DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports Events Over 2 million developers have joined DZone. Join Today! Thanks for visiting DZone today,
Edit Profile Manage Email Subscriptions Moderation Admin Console How to Post to DZone Article Submission Guidelines
View Profile
Sign Out
Refcards
Trend Reports
Events
Zones
Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
  1. DZone
  2. Data Engineering
  3. AI/ML
  4. Data Science Using Oracle Advanced Analytics

Data Science Using Oracle Advanced Analytics

Data science involves a wide array of technologies and statistical algorithms. However, there are some areas in data science that can be automated using scripts and workflows.

Sibanjan Das user avatar by
Sibanjan Das
CORE ·
Dec. 28, 16 · Opinion
Like (4)
Save
Tweet
Share
11.78K Views

Join the DZone community and get the full member experience.

Join For Free

We are now in an era where consumer preferences are changing at lightning speed, social media is making the reviews transparent, and new competitors are appearing out of nowhere. Digitalization is starting to rule the world and employee attrition has increased. To surf these new waves of business challenges, organizations are getting driven by technologies and deploying a variety of new enterprise applications like ERP Systems, CRM (customer relationship management) tools, Big Data, and Cloud-based solutions. Also, organizations are relying on data science technologies and algorithms to be intelligent and proactive.

Data science, which is a relatively new term in the Business and IT industry, has hit the mainstream for investment in the last few years. This is because it provides organizations the opportunity to gain insights about their business and drive proactive planning. Although analyzing data for information or insights has been there since a decade ago, proactive planning is relatively new. Organizations are now able to predict what might happen in the future, which helps them to formulate strategies and plan in advance for possible roadblocks. Statistical models and quantitative analysis sit at the core of data science and measure past performance to predict the future.

But, this is not the end of the story. To keep up with everyone’s expectations, organizations need to attend to the customers as soon as they need service, retain employees before they resign, or identify frauds before they happen. They also have to be operationally efficient and at the same time cost-effective. This requires the ability to apply analytical insights to business operations at the right time. The only way to stay ahead of everyone is to be agile at data-driven, decision-making capabilities. This is possible when algorithms are brought near to the origin of data such as databases and data science routines are automated and integrated to business process workflows.

Data science involves a wide array of technologies and statistical algorithms. This makes it difficult to automate each and every aspect of it. However, there are some areas in data science that can be automated using scripts and workflows.

Oracle Advanced Analytics: Bringing Automation to Data Science

Oracle Advanced Analytics, an analytics product from Oracle, helps to address problem-specific automation and repetitive-tasks automation. It has features like automated data preparation (ADP) and workflow that integrate data science modules to business operations databases. It also has a feature known as predictive queries, one of the forms of automated statistics, for professionals with no statistics knowledge to be able to perform predictive analytics.

Oracle Advanced Analytics comes up with a powerful combination of Oracle Data Miner and Oracle R Enterprise. Oracle Data Miner is a flagship product from Oracle that provides in-database execution of statistical algorithms to make predictions and discover insights. However, it is packaged with a limited number of algorithms. If we want to execute new advanced algorithms in Oracle Data Miner, it requires coding those algorithms in PLSQL, and it is time-consuming. This reduces the flexibility of Oracle Data Miner to implement advanced algorithms quickly.

To overcome this challenge, Oracle engineered open source R to Oracle R Enterprise. This engineered feature complemented the limitations of Oracle Data Miner and also made open source R scalable and enterprise ready for Big Data. This also helped Oracle Advanced Analytics to tap into the resources of the huge contributions of the latest libraries by the open source R community. 

You can learn more about Oracle Advanced Analytics from Oracle's Advanced Analytics page. Of course, you can also check out my book Data Science with Oracle Data Miner and Oracle R Enterprise. It aims to help you learn about Oracle Data Miner and Oracle R Enterprise which are the two components of Oracle Advanced Analytics. The book focuses on database embedded automation workflows for business use cases and provides an overview of the most commonly used data science techniques for business applications. You’ll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to your business systems. The goal of the book is to give you a head start to drive automation and implement data science using Oracle Advanced Analytics. It also contains solutions to some practical data science problems, so that you can learn by example. Please check it out and let me know what you think!

Data science Analytics Open source Algorithm

Opinions expressed by DZone contributors are their own.

Popular on DZone

  • How To Use Terraform to Provision an AWS EC2 Instance
  • What Should You Know About Graph Database’s Scalability?
  • Using JSON Web Encryption (JWE)
  • Why Every Fintech Company Needs DevOps

Comments

Partner Resources

X

ABOUT US

  • About DZone
  • Send feedback
  • Careers
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 600 Park Offices Drive
  • Suite 300
  • Durham, NC 27709
  • support@dzone.com
  • +1 (919) 678-0300

Let's be friends: