With Open Data Science, data scientists can: Access the very latest innovations in the industry A lot of the latest innovations, like Hadoop, came from large companies like Yahoo, Facebook, and Google.
These companies developed tools for their own use, then gave back to the community by open-sourcing their technology.
By adopting Open Data Science, you can get innovation almost directly from key players in major markets. For example, TensorFlow is an open source package developed by Google that has become a major deep learning artificial intelligence (AI) technology used by data scientists.
If you use only commercial tools, you would quickly fall behind the competition. Participate in a vibrant community You can report bugs, solve issues, and contribute to solutions, all while getting direct access to the actual developers of the tools you use.
This gives you the type of access—and opportunity to provide input that can help your company—that is unheard of in commercial analytics tool circles.
Teach yourself No need to pay unaffordable licenses to learn a new technology —you can learn for free. With most commercial analytical software, you can learn the necessary skills only through premium licensing, support, and training contracts.
Educators are realizing the benefits of building their courses around Open Data Science, as it increases the accessibility of their material and portability of the skills their students gain. Members of the Open Data Science community are motivated by mass adoption and therefore provide documentation online, run community support forums, and provide video walkthroughs, demos, and training.
This lowers the barriers to entry for everyone. Use the right tool for the job Data analysis, visualization, sharing, storage—these all require different tools (a complexity that multiplies when you consider the range of data types that an enterprise deals with). In the Open Data Science world you don’t have to make a one-sizefits-all decision.
You can choose to use R and Python, etc… You can use Tensorflow and Theano and Scikit-learn. You use the best tool for the problem you currently have. This is a marked contrast to proprietary technologies, which often tend to be insular and promote tools from that community.
Despite the many articles about “R vs. Python,” and the like, the reality is that data scientists need choices and flexibility. Open Data Science brings all the following domains together to solve the world’s data challenges with the most innovative software in each , Statistics, machine learning, optimization, artificial intelligence • Big Data and high-performance computing • Data storage, including data warehousing, RDBMS, and NoSQL storage technologies • Business analytics and intelligence • Notebooks, integrated development environments, analytic pipeline workflows • Plotting and visualization • Web technologies •
Open Data Science combines innovative software from many domains to address the world’s data challenges