A Day in the Life of a Data Scientist - Master of Information
What is data science all about? The advent of big data has created a need for professionals who combine leadership vision, computer science background, and the ability to identify meaning in massively complex data sets. While big data has the capacity to completely transform how organizations operate in business, healthcare, education, and much more, it may be many years before there are enough data scientists to take full advantage of it! A Online Master of Information Degree can help you gain the real world experience and necessary tools needed to handle big data.
The history of data science is rooted in collaboration between traditional statisticians and computer scientists. Many experts believe that the first reference to modern data science was made by mathematician John W. Turkey in The Future of Data Analysis back in 1962. Since then, methods of collecting and processing data have truly grown exponentially, and the need to find and verify patterns within enormous quantities of data has grown pressing.
Considering the vast technological and personnel challenges involved, Harvard Business Review has even proclaimed data science “the sexiest job of the 21st century.” All of this interest might be reassuring to students looking for a lucrative, stable, and challenging career – but it also raises some questions worthy of investigation. When theory meets practice, what is it that a data scientist does all day?
Data Science and Its Similarity to Other Jobs – Software Design and Statistics
Data science stands at the crossroads between statistics and computer science. Professionals can come from either discipline, but must quickly develop skills in the complementary one if they wish to yield great results. A data scientist understands software architecture and is familiar with at least one core programming language. Using these skills, he or she engages with the complex statistical challenges involved in “making meaning” from emerging data sets.
The early research process in data science will be very familiar to practicing statisticians. Before fruitful effort on a project can begin, the data scientist must define the problem, identify the key data sources, and set standards for what collection and vetting practices will inform the results. These early steps ensure clarity, accuracy, and methodological rigor – the collection, processing, and modeling of data, in turn, is largely automated through software.
Once the issue to be studied has been specified, the data scientist works on designing and coding software equal to the task at hand. Data scientists spend most of their working hours developing, testing, and refining algorithms necessary to sift through an ever-increasing flow of data. They often work alone, providing the software so it can be executed and monitored by other business groups. However, they might work in groups or have an ongoing stake in given problems.
What Challenges Do Data Scientists Face on the Job?
The core duty of a data scientist often boils down to discovering patterns in data. When a project reaches a data scientist’s desk, he or she might have very limited context about it. It’s important to develop an intellectual framework for the problem before software design can begin. This can involve independent research and reaching out to other stakeholders across the organization. It may be difficult to connect promptly, however, and timelines for projects can be tight.
Once the core challenge is better understood, it is not necessarily smooth sailing for data scientists. They are often called on to draw from their knowledge of programming to balance the “best” solution with the one that will produce fast and practical business results. Data scientists who are adept at navigating this problem can find their professional prospects growing: Knowledge of project management, for example, can place a data scientist on a leadership track.
Organizations around the world are still finding the right ways to integrate the data scientist into existing teams – in some cases, they are better at utilizing data-based insights than the data scientist! As a result, a data scientist might end up working largely independently rather than being closely tied to a specific team. It’s important that data scientists be able to manage and motivate themselves, adapting to work alone or with groups according to the evolving situation.
How Much of Data Science is “Science” and How Much is “Business”?
Although the working conditions of a data scientist can vary from employer to employer or project to project, each one is often paired with an “adviser” whose job is to help provide key context on new questions that must be addressed in a data set. Obtaining and understanding the data does rely on key business skills – communicating, networking, and reporting. Other developers and statisticians in the enterprise may also have questions about the ongoing work.
The role of the data scientist is still being defined, so better tools and processes are sure to emerge over time. To be prepared for any turns the industry might take, it is a good idea for a would-be data scientist to build clear communication skills. Results and methods may need to be explained to non-technical personnel. Incorporating and maximizing “best practices” might rely on being able to interface with other professional groups that have different skill sets.
The “Best” and “Worst” Parts of Being a Data Scientist
There’s much to recommend the world of data science for emerging professionals. Demand is high and is expected to grow for many years, for example. Compensation tends to be high as well, with plenty of room for growth among those who can quickly adapt their skills to new and unfamiliar situations. However valuable job security and compensation are, however, they are rarely enough to sustain a career. What do data scientists tend to find personally satisfying?
Many data scientists are motivated by the opportunity to solve large, novel problems with the tools at their disposal – algorithms and software. Creativity, logic, and practicality all combine in the process: The data scientist has the opportunity to see his or her efforts help resolve “real world” problems. Projects have clear end results that positively impact the enterprise and those who rely on it, making it easy for data scientists to quantify their work and take pride in it.
Naturally, there are also challenges involved. Many of these emerge from the interdisciplinary nature of data science and the “unknowns” involved in implementing it inside an organization. Like other types of computer scientists, many data scientists find the process of software quality control to be harrowing – and they might have less experience with this than their colleagues who originally come from computer science backgrounds. The need to develop novel approaches for many fundamental tasks can be a satisfying challenge or a serious source of stress.
All in all, data science is an excellent career – or, indeed, second career – for anyone who is analytical, self-disciplined, and interested in combining mathematics with technology. Since data science as we know it today is barely older than the “big data” it works with, there is still time for new entrants in this field to become trailblazers and respected experts. At the same time, the goals and duties of the data scientist are already defined clearly enough for them to make great contributions to specific problems that affect the world around them.