When Data Gets Wild — How to Handle it

Author(s): Rijul Singh Malik

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

When Data Gets Wild — How to Handle It

A blog on data wrangling and handling a messy dataset.

There’s always more than one way to wrangle data.

Data has grown exponentially in the last decade, thanks to the internet of things, social media, and the rise of smartphones and wearable technology. But how do you get the most use out of the data you collect and process? In the article, we highlight some of the ways you can use data to improve your business.

When you’re looking at data, you have a lot of different ways to work with it. You can use it to make charts, you can use it to make tables, and you can use it to make infographics. You can even use it to make other data. But sometimes, you don’t just have data. Sometimes, you have “wild” data. Data that looks like this.

Data wrangling can be a big pain. It’s boring, time-consuming, and, worst of all, it might introduce errors into the data you’re working with. There are so many different ways to wrangle data, too. You can use spreadsheets, code, or even a pen and paper. So, how can you wrangle your data, so it’s both accurate and quick?

Make more sense of your data.

Data is an inevitable part of your business. You rely on it to make decisions and track your progress. And although it can be a little intimidating, it doesn’t have to be. There are ways to make your data more manageable so that you can make the most of it. Here are a few tips to get you started.

1. Make your data useful. Just because you have data doesn’t mean you can use it. Make sure you’re collecting data that will actually help you achieve your goals.

2. Be selective. Don’t collect data just for the sake of collecting it. More isn’t always better.

3. Correlate. When you’re collecting data, look for patterns and trends.

4. Filter. Just because you have data doesn’t mean you have to analyze it all.

Reshape and reshuffle your data set.

When you start exploring your data, you will be amazed by how much you will be able to find. You will see the data in a whole new way. Think of it — almost every website out there has a wealth of information on it, and you have the opportunity to analyze that information! This can be really useful for you as a blogger, as you can use that information to discover new things about your potential customers. You can use this data to find out what your readers want, what they’re interested in, and what they like. You can also use it to find out what your competitors are up to.

Data is powerful. It can be used to help you find some of the most exciting trends, the biggest opportunities, or the most in-demand products for your niche. However, there’s a problem. What happens when you find a fantastic data set that has a few points that don’t make sense? What happens when your data is all over the place and you have no idea how to use it? It happens to the best of us. We’re here to help. With this blog post, we’ll take a look at some of the most common data problems and how you can reshape and reshuffle your data set to make it work for you.

“Creating data visualizations is hard.” And I found this to be true when I first started out making data visualizations. There is a lot to learn, and it’s overwhelming. I’m hoping that this article will help to provide you with some guidance on how to approach your data visualizations in a structured manner.

Presenting the data you’ve wrangled with R

Data wrangling is an extremely important part of the data science process. Collecting data from various sources and then wrangling it into understandable structures is what separates a data scientist from a regular computer programmer. However, no matter how good you are at wrangling data, it can be extremely difficult to present that data in a way that is easy to understand. There are a number of different methods used to wrangle data. Some of the most popular include database management systems like MySQL and PostgreSQL, data analytics software like R and Matlab, and data visualization platforms like D3.js and Tableau. In this blog post, I’ll be discussing how to use the R programming language to organize and present data.

Data is messy. No matter how clean your data is, it’s bound to get dirty. That’s a fact of life that we all learn to accept. However, it doesn’t mean that you should accept it just because you’ve been told to. The good news is that data wrangling is a skill that can be mastered. You may not be able to make your data perfect, but you can learn how to make it cleaner and more usable.

Conclusion:

Through data wrangling, you can make sense of your data, transforming a messy dataset into an organized one that gives you actionable insights.

When Data Gets Wild — How to Handle it was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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