The organization of today has access to previously unimaginable volumes of information. These are a prospective goldmine of insights that could be invaluable to businesses in their endeavor to take strategic decisions across all aspects of their operations. And that, precisely, is where data science comes in.
Why are platforms needed for data science?
There is a clear and present need to derive social and economic value from all the available information. The barrier, though, proves to be the absence of the right skills and tools. This is why there is a need for Data Science platforms.
A good platform can change the way data science is conducted. When combined with a team of certified Data Science professionals who possess the right Data Science skills, a platform can improve the performance of each team member as well as the team as a whole. The signs that suggest the need for a platform include the following:
- No idea how many models are in force at the organization
- Excess time spend on model maintenance after deployment
- Lack of collaboration within the team
- Being unsure about how to deploy in sync with scaled-up operations
- The absence of logical workflows, proper integrations, and version controls
What attributes are desired in a platform?
A good platform should:
- Be flexible
- Be supportive
- Include all the tools (and team members) needed
- Be integrative
- Foster collaboration and encourage each member of the team to produce high-quality work
Below are some of the best Data Science platforms being used by enterprises today:
Alteryx
This allows the client organization to create a data science culture without even having a data scientist leading the operations. This Data Science platform could be a good choice if the organization has less people with advanced degrees than it has citizen data scientists. It allows the client to build models within a workflow and offers model management and deployment as well. Alteryx Analytics’ technology partners include Microsoft, Qlik, Tableau and Amazon Web Services.
Anaconda
An open-source, free platform, Anaconda has over seven million clients across the world. Anaconda Distribution and Anaconda Enterprise are its most prominent products, with the former allowing clients to deal with the environment and platform for 2,000 data bundles for Python for Data Science and R. It uses an interactive notebook concept, which makes it a great choice for Python or R enthusiasts. The programming language makes it a niche product, but it is the only platform offering indemnity for the Python community. It offers improved collaboration features and model reproducibility for data discovery and analytics.
Cloudera
This platform is very popular as it is optimized for the cloud and enterprise data solutions. Its features include automatic data pipelines and support for full Hadoop authentication and encryption. It is a great choice when the organization wants to analyze sensitive data, commonly seen at large corporations. It allows Spark queries within a safe environment, and it can share models as REST APIs without rewrites. Cloudera Data Science Workbench is a particularly popular platform among Data Science professionals, including data scientists, programming specialists, and software engineers. It lets clients and data scientists use the most recent and updated systems and libraries scripted on Scala, R, and Python.
H2O.ai
With a userbase estimated at over 14,000 organizations and 155,000 users with Data Science skills, H2O finds use in healthcare, finance, retail, and manufacturing. ‘Driverless AI’, one of its tools, was a 2018 InfoWorld Technology Awards Winner. Prominent large organizations using this platform include PayPal, Cisco, Dun and Bradstreet, and a few assembling businesses. Its capabilities include deep machine learning, which can help to expand the reach into AI – it is, in fact, a leader in machine-learning unified platforms. Additionally, it is an open-source platform, and it offers predictive analytics. It is quite flexible and scalable but falls behind slightly on user-friendliness.
MATLAB
The finance sector is always looking for certified Data Science professionals, and MATLAB is a great choice for such requirements, as well as for non-fintech sectors. An easy-to-understand platform, it is used in data analytics for neural systems, cloud processing, and machine learning. It allows users to go through huge amounts of data from a variety of formats and sources, such as IoT gadgets, web content, record frameworks, video, and sound. Its uses include sensor analytics, telematics, and predictive maintenance. Its pricing makes it a great choice for organizations with big budgets, though that also puts it out of bounds for citizen data scientists.
RapidMiner
An easy-to-use platform, RapidMiner nevertheless does not compromise on solutions requiring sophistication. Highly usable, it is widely appreciated across levels of Data Science skills – citizen data scientists as well as highly-trained ones with advanced degrees. It is built specifically to help data scientists with machine learning, data preparation, predictive analytics, and text mining. It is a great choice for visual workflow and for a machine learning boost. RapidMiner Turbo Prep lets its users pivot, blend data from various sources, and take charge of transformation, with just a few clicks.
