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Data Science Platform

Introduction to Data Science Platform

The data science platform is a package of different tools which takes care of the entire data modelling process. Data science platform gives power to data scientists to carve out valuable insights from data collected at sources. Not only producing an insight, But It also helps data scientist teams to visualize and communicate results to key clients and stakeholders. The data science platform gives businesses an advantage to make data-driven decisions to maximize their output and enhance customer satisfaction. As technology is developing daily, the data science platform provides the team with better flexibility and scalability by adding the latest data science tools to the inventory.

Data Science Platform

Different data science platform is as follows:

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1. Anaconda Platform

Anaconda platform is the free and open-source distribution for python and R languages for scientific computing. It simplifies package management and deployment using Conda (‘Package management system’). Anaconda Covers up to 1500 popular data science packages currently used by 15 million users (as claimed by the company). This Platform is available on Windows, Linux, and macOS. Anaconda Navigator GUI is a plus point for the anaconda platform as it is better than CLI. Navigators can search packages on an anaconda cloud or local repository, install them and update them as required.Data Science Platform- Anaconda Platform

For Anaconda platform: https://www.anaconda.com/

2. H2o.ai Platform

H2O.ai is an Open-source and freely distributed platform. It is working to make AI and ML easier. H2O is popular among novice and expert data scientists. H2O.ai Machine learning suite.

  • H2O- Platform to build and produce data models.
  • Deepwater- An Integration with TensorFlow, MXNet, and Caffe for Dl workloads.
  • Sparkling Water – An integration with Apache Spark.
  • Steam – Company’s enterprise offering for building and deploying applications as well as APIs. (Paid version)
  • Driverless AI – A simplified feature for non-technical employees to prepare data, tuning parameters, determine optimal solutions for specific business problems without knowing any technicalities.Data Science Platform- H2Oai

For H2O.ai platform: https://www.h2o.ai/

3. KNIME

KNIME is a free and open-source platform. KNIME uses different data science tools for ML and data mining; its modular data pipelining concept makes it a complete data science platform (Data analytics, reporting, Integration).KNIME’s GUI and JDBC allow the user to work on different data sources for analysis, modelling and visualization with or without programming. KNIME initially started as a pharmaceutical research tool, but the modular concept makes an appropriate choice for different fields.Data Science Platform- Knime

For the KNIME platform: https://www.knime.com/

4. Alteryx Analytics

Alteryx Analytics is one of the leading data science platforms used by many MNCs. The platform is not open-source but designed to make advanced analytics easy for every data expert and the novice. The company currently offers four products under its analytics suite.

  • Alteryx Connect
  • Alteryx Designer
  • Alteryx Promote
  • Alteryx Server

Alteryx’s most popular program is self-service analytics. It empowers BI analysts with a re-usable workflow for self-service data, so you can spend less time preparing data and invest more time analyzing. Its drag-drop interface is also good for non-technical users.

Alteryx analytics

For Alteryx analytics: https://www.alteryx.com/

5. Rapidminer

Rapidminer is an integrated data science platform that provides advanced and predictive analysis. It is used for small and large commercial applications and research, education, training, rapid prototyping, and application development. It is paid software but freely available for 1 logical processer under the AGPL license.

Rapidminer currently offers five products.

  • Rapidminer Studio – It is the platform itself.
  • Rapidminer Auto Model – It is an extension to Studio that accelerates building and validating models.
  • Rapidminer Turbo Prep – It is designed to make data preparation easier. It provides a user interface where your data is always visible front and centre.
  • Rapidminer Server – It is an application-specific server designed for optimized performance.
  • Rapidminer Radoop – It is Integration for Hadoop technology.Rapidminer Platform

For the Rapidminer platform: https://www.rapidminer.com/

6. DataBricks

Databricks is an open-source cloud-based data science platform developed on the apache Spark computing framework. It is developed by the team that developed Apache Spark at the University of California. Databricks unified analytics suite comprises:

  • Databricks Workspace – It handles all analytic processes, from ETL to training models and deployment. (for example, python, R, Java)
  • Databricks Runtime – It prepares clean data at a massive scale and train ML models for your AI applications. (for example, Hadoop, TensorFlow)
  • Databricks Cloud services – As cloud-based, it reduces infrastructure complexity and more time to focus on data problems while keeping data managed and secure (for example, AWS, Azure).databricks

For Databricks: https://www.databricks.com/

7. SAS Unified data science

SAS is one of the oldest Data Science platforms. It offers big data, advanced analytics, and predictive analysis in a single package. SAS Software suite also provides GUI for non-technical and SAS languages for technical users. SAS system module comes with various tools such as Base SAS, SAS/STAT, SAS/ETS, SAS/OR, SAS/QR, SAS/Graph, SAS AF, SAS/Access and many more. SAS Viya is one more product from SAS company: an open, Powerful, unified, and multi-platform-based Platform. It offers a variety of options for installation, such as on-site, Cloud, and hybrid. SAS Viya uses Teradata Data storage sets for its operations.SAS Data Science

For SAS Data Science platform: https://www.sas.com/en_in/software/platform.html

Conclusion

Data Science platform is the need of today’s generation. Today we are producing as much data as never before. Using Data Science tools, we can help our generation make a better life, as described above. The Data Science platform is helping us in many fields.

  • Healthcare and life sciences
  • Information Technology
  • Banking, Financial Services, and Insurance (BFSI)
  • Manufacturing
  • Energy and Utilities
  • Research

The global Data Science platform market projected to grow at a CAGR of 40% for the next 5 to 7 years. During the 2016-17 fiscal year, the Global Data Science platform market accounted for USD 20 billion (According to Data Bridge Market Research). As a Data Science Platform helps us in many fields, we have an acute shortage of workforce to perform the task. According to LinkedIn Workforce Report, more than 151,000 Data Scientist jobs were going unfilled across the U.S only.

Recommended Articles

This has been a guide to the Data Science Platform. Here we have discussed the introduction and different types of data science platform with a detailed explanation. You can also go through our other suggested articles to learn more –

  1. Data Science Tools
  2. Data Science Languages
  3. Data Science Career
  4. Guide to Data Science Algorithms
  5. Data Science Lifecycle
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