The huge field of data science continues to expand, and data scientist skills must be top-notch to keep up with the advancements that are continually made in this area of expertise.
In the paragraphs that follow, we will discuss what data scientists are, the important roles they fill, and the everyday skills they must employ to get the job done efficiently.
What are Data Scientists?
To put it very simply, data scientists are experts in analytics. They combine a keen sense of curiosity with an in-depth knowledge of mathematics, statistics, and science to come up with novel ways to solve everyday problems.
Data scientists must be aware of current trends and predicting future ones based on things that are happening right now. They are in the forefront of the business world, helping to pave the way to success for countless entrepreneurs and large companies. They can be employed for a wide variety of reasons including, lowering manufacturing costs, expediting customer service, and predicting future problems before they arise.
Data science is a broad term that incorporates many sub-specialities of professionals including:
- Data Scientists
- Data Analysts
- Data Architects
- Data Engineers
- Database Administrators
- Business Analysts
- Data and Analytics Managers
The professionals within each subset of the data science field provide their own unique spin on the methodology used to take their vast array of skills and turn them into working solutions for small business and large corporations alike.
The bottom line is that both large and small businesses are continually trying to strike a balance between putting their customer’s needs first and improving their margins of profitability. To accomplish this goal and become more successful, they employ any number of professionals within the data science field.
Many of the data scientist skills will cross over and be used in multiple subsets of the field’s professionals overall. Below we will discuss some of the most common skills these professionals use by breaking them down between technical and non-technical skills. Keep in mind that both are equally important when it comes to having a successful career in data science.
General Data Scientist Skills Explained
Data scientist skills can be further divided into two groups.
Technical expertise deals directly with their knowledge of sophisticated mathematical techniques, comprehension of statistics, and technologically based skills.
Non-technical skills involve their interpersonal skills, superior communication abilities, and strong teamwork capabilities.
Technical Data Science Skills
While data scientists don’t need a lifetime of knowledge stored away in their brains to get started down a successful career path in this field, a few basic technical skills that can be further expanded upon are vital. These are as follows.
An Understanding of Basic Tools Used
No matter what company eventually hires a data science professional, this person will be expected to be familiar with some of the more standard programming tools and the language involved in using these programs. This includes a knowledge of things like R or Python concerning statistical programming and a familiarity with SQL regarding database querying.
Resource: Data Science Tools
An Understanding of Basic Statistics
Skills that data scientists need include being familiar with maximum likelihood estimators, statistical tests, distributions and much more. It is also vital that these professionals know how to recognize which technique is going to work best in a given situation. Depending on the company employing them, more or less emphasis may be placed on data-driven techniques used to understand and calculate statistics.
A Good Understanding of Calculus and Algebra
It may seem odd that a data science professional would need to know how to perform calculus and algebra since many of the programs and software used today can do all of that and then some. It’s true that not all companies place the same emphasis on this skill. However, newer businesses whose product is defined by data and small advancements made along the way will favor professionals who have these skills and don’t rely on software alone to get the job done.
Data Visualization Skills
This skill subset is of particular importance to newer companies that are just learning to make decisions based on this type of data and the future predictions that can be learned from it.
Correcting Dirty Data
While it’s true that programs take care of this issue in many cases, the ability to locate and correct corrupt or imperfect data can be an important skill that sets one apart in the field of data science. This skill is highly favored among smaller companies where incorrect data can so vastly impact their bottom line. This set of skills can include locating and replacing missing values, correcting inconsistencies in formatting, and changing timestamps.
Non-Technical Data Scientist Skills
It may seem confusing to think that data scientists would even need non-technical skills. However, there are several fundamental skills necessary that would fit into this category.
Excellent Communication Skills
Data science professionals need to be able to accurately convey the results of their work to both technically inclined individuals as well as those who are not tech savvy. To accomplish this, their interpersonal and communication skills need to be outstanding.
A Keen Sense of Curiosity
Data science professionals need to maintain a degree of curiosity to be able to detect current trends in their field and use them to make future predictions based on the data they collect and interpret. This natural curiosity will inspire them to stay on top of their game in terms of continuing their education as well.
Career Mapping and Goal Setting Skills
Individual skills that a data scientist utilizes will cross over from one sub-speciality to another. Professionals in this field may not remain in the same sub-speciality for the life of their career. Therefore, it’s important to understand what additional skills may come in handy down the line if they choose to enter another category of work within the broad field of data science. Staying ahead of the game and gaining those skills in advance can be a huge asset.
As both large and small businesses continue to rely heavily on the proper collection, analysis, and implementation of vital data regarding their company, the field of data science overall will continue to be in demand. This will provide countless opportunities for satisfying and exciting careers for those who decide to enter this field and use their data scientist skills to enhance the lives of millions of people around the globe.