With the skyrocketing increase in Big Data, Data Science, the recent buzzword, has become the driving force of today’s advanced data-based enterprises.
While the booming enterprise data is outstripping our capacity to interpret it effectively, the best Data Science platforms are bridging the gap between data understanding and data science — thanks to Artificial Intelligence and Machine Learning.
Let’s unpack our comprehensive guide about the best Data Science tools so you can choose the best tool that suits your business specifications better.
What is a Data Science Platform?
A data science platform is a system of packages with prebuilt scientific techniques for deep learning, machine learning, and other avant-garde analytics that help experts chart out and automate data mining, cleaning, and analysis to get better business insights.
It’s a unified system with tools to enable data scientists to run, track, reproduce, share, and deploy ML models faster and with ease for accelerated data analysis and decision-making process. Thus, your teams can devise strategies and interact with the entire enterprise to turn extracted insights into tangible business value from within the same environment.
A top-end data science system has tools essential to managing a data science project lifecycle —
- Information ideation, preparation, integration, interactive exploration, and visualisation
- Performance engineering
- Model development
- Model deployment
Importance of Data Science Platforms for Businesses
- Improved Collaboration: A high-end data science platform is a centralised system that supports teams across an entire data cycle. Besides enabling faster model deployment, this centralised approach allows teams to work within a single environment and boost team efficacy through capitalisation and reuse.
- Better insights, Faster: These tools are pre-optimised and pre-tested and thus can significantly downsize error rates and inconsistencies. They support effortless integrations and well-documented APIs that can make the model deployment runway significantly shorter.
- Enlarged Amounts of Data: These systems can extract voluminous data sets from various sources and consolidate them on a centralised cloud infrastructure for efficient analysis. Plus, your teams get secure yet prompt access to multiple data types and get data-based and accurate insights in less time.
- Secure Governance: Data science tools provide authorised and auditable outputs through centralised, consistent, and transparent processes.
The Best Data Science Platforms
Apache Spark
Constantly fueling business growth by facilitating large-scale data analytics, Apache Spark, an open-source multi-language data processing engine, tops our list.
Though it does not support automatic code optimisation, Spark can, in a breeze, execute a spectrum of analytics tasks on single-node computers or clusters — from SQL queries and simple data loading to model building and evaluation to batch/near real-time stream processing.
Key Features
- Utilises its abstraction-RDD (Resilient Distributed Datasets) and DAG for top-notch fault tolerance
- Supports EDA (Exploratory Data Analysis) on petabyte-scale data — no need to resort to downsampling
- Spark’s acyclic data flow and DAG execution engine make in-memory processing lightening-fast
- Built on top of Hadoop, Spark allows writing codes in Scala, R, Python, etc.
RapidMiner
Do you need a centralised solution for building, delivering, and maintaining predictive analysis? RapidMiner is a code-free top-end unified system that can bring speed and scale to your business and bolster revenue by improving standardisation and making Data Science fully automated.
Cash in on an integrated Artificial Intelligence, Machine Learning, and data analysis tool that can perform analytics and enable real-time data transformation monitoring to accelerate decision-making.
Key Features
- Supports data integration from different data sources and types — Microsoft SQL, Microsoft Excel, and Access
- RapidMiner streamlines model building by supporting visual, code-based, and automated methods and allows planning out of them effortlessly — no technical flair is required.
- Supports data exploration — interactive graphs, data visualisation, and descriptive statistical analysis
- Boost data analytics and processing speed executing ETL(extract, transform, and load) and data prep in-database.
DataRobot
Released in 2012, DataRobot AI Cloud is a purpose-built automated enterprise-ready system devised for constant optimisation across the complete AI lifecycle.
Though Data Robot offers limited integration functionality with external data sources and types, it democratises and speeds up data science by automating the end-to-end journey, from data extraction to driving value, and enables experts to design, deploy and maintain AI applications and predictive models at scale.
So, instead of struggling with setting things up, data science teams can leverage augmented intelligence to deploy models with a skinny amount of code, collaborate in a centralised environment, and better focus on what they are best at — driving better business insights.
Key Features
- Easy to deploy and multiple deployment options available — in the cloud, on-prem, or as a fully-managed AI system
- It comes with four independent yet integrable products: Automated ML, Automated Time Series, MLOps and Paxata
- Offers ETL and visualisation tools and self-service model delivery
- Splits computation over multi-core servers and employs distributed algorithms for large-scale dataset processing
- Supports Python SDK and APIs for seamless integration of AI models into software and tools
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