Learning From Three Big Data Science Career Mistakes – Starweaver
Learning From Three Big Data Science Career Mistakes

Learning From Three Big Data Science Career Mistakes

Learning From Three Big Data Science Career Mistakes:

The field of data science has gained in popularity over the past few years. It seems like this is the buzz word, one that many who want a technical career will look into and consider for themselves. However, there are several data science career mistakes that everyone should watch out to make sure they keep the interests of their data science career in mind. Some of the top career mistakes for data science include:

Ignoring the Importance of Social Media and the World Online: Data Science Career Mistakes

It is easy for a data scientist to get caught up in all the numbers and the facts of what they are presented. They want to look at the here and now and see where the data points them. While this isn’t necessarily a bad thing, when we forget to focus on how important social media is to our customers and our business, and we don’t focus on how much the internet is expanding, we lead ourselves right into trouble.

In the past ten years or so, the internet has grown by leaps and bounds and is not available in almost all parts of the world. This means that people are connected in ways that were never possible in the past through social media and other methods. Even if a data scientist does not use social media that much themselves, we must realize that one of the best ways to reach our customers, and to understand what they need and when is through social media.

People throughout the world are now connected like never. Communications that may have taken weeks or months in the past, depending on the distance, can now be done in seconds. This is a benefit and a negative. When you do something amazing, and your customers love it, you will see the results of that in just a few minutes. On the other hand, one negative review can go viral in a few seconds as well.

The growth of the internet and social media also means that data scientists will need to spend more time here for their work. A good percentage of the data that a company needs to gather and analyze will come from social media. Most of the rest will come from somewhere else online, including surveys and emails sent to the customer. Recognizing this and utilizing it will make a difference in whether the data scientist has the right data they need to succeed.

Not Understanding How the Industry Changes

The world of technology is changing all the time. It would be nice to make it five years and still use the same techniques and even the same programming languages, but this is not possible any longer. Technology changes too quickly, and what was once a popular and viable option now makes no sense to use at all.

For a data scientist, this is important to know. If they can’t keep up with their work and the changes, they will soon get left in the dark. Keeping up with changes in programming languages, such as the change from the traditional C programming language to Python, which is more comfortable and more versatile to use, is essential.

While some data scientists still use C, it may be worth your time to learn more about Python and how it works to open up new doors in data science as time goes on. At some point, another language will emerge as the top dog, and you need to know that.

And since the world of machine learning is so new and there is still so much that is unknown about it, you should consider making it your goal in your data science career to keep up to date. It is likely that the algorithms, the methods, and the processes you use today scratch the surface. Failure to keep up on all of this will only harm you in the long run.

 

Not Seeing the Difference Between Statistics and Data Science

If you are going into data science because you think it is a more profitable career than statistics, you will be disappointed. These two things are entirely different from one another as you dig into them, though they may seem remarkably similar when you first get started. Yes, both work with data and numbers, but they do so in quite different ways.

For example, when you look at statistics, you will see that it handles what is known as small data over a long period. You will find that with statistics, you will use data models most of the time. This will lead to questionable conclusions, a theory that is not relevant and can cause some problems when a business decides to use this information.

On the other hand, data science is going to work with what is known as big data over the short term. They will not focus on a handful of data points and hope that it works out. Instead, they will take large amounts of data in the here and now, and use algorithms to figure out the patterns. In a data science career, your goal is to use all of this data to help a company make essential decisions on how to grow, reach customers, and more.

When a company wants to learn more about reaching the customer and coming up with solutions that actually work and work well now, then a data scientist is the best option. And a data scientist should not try to fit into the role of a statistician. Both are different roles, and trying to combine data science, which is unique and better in most situations, can be a bad thing to work with.

When it comes to data science career mistakes, there are many that someone new to data science needs to be aware of. Knowing these mistakes can help you avoid them, helping you get ahead of the game with your new career. Knowing your audience, knowing the differences between different industries, and keeping up to date on the best techniques and topics of the industry will serve you well as a data scientist.

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