What is Data Science?

Data science is a field of study that seeks to solve problems based on available data and information. Data scientists use a combination of scientific methods, data mining systems, and algorithms to glean insightful knowledge to create action steps for change.

This field of study, which touches on statistics, data analysis, machine learning, and more, is quietly shaping our world. The wide-ranging field of data science is embedded in most aspects of our modern life. It works by uncovering useful information hidden within the treasure troves of data collected regularly.

Why Data Science is Important

Organizations may collect a mountain of information for a variety of reasons. But without the help of data scientists, it would be difficult to glean any helpful insights.

The real power of data science is that it can take seemingly unconnected points of information and create a deeper understanding of the problem at hand. With that deeper knowledge, data scientists can guide organizations to make the most efficient decisions possible.

How is Data Science Used?

Organizations can use data science to solve a variety of issues.

The goal of the analysis is to detect any existing patterns and relationships. Any trends discovered may point the scientist towards a resolution to an issue or an improvement on the current processes.

Currently, data science is at the heart of many organizations seeking to improve their customer experience and solve tricky problems. A few examples include combating tax fraud, improving healthcare recommendations, and optimizing shipping routes on the go. But the applications for data science are almost limitless.

Can Data Science Predict the Stock Market?

Data science can help solve a wide range of problems. But does that mean it can predict the stock market?

Currently, data science can help predict the business financials of a particular company with more accuracy than human Wall Street analysts. However, data scientists have not been able to predict the stock market accurately consistently.

The complexity of the stock market and the sheer amount of data that can impact it has created a difficult task for data scientists. But that doesn't mean people won't try to harness the power of data science to predict the stock market in the future.

Data Science Example

Want to see how data science plays out in the real world? Let’s explore an example.

Major credit card issuers are using the power of data science to combat fraudsters. These companies have used thousands of data points to create an anomaly detection system. When a fraudster attempts to use the card in a way that doesn't match up with the data science powered algorithm’s expectations, the credit card company can flag the transaction.

Although no system is completely perfect, the use of data science has been an effective tool to avoid credit card fraud.

What does a Data Scientist do?

Typically, data scientists will be presented with a problem by an organization. After defining clear metrics, the scientists will scour the available resources to uncover any relevant data.

If there isn't current data on the problem at hand, the scientists will devise methods to collect the necessary information. If there is data surrounding the situation, scientists can dive straight into the details.

With the data in hand, scientists can develop models to analyze the information. Based on trends and relationships in the data, the scientist can uncover actionable insights to solve the problem facing a particular organization.

Along the way, data scientists will need to do the following:

  • Accurately define the problem. Without a clear understanding of the issue, it will be impossible to solve.

  • Acquire all the necessary data. A data scientist without data cannot solve a problem.

  • Clean the data. Data sets will need to be processed into a standardized format with any errors removed.

  • Create models to explore data. The data will need to be explored for relationships and patterns.

  • Use insights to solve the problem. Once the relationships within the data are uncovered, it is time to solve the issue at hand. At this point, there may be a lot of trial and error to determine the best course of action.

  • Present the information. The organization with the issue will need a clear description of the problem and the solution the data scientist recommends based on the data.

Data scientists will need to be comfortable with statistics, modeling, and critical thinking throughout the process.

Examples of Data Science Jobs

In the field of data science, you may find many different job titles. A few include data scientists, data architects, data engineers, and more. Although the title may vary, the problem-solving nature of the job will remain relatively similar.

Data Scientist Salary

On average, data scientists should expect to earn around $115,000 per year. However, according to Glassdoor, the spread ranges from $81,000 to $162,000.

As with most industries, you'll likely find that you can command a higher salary with more experience.

How to Become a Data Scientist

Interested in becoming a data scientist?

You should start by earning a bachelor's degree in computer science, math, physics, or a related field. At that point, you may choose to pursue a master's degree in data or a similar field. However, an advanced degree is not a requirement for most data scientist jobs.

At the moment, the educational opportunities for future data scientists are expanding. Recently, the University of Virginia opened a new school of data science. The goal of the programs offered is to provide students with a clear path and the skills they need to excel in the field of data science. As the field continues to grow, you may find more finely tuned educational opportunities around the country.

Once you've completed your education, you should begin to gain experience in your field of choice. The good news is that data scientists are in demand in multiple industry sectors. With that, you can pursue something that interests you within the realm of data science.