Breaking Down 7 Data Science Myths

Data science is a field of interest for many people, including those who want to have a career in applied statistics. Whether you employ data scientists and statisticians in your business or plan to pursue a career as one, it’s important to separate the myths from the facts. Here are 7 data science myths to bust, once and for all.

data science myths
Shutterstock Licensed Photo – By Wright Studio | stock photo ID: 1247255884

Myth #1: Data Science is Only for Big Corporations

Not so! Smaller organizations can benefit from data analysis. The same is true of government agencies and other types of organizations, including hospitals and non-profits. For instance, they might want to look at data trends for medical research purposes or boost fundraising efforts.

Also, there does not have to be a big amount of resources available by the organization to do the work. While having the latest technology would likely help make the work faster and easier, the main thing is getting skilled professionals for the project at hand.

These professionals will work efficiently to get the work done. They will provide accuracy for the results that is desired.

Myth #2: Data Science is Hard to Include in Business Workflow

False. Technology has helped to overcome this challenge.

Rather than using a software system that is hard to integrate with other systems, there is now a range of software systems that use one programming language. The result is an easier workflow in the organization as they can communicate well with one another.

Myth #3: Building Models is at the Core of the Job

Many people mistakenly assume that a data scientist builds models all day. However, there is a lot more to their workday than that.

A few examples of activities involved in a project are data collection, natural language processing, and verification. The actual building of the model is only one stage in the project.

Myth #4: Artificial Intelligence will Replace Data Science Soon

This statement is another falsehood. While automation has become a part of data science, there are several facets that are integral for human professionals to do.

Although repetitive tasks are ones that artificial intelligence (AI) can do, data scientists and statisticians must carry out complex operations and instruct the machine on what to do. The need for human guidance will not dissipate altogether.

Furthermore, data scientists must continue to develop new analysis tools. They will be needed to operate a big part of the systems for a long time into the future.

Myth #5: Data Science is a Field with Slow Growth

This one is among the most frequent ones on the list of misconceptions. But the reality is quite the opposite of a slow rate.

There is a high demand for statisticians and others who work in the data science field. Job growth for mathematicians and statisticians will increase by 30% by 2028, according to estimates by the US Bureau of Labor Statistics (BLS).

The heightened need for these workers is higher than projections for the average occupation, as per BLS. Given the increasing amount of electronic and digital data that companies are using, the fast growth makes sense.

In other words, data science is not a fad or trend. It is here to stay.

Myth #6: Just as Many Positions are Available with a Bachelor’s as a Master’s Degree

Not true. According to the BLS resource mentioned earlier, statistician and mathematician jobs typically require at least a master’s degree. Find more about why data matters and the benefits of a master’s program by clicking here. While there are some positions available with a bachelor’s degree, the BLS states that the expectation is usually for a master’s level of education.

Myth #7: Being a Great Programmer is Essential to the Job

False! While computer knowledge is key to a data scientist’s career, there is not an expectation that this individual be an expert programmer.

Regarding the daily tasks of the data scientist, there is no in-depth coding required. Instead, algorithms are usually available and simply need a few small changes made to them.

With that said, you do need to be a logical thinker to do well in this profession. Good communication skills and a passion for learning will also get you far in university and after graduation when you are in the workplace. Business know-how is also an asset.

Clearing Up the Misinformation

Data science is an exciting field, but conceptions about it may keep some people from pursuing it as a career. The myths can also prevent companies from employing those who have a data science degree because they do not understand what the role entails.

Now that you know the misinformation and the realities, it’s time to consider whether this career trajectory is right for you. If you want to work in an in-demand field and collect important information to help guide business decisions, then working in this field is something to consider.

By taking an Applied Statistics Master’s Program, you can graduate with the skills and knowledge to practically apply stats to a range of applications. Being able to practice skills during a degree program is very valuable. Plus, an online program has a lot of flexibility, which means you can continue to work while going to school, as well as maintaining other responsibilities.

After graduation, you can expect to predict sales for your company, measure employee performance, analyze investment opportunities, and more, depending on the role you take on there.

Concluding Thoughts

Translating data is important work, as you can see by now. It is critical to organizations across a range of industries.

When it comes to the area of data science, there continue to be misconceptions that keep people from studying it in school. However, now you have the accurate details to know the value of data science.

When you have the details and truth, then you can paint a clear picture of what is data science. In the future, challenge stereotypes and misconceptions about other topics too in the search for what is accurate, which can help make the world a more informed place.