Data Science

Observability is a fast-growing concept in the Ops community that caught fire in recent years, led by major monitoring/logging companies and thought leaders like Datadog, Splunk, New Relic, and Sumo Logic. It's described as Monitoring 2.0 but is really much more than that. Observability allows engineers to understand if a system works like it is supposed to work, based on a deep understanding of its internal state and context of where it operates. It is the capability of monitoring and analyzing event logs, along with KPIs and other data, that yields actionable insights. An observability platform aggregates data in the three main formats (logs, metrics, and traces), processes it into events and KPI measurements, and uses that data to drive actionable insights into system security and performance.

Gartner has released its 2020 Data Science and Machine Learning Platforms Magic Quadrant, and we are excited to announce that Databricks has been recognized as a Leader. Gartner evaluated 17 vendors for their completeness of vision and ability to execute. We are confident the following attributes contributed to the company's success: The biggest advantage of Databricks' Unified Data Analytics Platform is its ability to run data processing and machine learning workloads at scale and all in one place. Customers praise Databricks for significantly reducing TCO and accelerating time to value, thanks to its seamless end-to-end integration of everything from ETL to exploratory data science to production machine learning. With Databricks, data teams can build reliable data pipelines with Delta Lake, which adds reliability and performance to existing data lakes.

Artificial intelligence's emergence into the mainstream of enterprise computing raises significant issues -- strategic, cultural, and operational -- for businesses everywhere. What's clear is that enterprises have crossed a tipping point in their adoption of AI. A recent O'Reilly survey shows that AI is well on the road to ubiquity in businesses throughout the world. The key finding from the study was that there are now more AI-using enterprises -- in other words, those that have AI in production, revenue-generating apps -- than organizations that are simply evaluating AI. Taken together, organizations that have AI in production or in evaluation constitute 85% of companies surveyed.

Earn your Master's, learn from pioneering Illinois faculty, and gain the data science skills that are transforming business and society. Illinois Computer Science offers a specialized track that includes both MCS degree requirements and data science-focused coursework. This degree is right for anyone who not only wants to learn to extract knowledge and insights from massive data sets, but also wants full command of the computational infrastructure to do so. The Master of Computer Science in Data Science (MCS-DS) leads the MCS degree through a focus on core competencies in machine learning, data mining, data visualization, and cloud computing, It also includes interdisciplinary data science courses, offered in cooperation with the Department of Statistics and the School of Information Science. Data Visualization: Coursework designed to show you how to create effective and understandable data presentations.

Predictive analytics is the technology that enables a look into the future. What data do you need? How do you get started with predictive analytics? What methods can you use?

Data is fundamental to the success of any organization. Data analytics gives insights into customer behaviour which thusly is utilized to fuel the vital activities of the business. Well-curated and comprehensive customer information can open up a universe of new opportunities dependent on solid numbers. Today, data reaches out past hard numbers. While realizing what number of changes you're getting from your site or having the option to figure the return on investment (ROI) of a marketing effort is as yet significant.

New, no-cost community version helps advance the AI landscape. Note: TDWI's editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements. Cnvrg.io, the enterprise data science platform, has released its community version, CORE, amid extended remote work and social distancing to advance ML development and help the data science community leverage its model management and MLOps capabilities at no cost. The data science community has been central to the rapid growth of AI and machine learning innovation.

From working to accelerate insights into Coronavirus, to striving to manage supply chains the midst of social distancing, governments and enterprises are figuring out how to make optimal decisions during times of uncertainty and emotion. Sadly, despite many advances in predictive analytics, AI technologies – unless used within an augmented intelligence framework that combines both machine and human intelligence – often fall short of expectations. Join Genpact's Analytics Business Leader Amaresh Tripathy and guest Dr. Kjell Carlsson from Forrester to discuss how smart organizations are leveraging augmented intelligence to help ensure accuracy and relevancy of decisions and better outcomes. Genpact and Forrester will share examples from Fortune 500 leaders – across banking, consumer goods, retail, life sciences and health care – who are harnessing the power of augmented intelligence during this period of crisis.

Job seekers interact more with advancing tech than they realize as more companies turn to automated tools in talent acquisition. The hiring process has come a long way from the days of paper resumés and cold calls via landline. Online job sites are now staples in talent acquisition, but artificial intelligence (AI) and machine learning are elevating the recruiting and hiring landscape. When asked about the current status of AI and machine learning in hiring, Mark Brandau, principal analyst on Forrester's CIO team said, "All vendors are moving in that direction without question. The power of AI lies in its ability to process high volumes of data at fast speeds, improving efficiency and productivity for organizations. Those same features and benefits can also be applied to the hiring process. "As organizations look to AI and machine learning to enhance their practices, there are two goals in mind," said Lauren Smith, vice president of Gartner's HR practice. "The first is how do we drive more efficiency in the process?

Michael is a hybrid thinker and doer--a byproduct of being a StrengthsFinder "Learner" over time. With 20 years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.