Data Science

The raging Australian and Amazon wildfires have raised a burning question for all of us - why the very technology, that has been a major facilitator to human evolution and growth could not predict, manage or control its destruction? To those of us who are in the business of technology, it is time to ask a few tough questions in our boardroom meetings and take ownership of solving the problem. After all, what is growth worth if the planet itself is in peril? As someone who has witnessed the digital revolution unfold, I may not have a full-proof plan to address the climate emergency, in fact, we don't even have the visibility of all evolving technologies that may be required to solve the climate emergency. But, I am clear and convinced that we have to start now and start with the available technologies which in their own right are very powerful and transformational.

"You start off with business intelligence, analytics, big data … and then add things like AI, machine learning, [and] deep learning into that analytics …

It is envisioned that Jenkins and its plugin ecosystem can play a key role in supporting, facilitating and accelerating the integration of ML workflow components and orchestrating their reproducible execution. Currently, Python and interactive computational notebooks (such as Jupyter and Zeppelin) are the dominant software tools for teaching, composing and executing machine learning workflows. Interactive notebooks have proven valuable data exploration and teaching tools as they adopt a'literal programming' paradigm where code fragments, results, instructions and documentation are integrated into a single UI, resembling a familiar paper'notebook'. However, interactive notebooks present significant limitations to the adoption of good software engineering principles (test-driven development, code versioning, reproducibility, etc) as well as scaling-up to data sizes typically used in DS production environments [2]. We propose the development of a Jenkins plugin that will integrate machine learning tasks and algorithms to build ML data systems that are easier to develop, test, deploy and maintain.

Location: US, Central Region OR Toronto, Canada Talend is a 600 employee, recent IPO, big data integration software company with deep open source roots. Well-funded, with over $100 million raised to date and continued rapid growth, Talend is the second largest independent open source company in the world. We are hiring Pre Sales Engineers to continue to build a proactive, customer-facing organization that ensures customers are getting value from Talend's products and solutions. We are seeking Engineers to join the sales team and support the increasing demand from our direct sales. Our portfolio of products has expanded from purely Data Integration to include Data Quality (DQ), Master Data Management (MDM), Enterprise Service Bus (ESB) and Big Data.

Using cognitive, cloud-based solutions -- such as outage prediction models -- gives utilities the opportunity to take a proactive stance against impactful weather. It is critical for utilities to determine the level of impact weather can have on their system and take the appropriate actions in advance of both major storms and everyday changes in weather patterns. This reality introduces a key question for energy providers: How can predictive analytical tools create operational and financial benefits for their organizations?

With all the buzz in the information technology industry around artificial intelligence (AI) and machine learning (ML) you'd think that every organization was using these tools or planning for how they are going to use them. After all, the promise is that AI and ML will help organizations harness the ever-growing volumes of data being generated by automating and augmenting human analytic processes and decision-making.

From minimizing accidents, traffic management, ticketing and preventive maintenance of fleets, AI has the potential to transform the transportation sector. The adoption of new technologies has helped the transportation sector innovate and evolve over the years. Today it is time for the industry to leverage Artificial Intelligence ( AI). AI, a technology that provides machines the ability to think like humans, is transforming the industry. The application of AI in transportation can help the industry in several areas including passenger safety, traffic management, and energy efficiency, amongst others.

In anticipation of his upcoming conference presentation at Predictive Analytics World for Business Las Vegas, May 31-June 4, 2020, we asked Kumaran Ponnambalam, Analytics Architect at Cisco, a few questions about their deployment of predictive analytics. Catch a glimpse of his presentation, Using Association Rules Mining for Segmentation and Profiling, and see what's in store at the PAW Business conference in Las Vegas. Q: In your work with predictive analytics, what behavior or outcome do your models predict? A: My models deal with natural language understanding for contact center voice calls. They transcribe the calls and derive call summaries, intent and sentiment based on the transcriptions.

Are you keeping up with internal communication trends? Of course the basics of communications never changes, we seek to gain the attention of an audience with a relevant message they will engage with. However, just like any other industry, the technology and channels of communications changes over time, and it's wise to stay current. It is important to note that trendy doesn't equal effective. This means that you need to stay up to date on what works and what doesn't to maximize effective communication within your company.

The purpose of this course is to teach about how to use Python and machine learning in order to predict sports outcomes. It takes you through through all the steps, from collecting data using a web crawler to making profitable bets based on your predicted results. The course is built around predicting tennis games, but the things taught can be extended to any sport, including team sports. The course includes: 1) Intro to Python and Pandas. This course is geared towards people that have some interest in data science and some experience in Python.