Suppose you are looking to book a flight ticket for a trip of yours. Now, you will not go directly to a specific site and book the first ticket that you see.

Suppose you are looking to book a flight ticket for a trip of yours. Now, you will not go directly to a specific site and book the first ticket that you see. You’ll first search for the tickets on multiple websites on multiple airline service providers. You will then compare the cost of the tickets with the services they are providing. Is there free WiFi available? Are breakfast and lunch complimentary? Is the overall rating of the airlines better than the others?

Whatever measures you will take from thinking about buying a ticket and finding the best ticket option for you and booking it is called “Data Analysis”. The formal definition of Exploratory Data Analysis can be given as:

Exploratory Data Analysis (EDA) refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypotheses and to check assumptions with the help of summary statistics and graphical representations.

Types of Data (Image by author)

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*Dichotomous Variable: **A dichotomous variable is a variable that takes only one out of two possible values when measured. For eg. Gender: male/female. - *
*Polynomic Variable: **A polynomic variable is a variable that has multiple values to choose from. For eg. Educational Qualifications: Uneducated/ Undergraduate/ Postgraduate/ Doctoral, etc. - *
*Discrete Variable: **Discrete variables are countable variables. For eg. your bank balance, no. of employees in an organization, etc. - *
*Continuous Variable: **A continuous variable is a variable that has an infinite no. of possible values. Any kind of measure is a continuous variable. For eg. Temperature is a continuous variable. The temperature of a particular area can be described as 30 °C, 30.2 °C, 30.22 °C, 30.221 °C, and so on.

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Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.

You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.

Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.

Exploratory Data Analysis (EDA) With Variation And Covariation. Hi folks, welcome back to this edition of my blog, thank you so much for your love and support, I hope you all are doing well.

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant