Remember that first line plot of ‘Volume’ data above. As we discussed before, it was too busy. It can be fixed by resamp

In data science we can use the r value, also called Pearson’s correlation coefficient. This measures how closely two sequences of numbers( i.e., columns, lists, series, etc.) are correlated.

The r value is a number between -1 and 1. It tells us whether two columns are positively correlated, not correlated, or negatively correlated. The closer to 1, the stronger the positive correlation. The closer to -1, the stronger the negative correlation (i.e., the more “opposite” the columns are). The closer to 0, the weaker the correlation.

I will use the monthly data that was calculated in the beginning. This time I chose bar plots. It shows the percent change clearly. There is a percent change function available to get the percent_change data.

In the ‘Volume’ data we are working on right now, we can observe some big spikes here and there. These types of spikes are not helpful for data analysis or for modeling. normally to smooth out the spikes, resampling to a lower frequency and rolling is very helpful.

Rolling is another very helpful way of smoothing out the curve. It takes the average of a specified amount of data. If I want a 7-day rolling, it gives us the 7-d average data.

So, this article will only deal with the resampling of lower frequencies. Though resampling of higher frequency is also necessary especially for modeling purposes. Not so much in data analysis purpose.

I don’t have to do this anymore. Remote-monitoring pacemakers were first sold to the general public around 2007; currently, the industry standard for remote monitoring involves routers paired via Bluetooth to wireless-enabled cardiac devices. These routers sit in a patient’s bedroom and run constantly, pulling data at regular intervals and transmitting it straight to their doctor via the internet. No phone calls and no magnets involved. Ideally, a patient never even knows their data is being collected.

The dataset below shows data on seven children. It has the following columns, weight, age(in months), amount of baby teeth, and eye color. The eye color column has been categorized where 1 = blue, 2 = green, and 3 = brown.

In the code above, .div() helps to fill up the missing data. Actually, div() means division. df. div(6) will divide each element in df by 6. But here I used ‘df.Close.shift()’. So, Each element of df will be divided by each element of ‘df.Close.shift()’. We do this to avoid the null values that are created by the ‘shift()’ operation.

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hate Joe Biden. He does nothing to inspire the visceral anger and fear I felt toward his predecessor. He’s the generic, semi-competent white man I wound up with after my hopes for several better Democratic candidates fell through. For most of the Democrats and left-leaning voters I know, Joe Biden is just that: the last available option, the human safety school. He’s the nice-enough boy you go to the dance with when your true love asks someone else.

Datasets can tell many stories. A great place to start, to see these stories unfold, is checking for correlations between the variables. One of the first tasks I perform when exploring a dataset to see which variables have correlations. This gives me a better understanding of the data I’m working with. It’s also a great way to develop an interest in the data and establish some initial questions to try to answer. Simply put, correlations are awesome.

We aren’t going to explain the math behind the r value, but if you are curious, this youtube video does a great job. Instead, let’s visualize correlations with a simple dataset

Luckily Python has some amazing libraries which give us the tools we need to quickly and efficiently look at correlations. Let’s take a brief look at what correlation is and how to find strong correlations in a dataset using a heat map.

Correlation is a way to determine if two variables in a dataset are related in any way. Correlations have many real-world applications. We can see if using certain search terms are correlated to views on youtube. Or, we can see if ads are correlated to sales. When building machine learning models correlations are an important factor in determining features. Not only can this help us to see which features are linear related, but if features are strongly correlated we can remove them to prevent duplicating information.

Clinically, the benefits of remote monitoring are twofold: The patient doesn’t have to enter a medical setting to be monitored, which reduces the likelihood of iatrogenic disease — illness caused by the interference of the medical system. At the same time, doctors get more data than they’ve ever had access to, allowing them, ideally, a window to disease prevention. (I, along with many other patients, take issue with the second proposition, given that we cannot access our own data; there’s a substantial activist movement toward data liberation that includes cardiac patients who have fought for more than a decade to gain access to the information generated by wireless-enabled pacemakers and ICDs.)