There’s no denying that the future of business lies in data. Big data is bigger than ever before, with most people generating tons of information about themselves every day of their lives. From shopping online to using fitness trackers to posting on social media, data is everywhere — and that data needs skilled data scientists to interpret and put it to use.
That’s why careers in data analysis are booming, and are likely to continue to boom well into the future. Data analysts are needed in nearly every major industry and business, as well as in governmental and academic work. There are several career options for a person going into data science, including:
- Data scientists who interpret raw data using techniques like computer modeling, statistic, and analytics to help organizations make major decisions.
- Data engineers who take raw data, find trends and insights, and develop algorithms to make that data more useful for clients and customers.
- Data analysts who, like engineers, shape data into forms more comprehensible and useful to clients, stakeholders, and other “end users”, and
- Machine learning engineers who design and develop AI software that will automate the process of gleaning useful insights from raw data.
Data science can be a rewarding and lucrative career — but like technology itself, data science is always evolving. Applications grow more sophisticated, client needs more complex, and requirements will always be changing. To keep abreast of trends and stay relevant, it’s important to keep your skills up to date, and / or continue to refine those skills to fit into existing (and emerging) niches like those given above.
Here are some of the most important skills a good data scientist must master and continue to develop.
It goes without saying that a strong technical foundation is an absolute must for a career in data science. A formal education like a degree in data science is a great start — but you’ll also need to further your education to stay current, such as courses in:
- SQL, the industry standard for database query languages and the likely backbone of any data scientist’s repertoire. SQL is one of the most in-demand skills in information technology and the first step toward doing serious work in Big Data.
- Excel. Despite its ubiquity in offices everywhere, Excel is more than a simple spreadsheet tool. Methods like VBA lookups and writing macros add an extra level of depth and power to Excel, which is frequently enough for doing some of the lighter analytics used in daily business life. Excel is still a commonly-used product in data analysis, although for more sophisticated tasks, you may want to move on to something more advanced, like:
- Python or R. If you’re looking to go one step beyond what Excel can do — and especially if you’re looking to develop advanced algorithms — a statistical programming language like Python or R is a must. The good news is, both are open-source and free, and both are an accepted industry standard.
Critical thinking skills aren’t just vital to good programming — they’re important so you know what questions to ask when you’re analyzing and interpreting data. Algorithms and automation will still only take data analysis so far, and the ability to see and reason out hidden connections is an invaluable skill for any data scientist. This is especially true for data scientists with ambitions toward staying on the cutting edge: you’ll have to learn to think around corners, rather than leaning on established routine.
Even when your primary skill is in data analysis, sooner or later you’re going to have to communicate the results of that analysis to other people, whether it’s clients, stakeholders, or the rest of your team. Good communications skills are an important part of data science — simply put, even the most insightful analysis is useless if it can’t be communicated effectively. This includes skills like written and in-person communication as well as public speaking and presentations.
Attention to Detail
Any programmer will tell you attention to detail is one of the foundations of good programming. But for data scientists, that principle goes even further — a data scientist must be able to look for irregularities, hidden clues, and signals that could lead to major insights. On the flip side of the coin, a single coding error could lead to a major disruption in workflow, or — worse yet — faulty interpretation of data.
While it may not seem like an obvious first choice for a career in data science, business management skills can play a major part in furthering that career. Knowing how and why business works will be a huge advantage in knowing what kind of data to collect, how best to analyze it, and what to do with the insights that emerge. It will also prove useful for data scientists looking to transition to more of a leadership role.