Data science has quickly become one of the most popular fields to work in. Glassdoor.com lists data science as #3 on their list of ‘50 best jobs in America for 2022’, while Business Insider rates it at #2. With that in mind, here are the top 10 interesting facts about data science.
1. Data needs to be cleaned:
Data analysts and data scientists sort through data daily. We might assume that the data is clearly organized into useful information. That is not always the case. A big part of their job is cleaning the data. The initial data is often inaccurate, duplicated, or incomplete.
2. Data science is used in every industry:
Data science can be used in all fields, from research to healthcare to retail to government to sports. For example, data scientists have helped to create a script that will analyze cardiac data, giving researchers a better understanding of complex arrhythmias. An arrhythmia is an irregular heartbeat, and it is a very common problem. With over 3 million cases in the U.S. each year, having technology to help study it could be revolutionary. Data is also used in areas like entertainment, as Spotify and other music streaming services use analytics to create music playlists curated just for you. Data science is everywhere. Yet another example is the financial industry. Data science is used to help measure risk assessment, potential fraudulent behavior, and customer analysis, to name a few. The Gaming world has also embraced the use of data science. In order to build models or identify patterns, data science is needed. But it doesn’t stop there. Data science is also used to analyze consumer behavior in order to help companies increase their profits.
3. Communication is key:
Many people might think that data scientists work solo, but the truth is that being a data scientist involves a lot of interaction with others. They often must communicate with other co-workers in order to fully understand a problem before they can brainstorm for a solution. Being able to communicate with people is an important skill in this field.
4. No degree required:
A lot of times when we think of science, we think of scientists in a lab. Data scientists do analyze data, much like a scientist would analyze chemicals in a lab, but this field does not require a doctorate. Data scientists can come from many different backgrounds; there is no need for a fancy degree.
5. Data science is not just Excel sheets:
When people think of data scientists and data analysts, they often think of people creating extensive Excel sheets. While data scientists definitely make use of Excel, they also use many other data analytics tools. SQL queries, statistical analysis, and predictive analysis are just a few of the tools they utilize. Also, with the availability of programming tools like Python and R, data scientists no longer need to make calculations exclusively with Excel.
6. Not all data is being used effectively:
Often, we think of data as having a useful outcome, but the truth is that some data is what we call “dark data”. Dark data isn’t necessarily data that is useless; it is often data that companies collect in order to satisfy basic requirements. For example, a company might collect information about sales, inventory, and losses. They may even have a social media presence. But if the company does not capitalize on this information, it would be considered dark data as the data is still untapped. Companies need to shine a light on this undervalued data. Data scientists can assist with this by helping companies sort through their dark data and find what is most relevant.
7. Data science and machine learning are not the same thing:
Nowadays ‘data science’ and ‘machine learning’ are big buzzwords. But what do they really mean? Are they interchangeable? The answer is no, they are not the same. Data science is the process of extracting useful data, while machine learning is the process of giving computers the ability to learn from the data without being explicitly programmed.
8. Big Data is just a LOT of data:
Big Data is a term tossed around a lot these days. It simply refers to large, hard-to-manage volumes of data. Examples of big data are things like social media data, Google searches, and Waze directions from all over the world. These are all considered big data because millions of records are created every minute from a simple thing like a Google search. However, many people don’t realize that the data that is gained can’t be used automatically. When using big data, a company needs an expert analyst to apply the best modeling practices in order to work with it properly.
9. Python is #1:
Anaconda.com — the world’s most popular data science platform — did a survey in 2020. The survey was very extensive, questioning people of varying ages, industries, and job functions. They found that 75% of the survey respondents used Python for data science work either “always” or “quite frequently”.
10. The data science field is always changing:
Part of the reason that data science is such a great field to go into is because it is constantly evolving. The field offers many niche professions, such as artificial intelligence, and big data. Because the field is constantly shifting and growing, there will be more and more job opportunities. 10 years ago, we couldn’t imagine what AI and big data were capable of. If you don’t like to be bored doing the same thing repeatedly, and want to keep learning new things, this could be the field for you.
I hope these facts give you some insight into the big world known as data science. Data science has re-envisioned the way that humans function in today’s society. Can’t wait to hear about the meaningful insights you create from data!