Data science is a rapidly growing field that shows a lot of upside. On the one hand, it’s expected that there will be a lot of jobs in data science available in the coming years. On the other hand, many data science jobs pay quite well.
So, what does this mean for you?
First, majoring in data science will prepare you well for a good job upon graduation. For some, it might even mean that you have a job waiting for you before you graduate.
Second, by entering such an in-demand field, you could find yourself in the position of choosing among multiple job offers. Being able to weigh salary, benefits, the company for which you’ll work, and the location in which you will work is an enviable position to be in.
Third, many new data scientists enjoy good job security. As mentioned earlier, this is a rapidly growing field with many job openings. In fact, the Bureau of Labor Statistics estimates that job growth in this field will be 22 percent through 2030. With so many jobs becoming available and new jobs being created, it’s reasonable to assume that you will have long-term prospects with good wages.
What you’ll be paid to do is work 40 hours per week during business hours in an office environment. However, they may have to work longer hours at critical points in a test or experiment with a data system.
Of course, before you begin work as a data scientist, you need to do your due diligence about this line of work, the educational requirements, and what you might be expected to do on the job.
In this guide, we’ll delve into the typical job duties that are assigned to data scientists. Keep in mind that the duties discussed below represent just some of the common tasks that data scientists usually do at work. Bear in mind as well that the specific duties of a data science job will vary somewhat from one employer to the next.
Generally speaking, though, data scientists work on new and innovative approaches in order to make the most of technology and its ability to handle large amounts of data. They spend most of their time at computers, but they may also spend some time discussing ideas with colleagues, researching theories in books and online, or meeting with clients or supervisors about their projects.
The Primary Role: Make Sense of Data
Let’s begin by exploring the primary role of data scientists.
In very general terms, a data scientist’s charge is to make sense of data. Businesses and organizations collect vast amounts of data from many different resources, but that data is useless to them if they don’t have someone that can turn all that data into actionable intelligence.
That’s where a data scientist comes in.
On a very basic level, data scientists collect, organize, clean, analyze, and report on data. These processes are undergone to accomplish all sorts of different tasks.
Data scientists seek to identify patterns or trends in data. It’s their job to figure out what the correct data sets are, what the variables are, and ensure that the data collected is accurate and uniform. They analyze the data, interpret it, and identify how the data can help solve problems. Likewise, data scientists look for ways that data can open new opportunities.
Of course, data scientists must put all this information into a format that’s easy for people to understand. So, a data scientist might use data visualization tools that summarize the data and paints a clear picture of its meaning for laypersons. A data scientist that works for a financial firm might create an image-based presentation to the board of directors to communicate the meaning of the data that they’ve analyzed. This is just one example, though.
As another example, a data scientist that works in the healthcare industry might utilize data analysis to determine a more efficient way of providing post-operative care to patients. Meanwhile, a data scientist that works for a security company might use data modeling to identify new and emerging security threats.
The manner in which data scientists go about making sense of data for their employers varies widely. From using modeling and statistics to computer science and mathematics, there are a host of tools at the disposal of data scientists that help them transform data into something that businesses and organizations can use.
Solve Complex Computing Problems
Solving complex computing problems is how data scientists spend a lot of their time. They might look at fundamental problems or new issues in computing and experiment with different methods of addressing those problems.
For example, a data scientist might work to solve a scalability problem for a company that wants to upscale its computer systems. Specifically, they might work on developing a means of having scalable architectures that allow for faster processing of big data.
Similarly, a data scientist might be asked to make recommendations regarding security or privacy issues for a company’s network. This might take the form of researching how to maintain privacy for large-scale datasets in different regions of the world. This is necessary because privacy concerns and regulations regarding private data vary from one area to the next.
Invent New Data Analysis Tools and Methods
Data scientists may invent new types of data analysis tools in order to improve the computing experience for other people.
For example, this might include creating more efficient codes or instructions so that a computer or app takes less time to start or process data inputs.
As another example, a data scientist might create new computer languages or add functionality to existing programming languages based on innovations in new types of computer hardware.
Data scientists may also create and test new methods for data analysis. For example, there are four main types of data analysis: descriptive, predictive, diagnostic, and prescriptive. A data scientist might develop a new descriptive method of analyzing data that offers end-users a greater level of insight into what information is contained in the data.
See Also: Job Profile: Data Analyst
Develop and Improve Computer Software
Many data scientists spend a significant amount of their time developing new computer software or new functions within existing software. They may do this as technology advances, which happens quite rapidly at times.
For example, as computer processors change, a data scientist may make updates to an existing piece of software so that it can do the same task in less time by harnessing the power of an updated processor.
As another example, data scientists may work on improving the user experience of software or applications for computers or mobile devices to take advantage of updated features in new hardware.
While it is usually software engineers that handle the development and improvement of computer software, it is becoming increasingly common for some data scientists to take on this role as well. In fact, some have argued that software development skills are one of the most important skills that a data scientist can have.
When you consider the job tasks that are related to computer software, you begin to see why this is the case.
For example, as noted earlier, data scientists are responsible for cleaning data to remove errors from data sets. To manually clean data would be a nightmare, especially given the enormous amounts of data that are collected and need cleaning. So, a data scientist that understands software development could devise software that allows them to clean data more quickly and easily while minimizing or eliminating errors.
But even if a data scientist understands how to use software (and does not necessarily understand how to develop it), they will have a leg up. For example, having detailed knowledge of how to use programs and languages like Hadoop and Python allows a data scientist to analyze huge amounts of data. Again, doing so minimizes the time required for analysis without sacrificing the ability to clean and organize the data effectively.
Design Experiments and Analyze Their Results
Another key job duty for many data scientists is designing experiments on computers. As a basic example, they might create a new line of code and apply it to a data set in order to see what happens. They might also try a new data analysis technique to see if the results are similar to an old technique for analyzing data.
A data scientist may also design and conduct experiments on different software systems in order to see how an operating system behaves. Likewise, they might examine whether a software program can handle a certain type of analysis.
Of course, as with any type of experiment, data scientists must also create documentation of the experiment’s design, results, and analytical procedures. Not only is this important for evaluating the work that’s been done, but having proper documentation allows other people to try to replicate the results.
In many situations, data scientists use AB testing in experimentation. A simple example of an AB testing approach is to alter the size of text on a website to determine if larger text results in more clicks of a button. While a data scientist doesn’t spend all their time conducting experiments, it is certainly one of their primary job duties.
In some cases, data scientists conduct research that seeks to solve a problem for their employer. For example, a large retailer might turn to a data scientist to research ways to improve customer engagement.
In other cases, data scientists might conduct more academic research. This is more often done by data scientists that work in an educational setting, such as a college or university. An example of academic research might be an investigation of how to combine multiple sources of data into a singular model that outputs useful results.
These are but two examples, but the takeaway is that since data science is such a vast and rapidly growing field of work, there is a wealth of subject matter on which data scientists can spend their time in research.
Publish and Present Their Findings
According to the Bureau of Labor Statistics, another common duty of a data scientist is to write, edit, and publish their research findings. They may send their results to one or more journals for publication. Some data scientists may volunteer as peer reviewers for other data scientists or professional journals. They may also present their findings at professional conferences or meetings.
Whatever the case, the purpose of publishing and presenting findings is to add to the knowledge base of data science. As mentioned earlier, this is an enormous field with many, many avenues of research, so adding to the body of understanding by publishing one’s findings is crucial for advancing data science even further.
Is Data Science Right for You?
When deciding whether to pursue an education in data science, it’s important to consider as many variables as you can. Do you enjoy the subject matter? Are your particular skills a good fit for this position? Do the salary and employment outlook bode well for your future career?
Of course, there are other factors to consider, too. For example, there are many different areas of specialization in data science, each of which can have a significant impact on things like the knowledge and skills you need, the demand for workers, and the salary you might make. So, this means that you’ll want to compare different jobs and their duties within the data science realm so you can choose the right direction for your future.
The list of specialties is not a small one, either. Here are just a few examples:
- Data architect
- Data mining engineer
- Data analyst
- Machine learning scientist
- Hadoop engineer
- Database administrator
Beyond that, there will likely be differences in job duties from one place of employment to the next. Meaning, a data architect that works for an automobile manufacturing company might be asked to take on different duties than one that works for a defense contractor.
Now that you know about some of the basic duties of a data scientist, the next step is to more clearly define what you want to do in the data science field and set about learning what you need to do to make that dream come true.
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