The world is more connected than ever, and perhaps nowhere is this more obvious than in the fields of science and business. As the competition between companies increases and foreign and domestic corporations vie for the same audience, businesses will look for anything that gives them an “edge”. This is where science comes into the equation. More specifically, it is where data science shines. Businesses all need to extract information from data that can direct their growth and marketing initiatives.
This blog will take a closer look at data science, why it is such an essential asset to businesses, and which skills you need to make it in the industry.
Table of Contents
What is data science?
Data science is a field of science focused on taking large amounts of information, be it structured or unstructured, and gleaning the insights it has to offer. From uncovering how the customer experience seems to unfold to examining how customers find the business in question, data science is an increasingly important tool in today’s competitive business landscape. In fact, information taken from data science can help drive growth in organizations by offering an in-house solution to spotting trends, making informed decisions and predictions, and answering those important questions.
Data scientists are usually in charge of data science work. These professionals can come from all walks of life and might have backgrounds in statistics, mathematics, advanced analytics, data mining, algorithms, and even AI and machine learning. Data scientists can sift through data and pull the numbers together into something usable for individuals with no background in the field. They are typically also equipped with a range of skills that can be applied to their discipline in a variety of ways, making a diverse “toolbox” of experience and abilities particularly important for this career.
What skills do data scientists need to excel in their field? We will explore some of these in more detail below.
Data scientists work directly with technology to unearth valuable information from large data sets. This requires technical skills which allow them to work to the best of their ability. Here are some of the most important of these technical skills.
Statistics and probability
Data scientists use a variety of variables as they work. These include capital algorithms, systems, and processes, all of which can be used to help data scientists explore the data, understand the underlying relationships between any two (or more) variables, and uncover anomalies. All of this, in turn, allows scientists to predict future trends and offer forecasts which help drive business goals. Statistics and probabilities are two technical skills that are necessary to master to provide the above insights.
Linear Algebra and multivariate calculus
Data science models and machine learning systems are created using several unknown variables or predictors. A firm grasp of linear algebra and multivariate calculus help data scientists build machine learning models. From plotting functions to recognizing gradients and derivatives, these fields are critical to success in the data science field.
The data that scientists use is not always delivered to them ready for the modeling process. Some of it will inevitably contain imperfections that need to be identified and understood before the data can be used. Preparing and “wrangling” this data is an important process that prepares data for future analysis. Data wrangling allows scientists to reduce their processing time and, in turn, increase the amount of time they can spend on actual data analysis.
Because so much of their data will need to be prepared before it can be processed, data scientists must understand how to build and maintain databases. These systems can index, manipulate, and edit the data within the system, making it easier to retrieve and store data without much fuss or confusion at any point.
Understanding the data isn’t enough for most data scientists. They must also understand how to visualize that data in ways that make sense to stakeholders and business executives. Creating smart visualizations that aid in their storytelling makes explaining the information gleaned from the raw data they provided much easier.
There are several skills in this section that might initially seem overwhelming. Education can help, and an online masters in data science makes the training process simple. This is especially true when you work with a trusted name in the industry, such as Worcester Polytechnic Institute. The educator uses a collaborative and interdisciplinary approach to set their students up for success.
In addition to the technical skills described above, data scientists must also possess soft skills. This is especially important for professionals interacting with individuals outside their field, such as a company’s board or a CEO. While technical skills are undeniably necessary, they begin to pale if the method used to deliver the information is ineffective.
Here are some of the soft skills data scientists should foster if they wish to interface with non-scientists and present their insights.
Understanding the needs of a business
Understanding why businesses ask for data science is important as it can help direct data scientists’ efforts. When you understand where a company is hoping to go, it is easier to focus your efforts on uncovering trends and data that directly impact those goals.
As experts in their field, data scientists understand their work. They know how data works, the different variables at play, and how to parse mountains of raw data to reveal valuable insights. However, individuals outside of that field will likely have difficulty understanding the data and how data scientists arrived at their conclusions. The ability to take the technical information and deliver it in a manner that is easy for the recipients to understand is invaluable.
Are you interested in becoming a data scientist? Keep the above information in mind as you plan your next steps!