Data Science Skills Study 2019 - Great Learning
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Data Science Skills Study 2019

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Data Science Top Trends

As is true with every emerging field of technology, data science has been undergoing several transformations every year. Data science professionals need to be informed about the constant changes and new advances in the field to stay on top of their game. As the learning of the domain reaches new depths, knowing the new tools and technologies become crucial to avoid disruptions at the workplace.

However, keeping track of all the new changes and advances can be quite challenging at times. At Great Learning, we understand these challenges, and with that in mind have brought together a list of trends of 2019 based on reports published by AIM. The following inferences highlight the essential new developments in data science languages, methods, tools and more to help you decide your learning needs.

Preferred Data Science Language

Python has emerged as the most used programming language among data scientists. Almost 68% of data scientists cite Python as their preferred data science language, replacing R which was popular even a couple of years back. SQL and SAS have also witnessed a steep decline in use despite their versatility. 

Popular Data Science Methods at Work

Logistic Regression dominates the domain when it comes to popular data science methods used at work. It is closely followed by Decision Trees and Neural Network which have 58% and 44% user base respectively.

Popular Python General Purpose Library

Since Python is the largest programming language in the world, data scientists frequently refer to Python libraries to analyse large amounts of data. Pandas top the list of Python libraries with 42% popularity, followed by Numphy at 30%, Sklearn at 13% and MatPlotLib at 7%.

Preferred Data Science Tools

Open source tools remain the most used by data scientists and have grown in popularity compared to last year. Only 5% of data scientists state that they prefer using custom-made tools that are tailored for very specific uses.

Preferred Data Science Visualisation Tools

Dashboards and visualisation tools are crucial for formatting and presentation. As it impacts decision making directly, data scientists are very particular about the tools they use to represent their findings. Tableau has emerged as the most preferred visualisation/dashboard tool with 56% of the votes. Other popular choices include Microsoft Power BI, IBM Watson Analytics, and SAP Analytics Cloud.

Poplar Cloud Providers

Amazon Web Services take the cake with a whopping 43% of the votes. Google cloud follows close with 33% and Microsoft Azure is fast becoming another popular choice with 16% votes.

The trend in Learning Resources

Thanks to the nature of the domain, constant learning is a mandatory part of a data scientist’s journey. 78% of data scientists resort to Youtube video tutorials for learning new techniques while the rest follows the old school way of reading books. Some data scientists also refer to MOOCs to upskill themselves.

Finding Open Data

Sourcing clean open data can be quite a task sometimes. Thankfully GitHub, government websites and university websites are ready sources from where you can find clean open data. Manual data creation also remains a popular choice for clean open data.

Most Used OS for Data Science

Data science tools often face compatibility challenges with operating systems. Windows OS happens to be compatible with most data science tools while Linux is the most secure platform to use. A minuscule percentage of data scientists use MacOS.

Favourite Development Environment

IDE or integrated development environment is very important for hosting all kinds of data science processes. Notebook, R Studio and PyCharm are the top choices for IDE in the domain.

Popular Neural Network Architectures

Convolutional neural network is the most frequently used neural network of 2019 apart from Feedforward neural network for network architectures.

If 2018 was all about the exponential growth of data science, 2019 and the coming years will be more about how professionals are transitioning into the domain. The demand for data science expertise is creating different kinds of opportunities for both specialists and generalist skills. Recruiters are using innovative ways to test and hire candidates. Companies are even creating internal training programs to upskill their employees. After all, a successful career in data science needs continuous learning. You can check out data science programs here and here to understand course structure and curriculum. 

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