Data science is a career that takes on a number of different areas of work including things like analyzing data, analyzing statistics, machine learning as well as basic computer science. If you are savvy with the latest technology on the market and you are good with data and numbers, this might be a very lucrative and exciting career path for you to take. Depending on what job you might be interested in, there are various paths you can take. Some will utilize your skills more than others so it is always a good idea to take a good look at your options and then decide your path from there. Let’s take a look at what you can do with a graduate certificate in data science.
A data scientist is very similar to a data analyst and depending on the company, these two are very interchangeable. You might be required to do things like pull data from a database, learn how to manage Excel spreadsheets like a master or produce data visualizations for people within your company. It can be helpful to have some knowledge of coding or programming, but data analysts don’t typically use these skills to the extent that data scientists do.
Data analysts essentially take a close look at a given company’s data and then draw important conclusions from it. Analysts make reports from their findings and help the company grow and improve with time. For example, a data analyst at a retailer may use purchasing data to identify the most common customer demographics. The company may then use that data to create targeted marketing campaigns to reach those demographics. Part of the challenge of this role is to formulate reports that explain data in a way that those outside of the data field can understand.
However, your exact responsibilities as a data analyst will vary greatly depending on your job description. In some cases, you might need to design and implement data-collection systems. In most cases, you’ll need to gather data, recognize patterns, and communicate those patterns to the rest of the company. Sometimes you may even be asked to train new hires to use data collection systems.
Recognizing patterns in data sets is arguably one of the most important skills for a data analyst to have. After all, data analysts are responsible for transforming data sets into something the company can use. Most data analysts regularly need to create visualizations of the data they collect. Visualizations can help other members of the company fully understand the insights you’ve discovered.
Generally, data analysts need to practice four kinds of analytics. Descriptive analytics identify something that happened (like a drop in sales). Diagnostic analytics look at why something happened. Predictive analytics determines what is likely to happen in the future. Prescriptive analytics recommend the next action the company should take.
Unsurprisingly, an eye for detail (and the patience to carefully examine the data collected) is essential in this role. Similarly, some level of business background can be helpful.
You might be surprised to learn that data analysts also typically have to collaborate with others. Parts of the job are solitary, but data analysts need to share their findings with others in a company. For example, a marketing team might benefit from knowing which ads were most successful, and a company’s salespeople will want to know which sales tactics work best in different situations. Executives will be interested to monitor the company’s progress as you track it through data. As a data analyst, you will likely need to collaborate with others in the data field, too. Your input will be helpful to database developers and data architects as they design your company’s data systems.
You can learn a lot from a job like this one. You will get started by using what you have learned at your university, but getting your feet wet and working is the way you can progress moving forward. In time, you may decide that you want to advance in the field. If this is the case, it may be worthwhile to pursue a master’s degree in data science.
Data scientists and data analysts frequently have overlapping responsibilities. However, the primary difference between the two is that a data scientist has a stronger background in computer science. Particularly in smaller businesses with limited employees, a data scientist may also take on responsibilities more commonly associated with data analysts.
To be a successful data scientist, you’ll need to have very strong skills in the areas of math and statistics. You’ll also need to be able to write code to more efficiently analyze data. Most data scientists also evaluate data trends and then make predictions based on them. Often, they develop algorithms to effectively model data, too.
Creative thinking abilities can be a great asset to a data scientist. Of course, much of the job is very technical, but data scientists need to be able to evaluate data for emerging trends and new opportunities for the companies they work for. And like data analysts, data scientists need to be good communicators — they’ll need to explain data findings to IT professionals and translate findings into layman’s terms for management.
Ideally, to work in this field you will need to be good at analyzing data and understanding what it is you are reading. You also need to be able to work with production coding. A data scientist usually fills a very specific role that a company feels they need someone for, so the job description may vary widely from company to company.
You will need some experience but you might be able to get this job just a couple of years out of school if you have enough educational experience, internship experience, etc. Ultimately, what qualifies you for the role of data scientist is your experience. A graduate certificate in data science is helpful, but the most successful data scientists will have a very strong background math, statistics, and computer science.
You might think that a data engineer is the same as a data scientist. However, while data scientists typically come from a mathematics background, data engineers come from a strong technology background. Data scientists are able to code and understand how software programs work, but data engineers need to be able to create, manage, and troubleshoot complex software in the data science field.
As a company grows, a data engineer is important because they will create the main data infrastructure that is needed in order to move ahead. Analytics come into play as well in order to find things that need to be improved and things that have been successful. You need very strong software engineering skills for this job as opposed to knowing how to really analyze statistics.
To succeed as a data engineer, you will need to have experience as a software developer. And while degrees in computer science can be useful, plenty of established data engineers maintain that you don’t need academic experience like you need real-world experience. If you have a lot of practice programming and enjoy doing it (regardless of whether you have a degree in computer science or not), you might find this job to be rewarding. Typically, hiring managers looking for data engineers want to see that you have a proven track record on larger data-related projects.
While the day-to-day responsibilities of a data engineer may vary, there are a few central job duties. One is maintaining the data pipeline. The data pipeline is a software system that gathers data from one area and transfers it to another area. If a data pipeline crashes or otherwise stops working, the data engineer will be the one to identify and fix the problem.
Another duty is to work with data scientists and data analysts. After all, if they don’t understand the data pipeline, the necessary data won’t be able to serve the company well. And lastly, these engineers control costs associated with data storage and moving.
It takes time to become a qualified data engineer– you’ll likely need a combination of education and experience. The best engineers will be familiar with different technologies and know when to use each one. This body of knowledge can take a significant amount of time to acquire — you can’t become a good data engineer overnight. It’s a difficult job to do, and it isn’t for everyone. But if you’re a great developer with a passion for data, it’s well worth looking into.
Machine Learning Engineer
For some companies, data is their main product. When it comes to data analysis, this can get overwhelming for just a small batch of engineers or data scientists. A large staff of employees is needed in order to sort through the massive amounts of data that are on hand so a data service can be provided.
In order to better handle enormous amounts of data, many companies are turning to artificial intelligence. In particular, machine learning, which is a type of artificial intelligence, is a vital tool for big data. But machine learning doesn’t happen automatically — it’s designed by machine learning engineers to automatically process data and turn it into something useful. Machine learning engineers design software that runs itself in order to automatically create predictive models. These predictive models can then be used to drive sales or otherwise improve a company’s performance.
For example, think about online streaming sites. When you first sign up for one, the site may recommend TV shows or movies to you. These first suggestions are rarely accurate. However, as you watch more videos, the suggestion algorithm gets more data points. As it gathers more data, the algorithm “learns,” and its suggestions become more accurate. And since the algorithm runs itself after creation, it simplifies the data collection process. Speech-to-text programs and image recognition algorithms are two other examples of machine learning in action.
Machine learning is a relatively new field, and you can’t (yet) earn a degree in it specifically. But like most jobs on the list, it requires excellent data skills as well as excellent programming skills. While you likely couldn’t get a job as a machine learning engineer with a graduate certificate in data science alone, this type of certificate could help you. If you’re someone who already has extensive experience programming, a data science certificate could give you the data background necessary to do well in this role.
At first glance, the role of a statistician may look a lot like the role of a data analyst or data scientist. After all, this job involves working with data on a daily basis. However, while data scientists and data analysts focus on gathering information from data and drawing meaningful conclusions, statisticians primarily focus on mathematics. In order to succeed in the field, statisticians need to be skilled and confident mathematicians. Data scientists (and to a lesser degree, data analysts) need to be more familiar with software development.
That doesn’t mean that statisticians and data scientists can’t work together, though. Since statisticians focus mainly on mathematics, they’re often responsible for designing polls and surveys, which can assist in the gathering of data for the company. They may also work alongside data scientists who create algorithms: the statistician can offer input for the equations needed in the algorithm, and the data scientist can build the algorithm itself.
Because most organizations need something in the way of statistical analysis, statisticians can be employed in a variety of different industries. It isn’t unusual for statisticians to specialize in a niche area like agriculture or education. However, because of the advanced math skills needed, you can’t become a statistician with only a graduate certificate in data science. If you have degrees in statistics and are looking to get into the world of big data, though, earning a certificate like this one is a good way to begin.
Data science is an exciting and developing field, and it offers a wealth of opportunities for the technically inclined. When you are applying for a job in the data science field, make sure that you read the description of the job very thoroughly. You might be more qualified for some jobs than others, and you can’t assume that a data scientist is the same thing with every job and every company that you encounter. Some of these positions are filling a very specific niche that you might not be qualified for just yet. The site Data Science Central shares the core skills that data scientists need, and is also a helpful resource.
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