The term data science is widely recognized in the technology industry. However, it is often not commonly projected into mainstream career options and typically sits under the computer science field. However, specializing in this area is a rewarding and varied career option for many people and opens up a world of opportunities in many more sectors than one might think.
What is data science?
You may be asking, ‘what is data science?’ In short, it refers to extracting insights from data. In modern society, data is everywhere. This concept of data extraction and analysis has been around for many years and has evolved over time. The discipline of data science covers many skilled areas, including mathematics, statistics, and programming. There are also many other elements and theories in the field, including data mining, modeling, and data engineering, to name a few.
Over the past decade, technology has paved the way for innovative new ways to work and do business globally. The role of data science has enabled people to look beyond the raw data and interpret and extract meaningful insights to improve the way people go about their daily lives. The beauty of data science is that it is also there to present the information in a simple way for the end-users that do not speak technical languages.
Who works in data science?
Data science sits under the umbrella of computer science but has a more specific role within the sector. People that work in these roles are often referred to as data scientists. The term scientist often conjures hair-raising experiments and lab environments. However, in this field, there are similarities; it is a comparable concept, but with computers as the environment.
DJ Patil and Jeff Hammerbacher coined the term ‘data scientist.’ Sometimes this role is also referred to as a ‘business analyst’ or ‘data analyst.’ Still, there is a significant difference in the disciplines of a data scientist, which include three distinct skill levels, such as:
- Strong business acumen
- Communication skills
- Exploring big data
What does data science involve?
Data science typically consists of three components. Each component plays a role in delivering the most accurate data to the right people. The three areas include:
Organizing big data
As you might imagine, the scale of data for some organizations is extensive and can get confusing. The foundations of delivering the results required are organizing and executing the physical storage of the data. This process also includes implementing best practices of data handling and ensuring future data is stored correctly to avoid issues.
Packaging the data
Once the data is organized, it is ready to be packaged. This is the process of creating prototypes and applying the statistics and visualization development. During this element, the data may also be modified and combined to produce information in a presentable format.
Delivery of the data
Delivering the outcome of the data is the next step. When the data has been packaged in the right format for the end-user, it will be delivered and narrated so it can be implemented accordingly.
Skills and qualifications of a data scientist
While the role of a data scientist is a rewarding one, it is not without its challenges. The computer science landscape is complex, and ensuring you have the right qualifications and on-going experience is vital to succeeding in this role. Technology is ever-changing and having a solid education behind you will help to secure a position in the industry. Most data scientists have a university degree and continue their learning with professional qualifications and training. Work-based opportunities are available, and qualifications can be completed alongside full-time work such as an online data science masters.
Alongside education, you will require other skills and talents, including:
Intuition and creativity
While the role often looks at analytical and logical explanations and results, having intuition and creativity can help you look outside the box in complex situations. This innovative approach is helpful when applying various techniques and can assist in looking at a real-world application.
Data science is not a lone wolf style role; it is a team-orientated role that will involve excellent communication skills. You may be discussing ideas and applications with people at all levels, including those not akin to technical language. This position will mean you have to interpret complex scenarios and outcomes to ensure all understand them. Within the team setting, it is also vital to have good communication skills with all members of staff from juniors to managers.
A data scientist may be working with a range of organizations that require the use of big data. With that, business acumen in the industry you are working in is crucial. This will ensure you tailor the requests of management and understand their needs. It is also helpful to understand the sector trends and identify the niches within the data you are investigating.
The call for experts in this field
There is a high demand for people in data science roles within the US. In 2019, this role was ranked number one by glassdoor.com, and LinkedIn highlighted a 37% hiring growth in the industry in the past three years. There are so many different and exciting roles with data science, including:
- Cloud Engineer
- Data Manager
- Data Scientist
- Machine Learning Engineer
- Artificial Intelligence Research Scientist
- Database Engineer
- Data Engineer
- Director of Data Science
- Plus, many others
The industry is growing, and it is an ideal time to start considering options in this varied sector. If you want to change careers, there is also the scope to do so. Many other analyst style roles could potentially have transferable skills when complemented with further qualifications.
The roles available are not without good rewards, either. The Bureau of Labor Statistics reported the average salary for data-driven research roles was $122,840. Plus, it also mentioned that the industry far outpaced other fields in terms of compensation due to the technical knowledge and skills required, and this is adequately compensated.