2021 Trends in Data Science
January 18, 2021
As we welcome in 2021, we wanted to share with you what’s around the corner in data science, and how can you take advantage of these trends to build your business or career.
Explosion of Python Projects
The first trend we’re seeing in the data science world is an explosion of Python projects. Python is now the most popular searched for term on Stack Overflow.
Data scientists are embracing Python with good reason. There are a huge number of Python data science libraries that are open source and easy to use, like Scikit-learn, Pandas and Tensorflow. In addition, Python is easy to learn. When compared to other data science languages like R, Python promotes a shorter learning curve by promoting an easy-to-understand syntax.
Collaboration Across Your Company
The second trend that we see and hear about a lot at Zepl is around collaboration. As data is collected throughout an organization – sales, marketing, IT, security, product — it’s important to give all stakeholders access to this data so they can gain insights from it. As data scientists model and gain insights from this data, they want to easily share their results with business and executives. This is often a huge bottleneck in the feedback loop between data science and the business, which can cause confusion or distrust in the ML results by the business users. By being able to collaborate between the data science teams and the business, everyone has visibility into what models are used and how results are generated. In addition to creating trust between the data science and business teams, this also accelerates the feedback loop so companies can get to market faster.
Data Analysts Are Turning into Data Scientists
With today’s huge shortage of data science talent, we’re seeing the lines blur between data analysts and data scientists. Data teams are starting to be asked more predictive questions. Instead of being asked “What happened?” they’re being asked “Why did it happen?” “What will happen next?” and “What should I do?”
Data analysts are equipping themselves to answer these questions by adopting tools like Zepl which lets you them use Python, SQL, R, and Scala all in a single notebook. Tools like this are providing a bridge between the data analyst and the data scientist worlds.
AI/ML Moving to the Cloud
It isn’t a surprise to anyone that cloud is on the rise in every vertical. But within AI and ML cloud adoption is on a particularly sharp curve, with Information Week estimating a 5X increase in cloud adoption by 2023. What’s driving this acceleration? Scale. As companies are racing to leverage AI and ML throughout their business, they need to make data science available to many more users. The cloud offers unlimited scale with little or no maintenance at a much more cost-effective price point than legacy on-prem systems. This frees data teams to focus on the data analysis that they were hired to do, instead of having to manage and run the infrastructure to support those projects.
The Rise of the Data Marketplace
Lastly, we’re seeing data marketplaces begin to solve the problem of getting data in the hands of those who need it. Snowflake is at the forefront of this trend, providing live and ready-to-query data from their ecosystem of business partners and customers. Zepl has put some data sets and notebooks on the marketplace to help our customers get easier access to data.
Zepl helps individuals and teams take advantage of many of these trends — by supporting Python, R, SQL and Scala in a single notebook to providing a robust collaboration environment for the entire company.