Data Scientist
Data science is an intriguing field, and recognition of its potential is rapidly expanding. But there are challenges. You need access to the right data and the flexibility to use a variety of tools of your own choice. The pipeline for data preparation and for model and application deployment needs to be reliable and efficient. And you need to increase the likelihood that stakeholders and IT managers will green-light new projects.
On this page, we provide a range of content – for advanced data scientists to those just getting started – to help you meet these challenges. You will find a rotating selection of foundational material, ideas to help you get inspired, as well as practical tips on key issues that help make your data science projects easier to build and more likely to be successful. You’ll also learn what Hewlett Packard Enterprise (HPE) offers.
The roles of the Data/ML Engineer and Data Scientist can overlap. You may also find content of interest to you on the Data/ML Engineer page. Content on this page changes as new material becomes available or new topics arise, so check back regularly.
Get Inspired
A sampler of new ideas
Learn how innovation may affect your job.
Building a Foundation
Optimize data access
The right data infrastructure gives you direct access to data via a wide range of APIs for a choice in tools.
Watch Deequ: Unit Tests for Data to learn why unit testing isn’t just for code
Read Swarm Learning: Turn Your Distributed Data into Competitive Edge to see how innovative architectures take advantage of the increasingly distributed nature of data
Study the technical paper HPE Ezmeral Data Fabric: Modern infrastructure for data storage and management
Learn best practices in Getting the most from your data-driven transformation: 10 key principles
View the HPE Ezmeral Data Fabric platform page
Working together
Domain expertise helps frame questions, identify useful data, and take action on insights.
Containerization of applications
The open source Kubernetes framework orchestrates containers.
Discover The New Data Science Team: Who’s on First? and learn how multiple roles contribute to a successful data science project
Combine diverse data sets to advance healthcare. Watch Data Saves Lives to learn how
Watch Data Feeds People to see how combined stakholder expertise puts advanced agricultural knowledge to work in the field - literally
Learn how management of large scale Kubernetes clusters is made easier with HPE Ezmeral Runtime Enterprise
Addressing Key Concerns
How do I find and get access to the right data?
Better connections between data producers and data consumers make data science more successful. Check out Getting value from your data shouldn’t be this hard
What can I do to lower the entry barriers to developing new AI/ML/data science projects?
AI/ML projects can and should be run on the same system as analytics projects: Read “Chapter 3: AI and Analytics Together” in the free eBook AI and Analytics at Scale: Lessons from Real-World Production Systems
Read 2nd project advantage: lowering barriers to AI and machine learning
How do I optimize data logistics and preparation efforts to keep them from overwhelming the data science project?
See content for the Data/ML Engineer role
What makes it easier to deal with edge computing in large-scale systems?
How are others doing this?
Check out these real-world case studies
Accelerating Autonomous Car Development with Ready Access to Global Data Fabric
Architecting the World’s Largest Biometric Identity System: The Aadhaar Experience
Skill Up
Munch & Learn technology talkMonthly meetups where you can hear from experts on the newest technologies. Catch up on any you may have missed and register for upcoming talks.
Workshops-on-Demand
Free, in-depth, hands-on workshops that allow you to explore details of a technology by interacting with it. Designed to fit your schedule, these workshops are available 24/7 – from anywhere at any time.
Blog articles and tutorials
7 Questions for Nelson Luís Dias: Atmospheric Turbulence in Chapel
Oct 15, 2024
Streamline and optimize ML workflows with HPE Ezmeral Unified Analytics
Sep 27, 2023
Deep Learning Model Training – A First-Time User’s Experience with Determined – Part 2
May 3, 2022Deep Learning Model Training – A First-Time User’s Experience with Determined - Part 1
Apr 14, 2022
Highlighting key features of HPE Ezmeral Runtime Enterprise Release 5.4
Mar 31, 2022
Writing Deep Learning Tools for all Data Scientists, Not Just Unicorns
Feb 11, 2022
On-Premise Adventures: How to build an Apache Spark lab on Kubernetes
Jun 15, 2021
Exploring Data Fabric and Containers in HPE DEVs new Munch & Learn monthly gatherings
Jan 28, 2021
Association Rule Mining – Not Your Typical Data Science Algorithm
Jan 22, 2021
Streaming Data Pipeline to Transform, Store and Explore Healthcare Dataset With Apache Kafka API, Apache Spark, Apache Drill, JSON and MapR Database
Jan 14, 2021
Spark Data Source API: Extending Our Spark SQL Query Engine
Dec 16, 2020
Fast data processing pipeline for predicting flight delays using Apache APIs: Kafka, Spark Streaming and Machine Learning (part 1)
Oct 21, 2020Datasets, DataFrames, and Spark SQL for Processing of Tabular Data
Aug 19, 2020