Amid a turbulent time for the news media and a groundbreaking time for the advancement of data analytics, Bloomberg is positioned to apply the benefits of AI and machine learning to news gathering, information gathering, and more. Bloomberg CTO and head of data science Gideon Mann joins All Analytics Radio. Mann's work touches a wide range of issues, from how data science can be used for news gathering and reporting and fact checking.

Can AI and Machine Learning Fix Fake News?

Big data and analytics are critical to the success of an enterprise today, but doing analytics right isn’t easy. The expert guests on All Analytics Radio share their advice and opinions on what it takes to succeed with big data and analytics initiatives. Learn about strategies for customer intelligence, the Internet of Things, predictive analytics, data privacy, visualization, the analytics talent shortage, and other challenges and opportunities that face the data science community.

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Join InformationWeek and AllAnalytics Radio as we welcome top quantitative recruitment expert, Linda Burtch. She is founder and managing director of the quantitative executive recruitment firm, Burtchworks, and has worked in the space for decades. Burtch shares her advice for quantitative professionals looking to keep their careers on track -- and properly paid -- in 2018 and beyond.

An Interview With Data Science Recruiter Linda Burtch

 Why are some retailers outperforming the competition? What secrets do the top Omnichannel retailers share?  Join AllAnalytics Radio as we welcome retail experts Brian Kilcourse and Paula Rosenblum to share the results of their research on how some retailers are outperforming the market, even in today's tough environment. Kilcourse and Rosenblum are analysts at RSR Research, specializing in insights and advice for the retail industry.  Learn what factors you should focus on to make your business one of those overperformers.

A2 Radio: Analytics for Omnichannel Success

Lean Analytics author Alistair Croll joins AllAnalytics radio on Monday, Nov. 27 at 1 pm ET/10 am PT. In the 4 years since Lean Analytics was first published, the book has helped companies determine their business models and growth stages and identify the One Metric That Matters. Following this path can help companies reduce risk and move faster in product development and customer satisfaction, following the Lean Startup methodology. Lean Analytics grows out of another book, Lean Startup, which spells out a methodology for organizations to reduce risk and improve product development by creating a minimum viable product and then engaging in iterative product development that incorporates customer feedback. A number of companies have implemented this methodology, including Intuit, GE, and Dropbox. Building on that idea, the book Lean Analytics helps organizations measure their progress and make better decisions along the way. A preface to the book describes it this way: "Lean Startup helps you structure your progress and identify the riskiest part of your business, then learn about it quickly so you can adapt. Lean Analytics is used to measure that progress helping you ask the most important questions and get clear answers quickly." Croll is a frequent speaker at industry conferences around the world on the topic of analytics and lean startups. In this show we'll talk about:  What is Lean Analytics and why do you need it What companies are using Lean Analytics, and how does it work for them What has changed in the field of analytics since the book first hit the market 4 years ago What mistakes and best practices are there for Lean Analytics   

Lean Analytics for 2018

What do black holes, hedge funds, and glaucoma have in common? They are all subjects that Intel Chief Data Scientist Bob Rogers has worked on over the course of a long career in data and analytics. And they are all examples of the varied problems where advanced analytics can be applied. These days Rogers brings his experience with data science, AI, machine learning, and related technologies to a broad range of applications as he helps enterprises with work on their big important projects. It's part of a program in place at Intel to help spread the knowledge and implementation of advanced analytics to businesses and other organizations. AllAnalytics Radio welcomes Bob Rogers as our guest on Wednesday, Nov. 1 at 1 pm ET/10 am PT. We're excited to hear about what he's working on today. We are going to ask him about AI, Machine Learning, and talk about some projects that apply these technologies to solve some of society's problems. As Intel Chief Data Scientist, Rogers has a unique view into the issues, challenges, and trends across multiple industry verticals when it comes to these technologies. We'll ask him about the trends he sees in the field, the most interesting projects he has encountered, and some of the mistakes he has seen as organizations work to implement these technologies. We'll also ask him for his recommendations on how to tackle these problems. What are the best practices? What are good choices for initial big-impact projects?

Intel Chief Data Scientist Explores Advanced Analytics Potential

Students of data science who are entering universities tend to have two things in common. First, they excel at and/or love math. They are mathematicians. Second, they have a passion for fixing a particular problem. Maybe they think younger students could be able to do better in school. Maybe they are looking for a cure for a particular disease. Maybe they see issues with financial reporting that they know they could fix if only they had skills and tools. Now consider the people who did not pursue a degree in data science. They have other careers. But they still want to be able to use data and analytics to help solve problems. These people are so-called Citizen Data Scientists. It's not a formal job title. These people may have other titles, such as sales representative, marketing director, social media associate, or something else. Data may not even be an official part of their job descriptions. But they want to use data to make a difference. Maybe they have taken some data or statistics-related courses here or there. Maybe they use their analytics skills in their jobs, and maybe they use them in a hobby or some other way. They are not full-time data scientists. But they are passionate about solving a problem, asking the right questions, and finding answers.  They care and are willing to learn and work. These individuals can bridge the gap between mainstream self-service analytics by business users and the advanced analytics techniques of data scientists, according to market research firm Gartner. Citizen data scientists in an enterprise organization can strengthen the commitment to analytics adoption. These individuals possess the domain knowledge so that they know the right questions to ask. And these individuals are already embedded within functional departments of the organization. They are on the front lines, dealing with the every day challenges that analytical insights can help influence and resolve. On Thursday, Oct. 26, at 1 pm ET, All Analytics Radio will assemble a panel of our expert bloggers to talk about citizen data scientists. We'll discuss who they are and how they can help. Are you a citizen data scientist? Do you want to reach out to potential citizen data scientists inside your organization in order to strengthen your organization's data analytics practice? Then you won't want to miss this show.

Can AI and Machine Learning Fix Fake News?

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