Exploratory Data Analysis

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Exploratory data analysis – first step towards data analysis process

Today's economy is highly driven by technology and every person is exhibiting small or large amount of data depending upon his/her vulnerability towards technology. Whether you buy accessories from an ecommerce website or are simply planning for a vacation with your family, every little information that you search online get tracked or monitored by different websites and there exists at least some amount of data on how every person spends his/her life. Whether you're carrying out a social experiment or running a business, there is something to be studied from the data that your customers expose, but being aware of what to do with that heap of information is a big task altogether. Companies these days are focusing on analyzing and unambiguously thrusting data to draw conclusions which can form a strong base and support while making extremely important decisions for their business.

exploratory data analysis

Data analysis can be a daunting task to take up, but with biztechnosys data analysis services, everything becomes quite feasible for you to dig through all the answers that are apparently hidden in the unlimited amount of data stored within your system. Data analysis mainly consists of inspecting data followed by cleaning, transforming, modeling the data to highlight on important bits, pieces of information, which enables you to make major decisions related to your business. Data analysis is considered highly important for the success of your business considering the fact that it enables you to monitor both opportunities as well as risks that your business is facing. Biztechnosys data analysis services are completely geared to support companies of all sizes, with capability to collect, analyze data effectively, deliver the extra edge needed to increase sales and outshine competitors. Since comprehensive data analysis services needs time-intensive work and professional skills to keep up with rapid technological change, biztechnosys comes with a practical solution with data management expertise combined with data analysis services.

Exploratory data analysis

Exploratory data analysis is an approach which helps to answer all the above mentioned questions, ensuring that the best possible outcomes for the project are delivered. Exploratory data analysis is an approach for analyzing, summarizing, visualizing data sets with their crucial or extremely important characteristics, frequently with the help of visual methods. With the increase of tools permitting for quick and easy implementation of powerful machine learning algorithms, it becomes a bit tempting to skip exploratory data analysis. Exploratory data analysis permits to get a bit closer to the certainty that the upcoming future results will be valid correctly interpreted and are applicable to the desired business contexts.

Exploratory data analysis also assists to find insights that were not evident or suitable for investigating to business stakeholders and data scientists but can be very informative about a particular business. Exploratory data analysis is particularly performed to define and refine the selection of feature variables that will be used for machine learning. Once data scientists become familiar with the data set, they often have to return to feature engineering step, since the initial features may turn out not to be serving their intended purpose. Once the eda stage is over, data scientists get access to a firm feature set they need for supervised and unsupervised machine learning.

Tools and techniques of exploratory data analysis

S-plus and r are the two most important statistical programming packages used to conduct exploratory data analysis. There are certain statistical functions and techniques that you can perform with these important tools and it includes -

  • Clustering and dimension reduction techniques which help you to create graphical displays of high-dimensional data containing many variables
  • Univariate visualization includes univariate visualization of each field in the raw dataset with summary statistics
  • Bivariate visualizations and summary statistics that permits you to assess the relationship between each variable in the dataset and provides the target variable you’re looking at

It is always recommended to explore each data set using multiple exploratory techniques and then compare the results. Once the data set is fully understood, it is very much possible that the data scientist will have to go back to data collection and cleansing phases in order to transform the data set according to the required business outcomes. The primary aim of this step is to become confident that the data set is ready to be used in a machine learning algorithm.

Why omitting exploratory data analysis is a bad idea!

In a rush to get to the machine learning stage or simply impress business stakeholders very quickly and easily, data scientists often incline to either entirely skip the exploratory data analysis process or do a very shallow work. Skipping exploratory data analysis process can often lead to skewed data, with outliers, too many missing values and can result in some very unwanted outcomes for the business project such as -

  • Generating inaccurate models
  • Generating accurate models on the wrong data
  • Choosing the wrong variables for the model
  • Ineffective use of the resources, including rebuilding of the model

Therefore, it is extremely crucial for the data scientists to consider exploratory data analysis step and not skip it. Exploratory data analysis helps in identifying various factors that affects the business projects such as -

  • Eda can help spotting mistakes and missing data
  • Eda can aid in mapping out the underlying structure of the data
  • Eda can identify the most important variables
  • Eda can help listing anomalies and outliers
  • Eda process can help in testing a hypotheses / checking assumptions related to a specific model
  • Eda can successfully establish a parsimonious model (a model that can be used to explain the data with minimal predictor variables)
  • Eda can help estimating parameters and figuring out the associated confidence intervals or margins of error

Why choose biztechnosys data analysis services?

Biztechnosys provides best in class solutions to help your business better integrate, increase confidence, improve data management and outshine competitors.

We perform data conversion from unstructured formats to digital structured formats in order to support organizational activities and gain competitive advantages.

We at biztechnosys are highly equipped with latest resources technology and experience of digitization to provide foolproof comprehensive data conversion services.

Our team of experienced developers can handle your data analytics infrastructure, apply advanced data analysis to provide you with regular, ad hoc reports, alerts, predictions, as well as self-service analytics.

Our valuable customers can make use of our managed analytics services to prioritize on accurate planning of their activities, continuous business management, change management, optimization of their business processes.

We at biztechnosys possess substantial domain expertise our focal industries being manufacturing, retail, wholesale, professional services, healthcare, financial services, telecommunications.

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