Data analysis is a process of examining data and drawing conclusions from it. Data can be numerical or non-numerical and can come in many shapes and forms: audio, images, log files, and so on. This article deals specifically with numerical data that comes in tables (i.e., spreadsheets).

Main tools

The main tools used for this purpose are statistical software such as R or similar packages such as Python’s SciPy library. These programs allow you to load your data into memory and then perform various operations over it. To do the work you must hire someone with salesforce devops certification.

Some essential techniques

Before analyzing any data, though, you first need to make sure that the data is relevant! You don’t want to spend hours performing fascinating analyses only to discover that the dataset you used is flawed and tells you nothing. There are a couple of checks you can perform:

Ensure that your data covers the range of possible values (e.g., 0 to 1). If some people were asked to rate their happiness on a scale from 0 to 100, but one entry was -100, it would be an invalid result since negative numbers aren’t allowed on this scale.

For example, if one person didn’t answer a question about what gender they were, then we cannot draw any conclusions about that person because we don’t know which category they belong to. We also don’t know how much weight should be given to that person’s opinion when we draw conclusions based on the other entries. Some datasets should also ideally include results for every category (i.e., no missing entries).

Correct data

Finally, ensure that your data is correct. If a value was recorded incorrectly as 8 instead of 23, it could significantly impact your analysis! In this example, you might find something interesting because the number 23 has some unique properties, whereas 8 don’t. On the other hand, if you use 8 in all your calculations and conclusions, you will likely wholly misinterpret what happened here.

One important thing to remember is that numerical datasets are usually extremely sparse (i.e., they contain very few non-zero values). For example, if people were asked whether or not they had children, you would expect most of them to answer no. If they did answer yes, it doesn’t necessarily mean that they’re lying about not having children! There are many reasons someone might skip the question, so we’ll need to be careful when concluding.

A standard tool used for this purpose is clustering. Clustering involves splitting your data into groups so that each group represents some underlying pattern in your data. For example, you could look at how my friends’ ages are distributed and split people into five roughly even groups (e.g., 0-20, 21-35, 36-50…). You could then look at any patterns between these groups, like income or where they live.

Points to Note

An important thing to remember is that clustering doesn’t assume anything about your dataset, unlike regression which tries to describe the relationship between variables with mathematical formulas. For example, if you find that people who make more money also have more education, how did you conclude this? Did education lead to higher income, or was it something else?

Maybe you should look at age next since older people are usually better educated and might be making more money because they’ve been doing their job longer. By using a technique that makes no assumptions about your data (e.g., clustering), you can prevent yourself from drawing incorrect conclusions too quickly!