What Is Data Science?

Data is a part of all of our lives in some way. The computers we use every day generate data when we use them. Businesses collect data from customers to provide a better customer experience. Governments collect data from citizens to learn about their population.

What happens with all of the data that is collected? The answer lies in the data science industry. Those who work in the data science industry, from data engineers to analysts, collect data and use that data to answer questions and find out more about particular problems.

In this guide, we’re going to answer a question that often goes overlooked: what exactly is data science? We will describe in depth what data science is and why it is important. We will then look at a few use cases of data science and what data science workers do.

What Is Data Science?

Data science involves using mathematical and scientific principles to draw conclusions for hypotheses. A data scientist will be given a question to answer or a hypothesis to prove; they must collect, analyze, and interpret data to prove or disprove that hypothesis. Data scientists use programming, visual tools, statistics, and mathematics to find answers to questions from data sets they analyze.

Data science is an umbrella term. Within data science, there are multiple fields, such as:

• Data analysis
• Data cleaning
• Statistical analysis
• Data engineering
• Machine learning engineering

All of these fields are built on the same fundamental principles of using data to draw conclusions, but each plays a different role in data science. Data cleaning involves preparing data for analysis, for example. Data engineering involves creating systems to collect and store data.

The Data Science Process

All data scientists follow a similar process in their work. This helps create consistency in data studies, which is crucial if, for example, a business only has one chance at collecting the data they need for a particular purpose.

In data science, the following steps are followed:

• Planning: A study must be planned. Analysts must figure out what data they will need to prove or disprove a hypothesis. This stage often happens in consultation with other members of an organization who have commissioned a study (e.g. statisticians, executives, marketers).
• Gathering data: A data scientist will then use their plan to gather the data they need. This may be done by using an existing data set, such as one from a previous study, a particular tool, or a third party.
• Data cleaning: Most data does not come packaged in the exact way needed for the study. Data scientists will “clean” (prepare) a dataset after it has been gathered.
• Analyzing data: Using statistical techniques, a data scientist will work to draw conclusions based on the data they have gathered and cleaned. This usually involves looking for patterns and trends in a dataset.
• Drawing conclusions: Based on the insights they have derived, data scientists must draw conclusions. These conclusions are usually presented in some kind of report.
• Visualizing: To make data accessible to more people, especially those without a technical background, a data scientist will usually create charts and graphics that showcase their findings.

Organizations usually have strict procedures for data science projects. These procedures are improved and built upon with experience. However, all procedures usually follow the steps above (or a variation thereof).

What Does Data Science Involve?

Data scientists usually oversee every part of the data science process, from collecting data to drawing conclusions. A data scientist will help plan out a hypothesis, find the right data to prove or disprove it, analyze that data, and then produce a report with their conclusions.

On a day-to-day basis, a data scientist might:

• Collect data from existing data sets.
• Plan ways to collect new data.
• Write programs that find patterns in existing data.
• Use statistical principles to learn more about a dataset.
• Remove erroneous values from a dataset.
• Work with other data scientists to figure out how to store data.
• Write reports based on their findings.
• Create graphics and visualizations with key insights.

Data science is called a multidisciplinary field for good reason. Reading the list above, you can see that data science operates at the intersection of a lot of different skills. For instance, to create graphics you need to know a bit about design and representing data. To analyze a dataset, you need to feel comfortable with statistics.

When is Data Science Used?

As technology advances, so too do the applications of data science. We now have more advanced ways of collecting and analyzing data. This means more people can make use of the data they collect.

Here are a few situations in which data science is used:

• By governments to analyze information from national censuses.
• By big shopping sites to view trends in purchasing.
• By social media companies to measure engagement on social media posts.
• By local governments to identify trends in spending.
• By financial services companies to detect fraud.
• By insurance companies to identify and mitigate risk.
• By search engines to view trends in queries over periods of time.

These are only a few of the many examples where data science is used. In short, data science is used almost anytime businesses have data that can help answer questions. Data science is also used by governments and even individuals for the same purpose.

Data Science Career Paths

As we said earlier, data science involves a lot of different tasks. Often, a business will hire different sorts of data professionals to handle their data science needs. Some businesses hire data scientists to cover all data-related tasks but larger organizations often need bigger teams to support their needs.

Here are a few data science career paths that you can pursue:

• Data scientist: A general data professional who conducts data science studies.
• Data analyst: A specialist who focuses on analyzing datasets.
• Big data engineer: A professional who develops and manages infrastructure that stores big datasets.
• Data engineer: A specialist who develops infrastructure for regular data collection and storage (not big datasets).
• Machine learning engineer: A specialist who works with data to teach systems to identify and learn from patterns.
• Artificial intelligence engineer: A specialist who aims to create intelligence systems that can make decisions.

Most people begin a career in data science either as a data scientist or a data analyst. Both of these positions will give you exposure to a lot of the data science process. Fields like data engineering and big data require a lot more training and are often reserved for people who have spent some time working with data.

Data Science Career Salaries

Data science is one of the best-paying jobs in technology. This is because data science incorporates a lot of different fields and not many people have all the skills that a data professional must have.

Taking the career paths we mentioned earlier, here are a few average salaries for top data science career paths. Our data was collected from Glassdoor.

• Data scientist: $113,309
• Data analyst: $62,453
• Big data engineer: $102,864
• Data engineer: $102,864
• Machine learning engineer: $114,121
• Artificial intelligence engineer: $114,121

As you can see, data science professionals are well compensated. Some people are even paid upward of $100,000 for their work. As you acquire more experience in the data science field, your salary may improve, too.

The Importance of Data Science

Data science as a field shows no signs of slowing down. This field has helped businesses unlock new ways to make informed decisions. Data science practices have also made it easier for businesses, governments, and individuals to derive insights from data they have collected.

Data science as a field is about collecting, analyzing, processing, and presenting data. The presentation part is especially important. Using data science, professionals can turn data that was previously very difficult to interpret into easy-to-read charts. Whenever you see a chart, you can bet there was probably some sort of analysis involved (even if that analysis was just in Excel).

Data science is a lucrative career path not only because of the salaries but also because of the possibilities in data work. You could help governments make data more accessible to citizens. You could work with machine learning algorithms to build cutting-edge applications. Or you could analyze data and come up with exciting new insights.

If you’re thinking of a career in data, data science is a field worth researching in depth.

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