choice of k-value is critical. A higher value of k would include attributes that are significantly different from what we need, whereas a lower value implies missing out on significant attributes.

After dealing with missing values, the next task is dealing with outliers. We often neglect outliers while building models, which is discouraging. Outliers tend to make data skewed and reduce accents. Let’s learn more about outlier treatment.

Let us now look at techniques of outlier detection and treatment for data exploration.

Data analysts and data scientists commonly use outliers. They need close attention, or else they can result in wildly wrong estimations. Simply speaking, an Outlier is an observation that appears far away and diverges from an overall pattern in a sample.

For example, we do customer profiling and find out that the average annual income of customers is $0.8 million. However, two customers have yearly incomes of $4 and $4.2 million. These two customers’ annual incomes are much higher than the rest of the population. These two observations will be seen as Outliers.

*Also Read: Detecting and Treating Outliers | Treating the odd one out!*

Outliers can be of two types: **Univariate** and **Multivariate**. Above, we have discussed the example of a univariate outlier. Outlier outliers can be found when we look at the distribution of a single variable. Multi-variate outliers are outliers in an n-dimensional space. To find them, you have to look at distributions in multi-dimensions.

Let us understand this with an example. Let us say we know the relationship between weight and weight. Below, we have univariate and bivariate distributions of weight and weight. Take a look at the box plot. We do not have any outliers (above and below 1.5*IQR, the most common method). Now, look at the scatter plot. Here, we have two values below and one above the average in a specific segment of weight an eighth.

Whenever we come across outliers, the ideal way to tackle them is to find out the reason for having these outliers. The method to deal with them would then depend on the reason for their occurrence. Causes of outliers can be classified into two broad categories:

**Artificial (Error) / Non-natural****Natural**.

**Data Entry Errors:**Human errors, such as errors caused during data collection, recording, or entry, can cause outliers in data. For example, a customer’s income is $100,000. Accidentally, the data entry operator puts an additional zero in the figure. Now, the income becomes $1,000,000, which is ten times higher. This will be the outlier value compared to the rest of the population.**Measurement Error**is the most common source of outliers. It is caused when the measurement instrument used turns out to be faulty. For example, there are ten weighing machines. Nine of them are correct, and one is defective. The weight measured by people on the defective machine will be higher / lower than those in the group. The weights measured on the faulty machine can lead to outliers.**Experimental Error:**Another cause of outliers is experimental error. For example, in a 100-meter sprint with seven runners, one runner missed concentrating ‘n ‘on the ‘Go’ call, which caused him to start late. Hence, his run time was more than the other runners, and his total run time can be an outlier.**Intentional Outlier**This is commonly found in self-reported measures involving sensitive data. For example, Teens typically underreport the amount of alcohol they consume. Only a fraction of them report actual values. Here, actual values might look like outliers because the rest of the teens are underreporting their consumption.*:***Data Processing Error:**We extract data from multiple sources while mining data. Some manipulation or extraction errors may lead to outliers in the dataset.**Sampling error:**For instance, we have to measure the weight of athletes. By mistake, we include a few basketball players in the sample. This inclusion is likely to cause outliers in the dataset.

Outliers can drastically change the results of the data analysis and statistical modeling. There are numerous unfavorable impacts of outliers in the data set:

- It increases the error variance and reduces the power of statistical tests
- If the outliers are non-randomly distributed, they can decrease normality
- They can bias or influence estimates that may be of substantive interest
- They can also impact the basic assumptions of regression, ANOVA, and other statistical model assumptions.

To understand the impact deeply, let an example check what happens to a data set with and without outliers in the data set.

**Example:**

As you can see, a data set with outliers has significantly different mean and standard deviation. In the first scenario, we will say that the average is 5.45. But with the outlier, the average soars to 30, which would completely change the estimate.

The most commonly used method to detect outliers in data exploration is visualization. We use various visualization methods, like **Box-plot**, **Histogram**, and **Scatter Plot** (above, we have used box and scatter plots for visualization). Some analysts also use various thumb rules to detect outliers. Some of them are:

- Any value which is beyond the range of -1.5 x IQR to 1.5 x IQR
- Use capping methods. Any value that is out of the range of the 5th and 95th percentile can be considered an Outlier
- Data points three or more standard deviations away from the mean are considered outliers. Outlier detection is merely examining data for influential data points; it also depends on the business’s understanding.
- Bivariate and multivariate outliers are typically measured using either an index of influence leverage or distance. Popular indices such as Mahalanobis’ distance and Cook’s
*D*are frequently used to detect outliers. - In SAS, we can use PROC Univariate and PROC SGPLOT. We also examine statistical measures like STUDENT, COOKD, STUDENT, and others to identify outliers and influential observations.

Most ways to deal with outliers in data exploration is similar to methods of missing values, like deleting observations, transforming them, binning them, treating them as a separate group, imputing values, and other statistical methods. Here, we will discuss the standard techniques used to deal with outliers:

**Deleting observations:**We delete outlier values if they are due to data entry errors, data processing errors, or outlier observations that are very small in numbers. We can also use trimming at both ends to remove outliers.**Transforming and binning values:**Transforming variables can also eliminate outliers. The natural log of a value reduces the variation caused by extreme values. Binning is also a form of variable transformation. The decision Tree algorithm allows us to deal with outliers well due to the binning of variables. We can also use the process of assigning weights to different observations.

**Imputing:**Like imputation of missing values, we can also impute outliers. We can use mean, median, and mode imputation methods. Before imputing values, we should analyze whether they are natural, outliers, or artificial. If it is artificial, we can go with imputing values. We can also use a statistical model to predict the values of outlier observations, and after that, we can blame them with expected values.**Treat separately:**If there are many outliers, we should treat them separately in the statistical model. One approach is to treat both groups as separate entities, build an individual model for each group, and then combine the output.

We have learned about the steps of data exploration, missing value treatment, and outlier detection and treatment techniques. These three stages will improve your raw data regarding information availability and let’s. Let’s proceed to the final stage of data exploration: Feature Engineering.

Feature engineering is the science (and art) of extracting more information from existing data. You are not adding any new data here, but you are making the data you already have more helpful.

For example, you are trying to predict footfall in a shopping mall based on dates. If you try to use the dates directly, you may be unable to extract meaningful insights from the data. This is because footfall is less affected by the day of the month than by the day of the week. This information about the day of the week is implicit in your data. You need to bring it out to improve your model.

This exercise of bringing out information from data is known as feature engineering.

You perform feature engineering once you have completed the first 5 steps in data exploration – Variable Identification, Univariate, Bivariate Analysis, Missing Values Imputation, and Outliers Treatment. Feature engineering itself can be divided into 2 steps:

- Variable transformation.
- Variable / Feature creation.

These two techniques are vital in data exploration and remarkably impact prediction. Let’s plot each step in these steps.

In data modeling, transformation refers to replacing a variable with a function. For instance, replacing a variable x by the square/cube root or logarithm x is a transformation. In other words, transformation is a process that changes the distribution or relationship of a variable with others.

Let’s look at the situations when variable transformation is useful.

Below are the situations where variable transformation is a requisite:

- When we want to
**change the scale**of a variable or standardize the values of a variable for better understanding. While transformation ion is a must if you have data in different scales, transformation ion does not change the shape of the variable distribution. - When we can
**transform complex non-linear relationships into linear relationships**, the existence of a linear relationship between variables is more straightforward compared to a non-linear or curved relation. Transformation helps us convert a non-linear relation into a linear relation. A scatter plot can be used to find the relationship between two continuous variables. These transformations also improve prediction. Log transformation is the commonly used transformation tech in these situations. **Symmetric distributions are preferred over skewed distribution**s as they are easier to interpret and generate inferences. Some modeling techniques require a normal distribution of variables. So, we can use transformations that reduce skewness whenever we have a skewed distribution. For a right-skewed distribution, we take the square/cube root or logarithm of the variable, and for a left-skewed distribution, we take the square/cube or exponential of the variables.- Variable Transformation is also done from an
**implementation point of view**(Human involvement). Let’s understand it more clearly. In one of my projects on employee performance, I found that age directly correlates with the employee’s performance, i.e., the higher the age, the better the performance. From an implementation standpoint, launching an age-based program might present an implementation challenge. However, categorizing the sales agents into three age group buckets of <30 years, 30-45 years, and >45 and formulating three different strategies for each group is judicious. This categorization technique is known as the Binning of Variables.

Various methods are used to transform variables. As discussed, some include square root, cube root, logarithmic, binning, reciprocal, and many others. Examine these methods in detail and highlight their pros and cons.

**Logarithm:**The log of a variable is a standard transformation method used to change the shape of the variable’s distribution on a distribution plot. It is generally used to reduce the right skewness of variables. However, it can also not be applied to zero or negative values.**Square / Cube root:**A variable’s square and cube root affect variable distribution. However, it is not as significant as logarithmic transformation. Cube root has its advantages. It can be applied to negative values, including zero. Square root can be applied to positive values, including zero.**Binning**is used to categorize variables. It is performed on original values, percentiles, or frequencies. The decision to use this categorization technique is based on business understanding. For example, we can categorize income into three categories: high, Average, and Low. We can also perform co-variate binning, which depends on the value of more than one variable.

Feature / Variable creation generates new variables/features based on an existing variable(s). For example, a date(dd-mm-yy) is an input variable in a data set. We can generate new variables like day, month, year, week, and weekday that may have a better relationship with the target variable. This step is used to highlight the hidden relationship in a variable:

There are various techniques to create new features. Let’s look at some of the commonly used methods:

**Creating derived variables**means creating new variables from existing variable(s) using a set of functions or different methods. Let’s look at it through the “Titanic – Kaggle competition.” In this data set, variable age has missing values. To predict missing values, we used the salutation (Master, Mr, Miss, Mrs) of the name as a new variable. How do we decide which variable to create? Honestly, this depends on a business understanding of the analyst, his curiosity, and the set of hypotheses he might have about the problem. Methods such as taking the log of variables, binning variables, and other methods of variable transformation can also be used to create new variables.**Creating dummy variables:**One of the most common applications of dummy variables is to convert categorical variables into numerical variables. Dummy variables are also called Indicator Variables. It is useful to take categorical variables as a predictor in statistical models. Categorical variables can take values 0 and 1. Let’s take a variable ‘gender’. We can produce two variables, namely, “**Var_Male**” with values 1 (Male) and 0 (No male) and “**Var_Female**” with values 1 (Female) and 0 (No Female). We can also create dummy variables for more than two classes of categorical variables with n or n-1 dummy variables.

In conclusion, comprehensive data exploration is a critical initial step for any data science, machine learning, or analytics project involving large datasets. Data analysts and scientists deeply understand the raw data through exploratory data analysis (EDA) techniques like univariate analysis, bivariate analysis, data visualization with graphs and plots, and outlier detection. Popular open-source tools like Python and commercial options like Tableau enable robust EDA through histograms, scatter plots, box plots, and other visualizations.

Effective data exploration allows early identification of data quality issues like missing values and outliers, guides future analysis like regression modeling and predictive modeling, and facilitates data-driven decision-making for business intelligence. The data exploration phase lays the groundwork for accurate insights, optimal data mining, and reliable statistical analysis outputs by transforming variables, creating new features, and preparing high-quality datasets. Leveraging best practices in EDA is essential for data scientists to unlock maximum value from their data assets across formats and domains.

A. Data analysis interprets data to conclude, often using statistical methods and algorithms. Data exploration is the preliminary phase of examining data to understand its structure, identify patterns, and spot anomalies through visualizations and summary statistics.

A. Data exploration tools are software or platforms that assist in exploring and analyzing data. These tools enable users to interact with and visualize data, identify patterns, and discover insights. Some popular data exploration tools include Tableau, Power BI, QlikView, and Google Analytics.

A. During data exploration, visualize data, check for missing values, assess data distributions, and identify correlations and patterns to understand the dataset’s characteristics and prepare for detailed analysis.

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Really useful and comprehensive, thanks

Hi Ray, I would like to thank you very much for this useful post I took more than 30 statistical courses but your post has summarized them for me Now all things are clear about EDA I'm member of the John Hopkins University Data Scientists (Coursera) Group Best,

Excellent series of blog posts. Thanks and keep up the good work!

Superb writing, crisp and comprehensive. Certainly a good refresher. Keep writing!

Very comprehensive. Thanks

Excellent article on the most important aspects of Machine Learning. The points are explained in a simple and concise manner. Thank you.

Thank you very much for this tutorial!

I haven't come across any other article as detailed as this one. Anyone who is keen about data exploration and Predictive Analytics in general has to go through this. Wondering if you have any data set where in I can work on it. Bookmarked!

Hi Ray, This is a great post. You have treated a fairly vast topic with just the right amount of detail. This makes it very useful, and also very intresting. Thank you for the good work. Keep it up.

Thank you so much for this very valuable post. I like your blogs, Please continue your good work !

I would like to thank Mr. Sunil Ray for such comprehensive information. Also, I would request some to write a blog on ETL, SAS BI and how SAS BI is better than other BI tools like Tableau, Qlikview....gaining more popularity in market. Thanks again for sharing helpful information!!

Well defined process of data exploration Sunil. I appreciated if you continue this wonderful work and post an example of data analysis step by step using Python. Thanks

Thank you Mr. Ray for the very comprehensive discussion on data exploration. I specially liked how you emphasized on the importance of EDA with this statement "quality and efforts invested in data exploration differentiates a good model from a bad model". Great work Sir! I wish you can tackle dimensionality reduction techniques, principal components analysis, discriminant analysis and the likes in the future. Thanks again Mr. Ray.

I found myself nodding my noggin all the way thguorh.

Its really worth to read. Very comprehensive and easy to understand . I will be happy to read your article using R on data exploration & Data preparation.

Thank you all for exciting comments and I’m glad it helped. Regards, Sunil

Hi Sunil: Thanks for your article on such an important topic. BTW, there is a missing graph on the paragraph Continuous & Continuous under Bi-Variate Analysis. Could you please edit it and add the missing graph. I think is pointing to a wrong place looking for the ping file. Thanks.

The missing picture/draw might be located here: http://www.analyticsvidhya.com/wp-content/uploads/2015/02/Data_exploration_4.png This picture is the missing one below the paragraph: "Continuous & Continuous: While doing bi-variate analysis between two continuous variables, we should look at scatter plot. It is a nifty way to find out the relationship between two variables. The pattern of scatter plot indicates the relationship between variables. The relationship can be linear or non-linear."

Hi Sunil, An intriguing article, I can see the amount of hard work you must have put into it. Its a must read. Thanks, Akshay Kher

Thanks a lot for the comprehensive material Sunil. I had All these points scattered across but you got all of them together, along with few new pointers. Bookmarked this page and this would now be my first page to refer for any data analysis project.

Clear explanation with example and graph. Thanks.

This common techniques are core of any data analytics project. Good work keep up.

Very well explained and interesting article..It helped me a lot....Thanks a lot

when we create new variable like var_male and var_female we assign 0,1 to them? how is this 0,1 is used in our model? can we assign 200 instead of 0 and 2000 instead of 1? Please help .

clear, Concise and Very well explained. !!

Great article. One quick suggestion regarding log transform for zero or negative values. For all values, convert to absolute value, add one to all values (if data has lots if zeros), take log, then finally reapply the negative sign where original was negative. E.g. log(-2) = -1×(log(abs(-2)+1)) Hope that helps.

Excellent guide! Thank you very much! Very pedagogic and comprehensive. Two thumbs up! An excellent place to come back when starting a new data project...

Very well explained article.. Person having basic math /statistics understanding can also understand subject well..

Concise and comprehensive. Great article.

Very well written.

One of the best blogs I have ever read till date!

This is really great. Thank you so much!!

Can we use Weight of Evidence to impute outliers and Missing Values??

Great! Thanks!

Very Useful. Thank you.. :)

Amazing guide.. very structured and simplistic. enjoyed and learnt a lot reading this article.

Many thanks for the guide, very useful. Would you advise R packages that help with data exploration? Thanks

THANK YOU FOR SHARING THIS CONCEPTS AND METHOD.

If a variable is very skewed at 0 but valid. How should we treat them in a logistic regression framework?

Very useful. precise and clear Thank you.

Excellent article Thank you very much

really awesome..crisp and concise

I open a file in google drive to keep this page alone as a cheatsheet...Thank you so much..

This is very useful summary, thank you for that! I particularly liked the before-after comparisons to demonstrate the importance of the process steps. Thanks, Chill

Great article! Few questions: 1) Do you run your data exploration on sample or full data set? If sample then what percentage and any article on how to take samples for unstructured text based dataset. 2) How to explore fields which are unstructured text, images etc. Do we need to run feature extraction before we explore. how do we explore them anyway? I understand there's no single answer but in your opinion what's the best way to explore unstructured dataset.

The blog articles from AV are just awesome! Thanks to all the blog writers for sharing their knowledge.

Definitely going to Bookmark this blog ! Thank you .

you nailed the process. I thoroughly enjoyed reading your blog and learned a lot!!!! Thanks a lot for investing time and sharing your experience.

Well Written. it really shows how to tackle the data

Excellent read on EDA simple and to the point. Great Help to newbie like me.

Thank you for sharing knowledge. It helps a lot.

great article! Very useful!

You Sir are amazing...

Great article! I would like to add or comment on the imputation of missing values. I once had a dataset with missing values in one of the categorical variables. Instead of replacing missing values with the most frequent value of that variable, I looked at the distribution of unique values and found that they were all uniformly distributed. With this information, I would replace a missing value by randomly choosing a value among the set of unique values. It worked quite well but I would love to hear if this was statistically the right thing to do?

The Best. Period.

Hi Sunil Thank you very much for really useful and clear structure.

Great explanation, would be better. If you could give us some sample data and then explain step by step on that.

Loved reading it. Thanks for sum it up in the best explanatory manner. :) Best,

Well summarised explanations covering each topic of data exploration with enough details to understand. Thanks a lot for this post.

This is such an amazing resource. Thank you very much for sharing

It was a crisp and clear and more importantly step by step explanation of EDA process. I read all these things here and there but first time as an organized flow. Keep up the good work sir.You understood the pain points of novice data scientist.

I've started to study Data Science fewmonths ago, this tutorial was one of the most clarifying for me, the step by step guide introduced the theory that can easily be used at practice. Thanks for the advices.

Great! Very crisp, yet comprehensive.

"Though, It can’t be applied to zero or negative values as well". Did you mean "can" and not "can't"

Excellent article. thanx

Simple excellent post... keep writing.

[…] Source: www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/?utm_content=buffer087f0 […]

great one. Could you please also add python sample code for these examples? Thank you.

Hi Jack, I am working on a prediction problem for which I am using this post as a guide for EDA. If you want some code examples please check out https://github.com/JosephKevin/sales_prediction Regards, Joseph

Very well written article. One suggestion for next Enhanced version of the Article It would have been good of sample data set along with example from same data set is provided.

Thank you Sunil for explaining the Data Exploration process very lucidly. Kudos !

Hi Sunil that was a nice article. Thank U

Good and nice flow of explanation. Really useful for base understanding.

Thank you for the article, It is super helpful! Do you mind providing the download of the dataset as well? Thanks! As a beginner, I'd like to follow your tutorial step by step!

Hello Sunil, Really an amazing stuff . Appreciate you for sharing your hard work..

please tell me ,which course are better for statistical and exploratory analysis in sense of industry.

Best guide ever!

Hi, I was trying to research into covariate binning through Google, unfortunately I couldn't find anything. Is there another term I could use that's more popular? Thx.

amazing guide, thanks so much for posting this. would love to hear more from you and dive deeper into this topic.

Really great help for beginners in data exploration and feature engineering!

Very clear and concise as well as informative . Well done.

Very good article! Comprehensive and very easy to understand. Do you guys have any ebooks with all of this content?

great article. precisely written. Thanks for the clarity in the explanation given. keep up the good work.

well done. very helpful.

I agree with everyone else that this is a very good article. There are, however, some caveats. I am not a statistician so here is an incomplete list 1. Be sure whatever you do to data makes common sense, which should guide all your actions; 2. Be sure your data set is large enough such that the modifications you make have a small impact. 3. Beware of "messing with the randomness." Remember that the reason the Monty Hall problem works the way it does is that the randomness of the first draw (3 doors) is disturbed midstream 4. Know about what effect your change can have on small samples. Two good examples to Google are Abscombe's Quartet and Simpson's Paradox. There are others. 5. Know the difference between mistakes and extreme values even though both are sometimes referred to as "outliers.". The effect of extreme values may be valid and eliminating them can be very misleading (There is a huge literature on Extreme Value Theory. See www.mathestate.com for an in depth look at heavy tail phenomena). 6. Run a test for normality such as Jacque-Berta. If your model (like comparison of difference of means) requires normality and you use non-normal data you produce gibberish. RJB

The guide is super. IF you can take a sample dataset and apply all the steps to make dataset more informative then it would be very helpful.

Hi Sunil, Thank you for the amazing article, very organized and clear. I have a question In the 'Categorical & Continuous' bivariate analysis part, if ANOVA shows a statistically significant difference between various groups in one variable, how do we incorporate this knowledge into the prediction process ? Regards, Joseph

Thanks so much bro. Really useful stuff

Thanks bro..for such an awesome article.

Extremely helpful. Does a great job at breaking down each individual concept. Adding some actual code to the examples would also be helpful from a practical standpoint.

Wonderful and Descriptive but can I get some Working Codes which can highlight the procedure "what if the data is heterogeneous..?" (I mean to say multi-valued data and mixture of numeric and text form). Does Python, R or Matlab provide any help in this regard..?

[…] ref: https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/ […]

A well written primer. Thank you.

Great article. really helpful for beginners.

This is one of the Best article a beginner or a seasoned professional can read....

Thanks alot. Great article.

Sir, I am beginner in data science. I started reading aricles one by one. Your articles are awesome. please keep doing what you are doing. as people start reading your artcles one by one, soon there wont be any shartage in data science field. thank you so much

Hi! Thanks for this vital and core information. Your presentation is very sharp.

Thanks a lot. I'm a beginner to data science & machine learning and your blog posts provide a great platform to equip myself as a data scientist!! Detailed, easy to understand!! You have one of the best articles!!

Hello I am facing a problem with imputing values. I have mixed type data(nummerical+nominal) For nominal i want to input values with average. But nominal data has two cases either yes or no, How can i take mean for that??? Please suggest

Best inputs and for a beginners it gives complete picture of data exploration. Keep it up

Hi, Sir Thanks for your article, it help me

"Any value, which is beyond the range of -1.5 x IQR to 1.5 x IQR" is this correct or just badly expressed? This is a range centered on 0 without any reference to the actual values of the variables. Shouldn't this be something like 1st quartile -1.5IQR to 3rd quartile+1.5IQR?

Help is make information from our data ! Thanks !

i think data that is scatter plot. is Discrete variable, not continuous variable.

Hey - Can you clarify what your doubt is?

Hi Ray, It is good post as i am fresher it is very useful to me

It is very useful. Thank you for your efforts Sunil.

Very complete and useful ! Thank you !

Extremely useful article, can someone guide me to a link or any resource where all steps mentioned above are applied on real dataset.

Hi Bhagwat,

Hereis a training course on R for big mart sales dataset. A similar course will be made available soon.Great article, thanks!

Very resourcefull and helpfull article

Really an interesting article, also explained so well.

Excellent article & very useful, Thanks

The information is very useful to upskill ourselves.

Content was clear and informative to read.

It is very useful and helpfull article thank you

Its really great and knowledgeable.

its is very useful ,thankyou

Very well explained and interesting article..It helped me a lot....Thanks a lot

great article. precisely written. Thanks for the clarity in the explanation given. keep up the good work

great article. precisely written. Thanks for the clarity in the explanation given.

Great and helpful article

it is very helpful and useful

Great article. precisely written. Thanks for the clarity in the explanation given. keep up the good work.

Extremely useful article

Extremely useful and resource full article

good article and also very useful Thank you

good article and also very useful Thank you...............

Thanks for sharing this to us.

Very insightful article. We presented too. Thank you.

really good man this site is great. from the uk

Thankyou so much.. Really help me more understand the topic. Very helpful guides

Thank you so much for this contribution. Very valuable and useful indeed! 👍

veriy nice post wow post

wow post wow post

Very very very useful, thank you so much

I love GOLD! Big Booty Goddess of TEASE and MEAN. SPH, FIN, CUCK