Data science involves the extraction of useful knowledge and insights from large amounts of data. It helps companies in a variety of sectors to manage operations efficiently, make smarter decisions, and measure their performance. This career continues to evolve as it has been hailed as the sexiest job of the 21st century.
More businesses need data experts with the right combination of analytical, communication, and technical skills. They would use data to discover patterns and solve difficult problems. As companies continue to collect massive amounts of data, they would need qualified data scientists who can make sense of it.
Data science is one of the fastest-growing jobs in the United States. According to the Bureau of Labor Statistics, there are going to be 11.5 million new data science jobs by 2026. This shows that there are tons of opportunities for data experts. Here are some reasons why qualified data scientists are in big demand.
1. We’re Generating More and More Data Each Year
The internet-of-things (IoT) has made the world a global village. Our electronics, gadgets, and applications can connect and exchange data with each other. There are some interesting statistics on the amount of data being generated today and how much it is expected to increase in the next few years.
For example, research has shown that more than 6 billion connected devices around the world generate millions of terabytes of data every year. The world spends almost $1 million per minute on commodities on the Internet. The amount of data generated every day has been predicted to reach 463 exabytes globally.
Some predictions on the Internet-of-things that we would have 75 billion IoT devices in the world by 2025. They also anticipate that nine out of every ten people aged six and above would be digitally active in 2030. All of these means that companies would have plenty of data to collect, analyze and interpret.
2. Companies Are Managing Increasing Amounts of Data
Companies are collecting large volumes of data that keep increasing at a fast pace. But, this poses a problem as they aren’t equipped enough to clean, organize, and manage big data. So they need skilled data scientists who can manage their big data and extract insights that can help grow the business.
Before the Internet-of-things (IoT), it was quite expensive for businesses to gather any useful data. They had to spend a lot of money and resources on figuring out how to collect data. Now, technology has made it easier for them to collect all sorts of information. They just need to work with the right data science expert.
Every industry gathers and analyzes data to understand customer needs, optimize their operations, and come up with better products or services. Several companies also use predictive analytics to forecast future outcomes and make preparations for any market trends or operational issues that might happen.
3. More Industries Are Investing in Data Management
Although it’s easy to gather big data, not everyone can sort through and make sense of it. When companies can extract useful insights from data, they gain a competitive edge over their business rivals. Data scientists help companies to find information that would solve problems and achieve their goals in no time.
More industries are investing in data scientists because their expertise is extremely valuable. They can keep up with increasing amounts of data as well as detect sudden and unexpected changes. Data experts use their knowledge and experience to ensure that their employers do not fall behind their competitors.
Data scientists have a couple of roles and responsibilities. They may assist in setting up channels so companies can gather the right data. They would separate important data from useless data before analyzing it. Then, they’ll present the results in a way that helps business leaders to understand what’s going on.
4. Small Businesses Are Also Leveraging Data
There are so many reasons why startups and small businesses should leverage big data. It can empower them with meaningful information that can increase profits and guarantee success in the future. It’s no wonder why the majority of small and medium-sized businesses are using data analytics to grow much faster.
When small businesses analyze data, they get to understand their customer’s journey. For example, they would know where their customers come from, what motivates them to buy, what they seem to buy the most, and when they prefer to make the purchases. Such details help to serve their customers better.
Data analytics can help small businesses to track their expenses (operations, advertising, and tax), the number of sales or clients, and their net profit over a period of time. They can also hire data scientists to improve inventory management, adjust faster to the market’s needs, and provide better customer service.
5. There Is a Shortage of Qualified Data Scientists
Data science is a highly lucrative profession but there’s a limited number of skilled data scientists. According to LinkedIn, the United States had a shortage of over 150,000 data science skills in 2018. Since the supply doesn’t seem to match the demand, more people are encouraged to pursue a career in data science.
Many companies are offering high salaries for data science positions. This is mostly because qualified data experts are hard to come by and the role can be very challenging. They need to have the ability to handle large amounts of data, use algorithms or other tools to analyze them and extract useful insights from them.
Anyone can launch a data science career with the right education and training. They could enroll in data science courses being offered at academic institutions. It’s also possible to get started with college degrees in computer science, mathematics, statistics, or IT. They just need to take post-baccalaureate courses, like a certificate in big data.
6. Data Science Specialties Are Growing
Data science is a broad field so many data scientists choose to specialize. They often start as generalists before mastering a particular area in data science. The data analysis needs of large companies are often handled by a data science team. So, it’s a good idea to narrow down within the field of data science.
Some roles in data science include: business analyst, data engineer, data architect, database administrator, statistician, machine learning engineer, data scientist, AI specialist, business analytics manager, etc. But the title of a data scientist is the most common one and it pays some of the highest salaries in the field.
Although there are specific skills for each specialization, every data science professional is expected to have a solid understanding of mathematics, statistics, and programming. They should also be proficient in python or R, Hadoop, Spark or SQL, machine learning and AI, data visualization, and business strategy.