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Correlation vs Covariance are two commonly used statistical concepts majorly wont to measure the linear relation between two variables in data. When wont to compare samples from different populations, covariance is employed to spot how two variables vary together whereas correlation is employed to work out how change in one variable affects the change in another variable. albeit there are certain similarities between these two mathematical terms, these two are different from one another . Read further to know the difference between covariance and correlation.

Covariance:

It is an indicator of the degree to which two variables change with reference to one another i.e.., it measures the direction of linear relationship between these two variables .The values of covariance can lies within the range of -? to +?

Here,

Xi – values of X variable
yj – values of Y variable
X?- mean of x variable
Y?- mean of y variable
N- Number of knowledge points ( n-1 for sample covariance)
Now let’s see the way to calculate an equivalent in python using inbuilt functions:

Here, Covariance for the variable itself is that the variance for an equivalent .

Correlation:

Correlation measures the strength and direction of linear relationship between two variables or we will say it’s a normalized version of covariance. By dividing the covariance with variance of the variables it scales down the range to -1 to +1 , comparatively correlation values are more interpretable.

As we will see from the formula itself, correlation is calculated from standardising covariance results; allow us to just execute an equivalent in python and see the difference.


Here , the correlation results on original data is analogous to covariance on standardized data ( with deviation in decimal values ) . For any of our applications like PCA , we will use either of them which yields an equivalent results. Alternatively, we will use function from NumPy modules also Covariance : numpy.cov(a,b) Correlation: numpy. corrcoef (a,b)

Difference between Correlation and Covariance:

Covariance is suffering from the change in scale as opposite to an equivalent correlation values aren't influenced by change in scale. Correlation values are dimensionless with unit free and scale free measure of strength and direction between two variables.

For more info : https://www.excelr.com/blog/data-science/statistics-for-data-scientist/Correlation-vs-covariance

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Is data science applicable in today’s world? This is a question that people could face when they choose data science as their career. With data being generated in huge amounts in every business, it’s becoming essential to understand data and use it to improve the performance of a business. Hence, this is where data science comes into play and allows people to understand and extract the patterns, meanings and insights from data. Currently, data science is playing a pivotal role in large organizations who wish to thrive using technology. Therefore, here are the applications of data science and its impacts on the progress of different organizations.
E-Commerce
E-Commerce has become the most popular and thriving industry of this generation. Top companies like Amazon, Flipkart, eBay, etc. are using [b]data science course to understand their customers and the latest product trends. The popular ways that data science is being used in e-commerce platforms are as follows:
With machine learning, e-commerce platforms understand the latest trend in products among their different customers. With this, the product recommendations for the customers are tuned based on these trends.
Data science allows us to understand the analytics and behaviors and thus enables us to predict future trends.
With data science, e-commerce platforms understand their customer base and then change the pricing of the products based on the customer’s usage patterns.
Data science is also being extensively used in collaborative filtering where e-commerce platforms understand the usage patterns of customers and filter their recommendations based on their historic purchases and the purchases made by the customers of a similar kind. This enables product-based and collaborative recommendation systems which allows e-commerce platforms to provide better services to their customers.
E-commerce platforms also make use of sentiment analysis which is part of machine learning to understand the feedback given by the customers on different products or fraud merchants. Surveys conducted by these platforms allow them to understand the opinions of different customers using natural language processing.
Transportation
This is another field where data science has made its mark. In the transportation sector, data science is being used to create a safe environment for the driver and it’s also used to check the performance of a vehicle. With data science, it’s possible to track the usage of a vehicle. Also, it allows companies to keep track of the performance of vehicles. Moreover, with the rise of self-driving cars, data science has increased its influence in the field of transportation. Through general analysis, data science has allowed us to know the fuel consumption, driving patterns, and vehicle resources which can lead to the betterment of the transportation sector through its continuous improvement.
Also, companies like Uber and OLA use data science course to optimize the price and customer experiences based on their past experiences. By using predictive tools, these companies can predict the parameters such as weather and traffic and change the price and the customer experience.
The self-driving cars are a hot topic today, which extensively use data science for various purposes. Using data science, machine learning modules are implemented into these self-driving cars which allows them to know their current location, environment, etc., and thus make the required decisions.
Finally, we can conclude that data science has its mark in almost every field that exists. It has successfully shaped the future of every organization with its huge advantages and it will continue to have an impact on every field.