2023 and Beyond: The Future of Data Science Told By 79,306 People
Data science has been the hottest thing for a while now, but it still continues to be a major talking point even today. In fact, according to the U.S. Bureau of Labor Statistics, it’s estimated that around 11.5 million data science jobs will be generated by 2026.
We decided to look further into how data science and its usage are evolving. By comparing the results from the 2021 Python Developers Survey and the 2022 Python Developers Survey, we discovered some insights that support the claim that data science is indeed on the rise.
The data for both the 2021 and 2022 Python Developers Surveys were collected from over 40,000 and 39,000 individuals, respectively, from PSF (Python Software Foundation). Hence, the data and our findings don’t represent the whole Data Science community.
Let’s address the main question first, what exactly is data science?
What is Data Science?
Data science is a combination of – among other things – mathematics, programming, and statistics to study data and identify trends. It is used by businesses to extract insights into how things are running, predict future trends, and so on.
One example of a company using data analytics is Netflix. By using and analyzing data, Netflix saves about $1 billion in user retention!
With so many businesses now using data science, it’s no wonder that by the end of 2023, the big data analytics market is anticipated to grow to $103 billion. It is also one of the fastest-growing industries right now.
All of this information is supported by the 2022 Pythons Developer Survey, as you’ll see in this article.
Python for Data Science
Programming plays a huge role in data science. To narrow it down further, Python specifically, plays a vital role.
This is supported by the fact that 86% of data scientists said that Python is the main language that they use for current projects. 10% said they use it as a secondary language.
This leaves around 3% who said they don’t use Python at all.
Moreover, out of the 39,022 people we surveyed in 2022, 50% of them said they used Python for data analysis, followed by web development, machine learning, and DevOps.
This goes to show how data science and Python go hand-in-hand when it comes to their popularity.
The rising popularity of data science tools
Since data science is growing in popularity day by day, it’s only natural that tools and technologies related to it are also becoming more popular.
It’s not just data scientists who are using these tools. From banking to healthcare, professionals from other industries have also taken an interest in data science.
We found this to be true when we compared the past two years’ survey results with each other.
As you can see, the usage of data science frameworks in 2021 was less than the usage in 2022.
NumPy remains the most used data science framework, followed closely by Pandas.
There was also a rise in using big data tools in 2022. As demand increases for data-driven reports and decisions, so does the popularity of data science tools.
There has been positive growth in the usage of all the big data tools mentioned above.
Overall, the increase in the trend of using data science frameworks and big data tools supports the fact that this industry is growing year by year.
What’s Next For Data Science?
Data science is showing no signs of slowing down anytime soon, especially with the increase in demand for data science jobs.
With the exploding popularity of AI tools and machine learning, it’s safe to say that their sister industry, data science, is here to stay for good.
Make sure to check back next year to be the first to know about new trends in Python and the data science industry!
Frequently Asked Questions
Is data science a good career?
Data science is a good career path considering the rise in demand for jobs in the industry. The pay is competitive with good benefits, and there is lots of potential for both financial and professional growth.
Is data science hard?
For a non-technical person, entering into the data science field may prove to be challenging, as it requires knowledge of mathematics specifically statistics, and programming. The main hurdle is to learn the right skills needed for the job. Once an individual is past the initial learning curve, data science is a truly fascinating field to work in.
What is machine learning in data science?
Machine learning plays an important role in data science. It’s the process by which experts train machines and algorithms with current data to predict future data, while also discovering new trends. Machine learning allows huge amounts of data to be processed hassle-free, making the lives of data scientists much easier.
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