Key Responsibilities of Professional Data Scientists
- Analyzing Data
- Creating Data Models and Modules
- Processing Numbers and Numerical Data
- Presenting Information to an Audience
- Ensuring Accuracy
- Writing and Checking Code
- Application of Machine Learning
- Handling and Managing Data
- Team Collaboration and Communication
- Thinking from a Business Perspective
While the profession is incredibly diverse and has applications throughout most major industries, there are a few core aspects shared among most data science jobs. Essentially, data scientists provide value to their employer by helping them leverage the power of information through applied mathematics, statistics and computer science. There are many different kinds of career opportunities in the field, but there are a few skills that are useful just about anywhere.
1. Analyzing Data
The analysis of data is one of the primary duties of data scientists. Whether it’s tracking trends, generating statistics, mining user data or looking for anomalies in a set of variables, data scientists should be comfortable working with large amounts of information from disparate sources.
This skill involves more than just patience and attention to detail. Thanks to the complex infrastructures of modern technology, data scientists might need to be familiar with many different types of software, including statistical analysis programs and data visualization programs. These can be considered the tools of the trade for data analysis, so technological proficiency is a must.
Additionally, some data scientists are expected to have high-level computer science skills that can include writing their own codes or developing their own networks and databases. Data analysis can be a very digital endeavor in these jobs.
Ultimately, every business is different, so the data analysis required by one company might look different than the data analysis required by another. At its core, however, it’s about finding, organizing and modeling information into comprehensible units that can be analyzed for future business needs.
2. Creating Data Models and Modules
Gathering and organizing data is only part of the job of data scientists. The other part is modeling it.
Data models are representations of data for a specific purpose. They can be used in visualizations for projects and presentations; they can be used in databases and software programs where each chunk of data fulfills a technical purpose. Most modern data models are created through GUI software that accepts user input to generate computer-assisted output in the form of graphs, charts, tables, lists and more.
A similar creation is a data module. Modules can be made from directories, databases, data sets, uploaded files, information packages and any other type of collected information. The term “data module” is sometimes used interchangeably with “data model,” but technically, models and modules are distinguished by their structures. Models are singular while modules can be comprised of several different models.
Both models and modules are an important part of data analysis, so becoming familiar with them should be a key component in your education as a future data professional. Your job could require everything from their conception and design to their interpretation and presentation.
3. Processing Numbers and Numerical Data
You’ll need strong mathematical skills as a data information specialist. Statistics is usually a required course during data science undergraduate studies, and many programs will want students to tackle calculus and linear algebra as well. Some specializations might also ask for further math and logic work related to algorithms, discrete math and numerical analysis.
In the real world, applied mathematical skills can help with many data science duties. You might have to optimize shipping routes for a distribution company, for example, which will require graph theory. You might have to perform a principal component analysis for a hospital’s in-patient network, which will require algebraic calculations.
Math skills are also necessary for things like statistical modeling, risk/reward analyses and vectorized operations. You might need to call on everything from linear operations to probability theory to make sense of values and variables within a data set.
From crunching numbers to creating algorithms, you’ll often utilize your background in math to assist you with the on-the-job challenges of data science. Make sure that you build a strong foundation in numbers before moving forward with your career.
4. Presenting Information to an Audience
One of the lesser-known data science duties is presenting information to an audience. This could be a small team working on a project together, a full room of executives and investors, or a huge gathering of industry professionals at an expo.
The reason for this is simple: People need to know what you’ve found within your data. Whether you’ve calculated potential sales for an upcoming product launch or tweaked the company’s security software to detect a greater percentage of vulnerabilities, your bosses will want to share in that knowledge.
Another reason is that data science is a highly specialized job, and many people outside of the field don’t understand it. It’ll be your responsibility as a data scientist to clearly and efficiently share your information in a way that your audience is able to parse. Communication skills and interpersonal skills can come in handy here.
Some data scientist jobs are more “forward-facing” than others, but speaking very generally, it’s rare for data scientists to work alone. The image of a data scientist staying behind a computer 24/7 is a false one. Most professionals are called on to share their work with others, so if you’re pursuing a career in data science, be prepared for interactive responsibilities like writing reports, creating slideshows, giving technical lectures and hosting educational briefings for various assortments of people.
5. Ensuring Accuracy
There can be a lot of tedium associated with data science, but it’s a necessary evil to ensure that only accurate and up-to-date information is being used within systems, networks and organizations.
Some data might need to be combed over by hand. Other data can be fed into automated computer programs, but you might have to write those programs yourself, or you might have to verify its accuracy before modeling or charting it. You might also be asked to complete troubleshooting for poorly performing systems or vulnerable infrastructures.
Another aspect of data management is “cleaning” it. This means removing any incomplete, incorrect or irrelevant aspects that might come from corrupted files or duplicated data. For example, a common data cleaning technique is to look at the values of a data set and check for any typos, miscalculations, broken links or repeated figures that might be throwing off the entire set.
Ultimately, whether you’re dealing with graphs and spreadsheets or wide-scale cybersecurity programs, due diligence will be an important part of your work as a data scientist.
6. Writing and Checking Code
Early data scientists weren’t necessarily bound to computers in their activities or studies, but modern ones certainly are. A diverse base of fundamental programming skills is practically essential for people looking to enter the profession.
Python is a particularly common language in data science. It’s used for many automated tasks, and it also has practical uses in a variety of tech- and software-centered domains such as machine learning and artificial intelligence.
A lot of data science work also involves C/C++ or Java. Other areas of study could include Scala, SQL and R. For data analysis with a heavy emphasis on math, MATLAB and SAS are specialized programming languages meant for things like statistical modeling. You might also consider learning an up-and-coming language like Julia to gain an edge over other job seekers.
Programming skills are a must for future data scientists. Although some companies position data scientists alongside programmers or software developers to maximize all of their associated skill sets, it’s also common for data scientists to be responsible for their own coding, so it’s important to develop programming skills on your own as well.
7. Application of Machine Learning
Machine learning has quickly risen to dominate discussions and developments in applied data science. This term describes the practice of designing and training software programs to learn from their environment over time, allowing them to evolve and adapt to better perform their function.
In data science, this type of learning is usually accomplished by priming programs with large sets of data. Machine learning is also heavily associated with “big data,” or data that comes in such large, complex quantities that traditional methods of data management aren’t enough to handle it.
Machine learning is often considered a specialty within the world of data analytics, so it isn’t necessarily something that you have to know. You can find data science jobs that don’t require a familiarity with machine learning.
According to Forbes, however, industry experts believe that machine-assisted programs will grow significantly in the years ahead. This could mean lucrative opportunities for data science professionals who are adept with machine learning. There are even data science degree programs that offer machine learning as a concentration, so if you’re interested in this line of work, it could be a niche area of study that leads to a highly specialized job after graduation.
8. Handling and Managing Data
Data scientists typically spend a lot of their time working with data, which includes gathering, analyzing, cleaning and storing it in a communal digital environment. Their playgrounds can include everything from private company networks to entire digital information systems (DIS) and industry databases.
Handling this data is no simple task. It can require an entire toolbox of skills that range from research ability to organizational efficiency. Depending on your exact job, you might need to wear many hats as a data scientist, acting as a sort of “jack of all trades” when it comes to managing and maintaining data flows.
There are also difficulties associated with handling such large quantities of information. There are a lot of potential mistakes that can jeopardize the data or results obtained from its analysis, so data scientists absolutely need to know how to navigate these systems successfully.
Depending on their specific responsibilities, data scientists may also need to apply statistical or mathematics skills while working with data. This is where your math background will become relevant to your real-world data science duties. It requires a steady eye, however, with precise numbers and a strong attention to detail to ensure that operations run successfully. This diligence is a key component in data management for all types of data scientists.
9. Team Collaboration and Communication
Even though data scientists spend a lot of time working on a computer, people skills can be just as important for establishing a successful career. Being able to clearly and effectively communicate with others is essential. This is because data science professionals usually work as part of a team within a larger organization, so their colleagues might be everyone from software engineers and database administrators to product developers and project managers.
In other words, your day-to-day job duties as a data scientist might include a lot of collaborative effort. This might include working in small groups as well as educating or presenting data to an audience. You might need to act as a liaison between the tech- and non-tech people in your company. You might be called upon to speak in front of investors and other stakeholders if your data is relevant within a board meeting.
In a leadership position, data scientists might also be expected to set deadlines, delegate tasks, supervise junior employees and oversee the completion of various projects. If you have aspirations of climbing the corporate ladder, it might benefit you to take a leadership course along with your data science courses.
10. Thinking from a Business Perspective
Students of data science know that most of the career paths ahead of them are in business and commercial industries. Even though they are focused on the actual management and analysis of data, they always have to keep in mind that they are doing this to create value for their employer.
This means that data scientists need to think like their company executives. They should understand company priorities, be able to identify risks and opportunities, and have the knowledge and sense to filter out things that aren’t important to the company’s bottom line. This is sometimes referred to as “executive-level thinking.”
A good data scientist will also possess business acumen in addition to technical skills. While business acumen can run the gamut from financial literacy to management and organizational leadership, a few core skills can be learned to improve your employability. These include things like problem solving, critical thinking, customer service, employee relations and understanding basic business models.
Data science is a rapidly growing profession that has taken many industries by storm, with applications for everything from customer service to city planning. The core aspects of most data science jobs are really only a foundation for a much broader and specialized skill-set tailored for the industry or position in question.