Data Crunch | Big Data | Data Analytics | Data Science

The way you organize your data science team will greatly affect your business’s outcome. This episode discusses different structures for a data science team, as well as top down versus bottom up approaches, how to get data science solutions into production organically, and how to be part of the business while remaining in contact with other data scientists on the team. Mark Lowe: Having lived through small scale, two people working, to large scale, thousands of people in your organization, the way that you organize the data science team has dramatic effect on its productivity. Ginette Methot: I’m Ginette, and I’m Curtis, and you are listening to Data Crunch, a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. Building effective data science processes is tough. Mode, the data science platform, has compiled three tips to make it a bit easier:

David Millar is a man bringing analytical solutions to an industry that historically has had little data. But with the explosion of smart devices, that is all changing, and the way utilities operate is as well. David Millar: The way that electricity markets work is that you have what's called the day ahead market. And so the day before, let's say one o'clock tomorrow, markets run, and this is a big optimization problem. Ginette Methot: I'm Ginette Curtis Seare: And I'm Curtis Ginette: And you are listening to Data Crunch, Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation and analytics training and consulting company. Ginette: The father of lean startup methodology once said “There are no facts inside the building so get the heck outside.” The utilities industry is no different. Sometimes the facts that’ll make your machine learning career are waiting j

Simeon Schwarz has been walking the data management tightrope for years. In this episode, he helps us see the hidden organizational and economic impacts that come from leading a data management initiative, and how to understand and overcome the inertia, fears, and status quo that hold good data management back. Simeon Schwarz: Fighting against shadow IT . . . you have to find a way to adopt it, you have to find a way to incorporate it, and you have to find a way to leverage it. You will never be able to completely eliminate it. Ginette Methot: I'm Ginette. Curtis Seare: And I'm Curtis. Ginette: And you are listening to Data Crunch, Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. Ginette: This might come as a surprise to some, but......tools won’t build a data-driven culture.  The right people will.  Read more at mode.com/datadrivenculture. m o d e dot com slash data driven culture. Ginette: Today we speak with S

David Saben is on a mission to make taking tests less painful, and he’s using data to do it. In this episode, he’ll discuss reviving methods developed in 1979 to shorten tests and make them more effective, as well as how to use psychometrics to aid in the design and crafting of an effective test. David Saben: When I see my son who's 11 years old, spending three days and testing when I know there's absolutely no reason for it that you can do that in an hour. Ginette Methot: I'm Ginette Curtis Seare: And I'm Curtis Ginette: And you are listening to Data Crunch Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. The father of lean startup methodology once said “There are no facts inside the building so get the heck outside.” The education industry is no different. Sometimes the facts that’ll make your machine learning career are waiting just outside your office.  Read more at mode.com/mledu m o d e dot com slash M L e

Todd Jones: My name is Todd Jones. I'm the chief analytics officer here at WebbMason analytics. We are a professional services firm helping our clients accelerate their analytic evolution. So I think my journey started about 10 years ago. Uh, I graduated from Princeton with a degree in operations research and financial engineering. So I could have basically taken f two paths. One, I could have went into the financial space or the second path I could have taken was going into the analytics space and I, and I chose the, the analytics space. I joined a very early company called Spry. When I joined. It was about four months old and primarily started off doing a lot of DOD contracting specific to analytics and data. And we eventually built that company to a pretty nice size. We expanded past the DOD space, got into commercial, started consulting with some large, uh, pharmaceutical companies, transportation companies, and really built that company up and then sold that in 2015. Curtis: When you fill that is Web

Today we speak with Professor Ram Bala, an expert in supply chain management analytics, particularly last-mile delivery. He has very interesting insights into how today’s supply chain is evolving. He talks about various methods and algorithms he uses, the specific challenges inherent in doing last mile logistics and deliver, how pricing factors in, and how everyone is trying to catch up to Amazon. Ram Bala: Then there is this great opportunity to actually use the data effectively. But that is a long way to go in terms of coming up with the right algorithms, both on predictions, as well as the optimization to actually get this done in a meaningful way. And if you look at the landscape today in terms of industry, I would say very few companies that actually there yet. Right? I mean, Amazon obviously is a clear example of the leaders in the space, but everyone's trying to get there as well. Ginette Methot: I'm Ginette Curtis Seare: And I'm Curtis Ginette: And you are listening to Data Crunch Curti

Today, our guest, Alain Briancon, will talk to us about how to work with Fortune 500 companies and help them get quick value from their data, how to build a roadmap of incremental value during the data collection and analysis process, how they help predict and incentivize customer purchases, and how to dial in on an idea for successful data science software companies. Alain Briancon: Adding one more question to answer is always easy. The difficult part is what question can I remove and still providing insight. Ginette Methot: I'm Ginette Curtis Seare: And I'm Curtis Ginette: And you are listening to Data Crunch Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. Ginette: If you’re a fortune 1000 company, and your team needs to be trained in Tableau, Statistics, Data Storytelling, or how to solve business problems with data, we’ll fly one of our expert trainers out to your site for a private group training. The most import

Today is a special episode. We welcome three executive guests from different organizations to share their experiences and insights about how data science can best support executive goals. Ginette Methot: I'm Ginette Curtis Seare: And I'm Curtis Ginette: And you are listening to Data Crunch Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. There's a lot going on here at Data Crunch. Just this last week we finalized the merger of Vault Analytics and Lightpost Analytics under the new banner of the Data Crunch Corporation, which improves our capabilities to serve our clients head over to datacrunchcorp.com to check out our training and consulting offerings. For our executive panel, today we'll be talking to Simon Lee, the chief analytics officer from Waiter; Fatma Kocer, who is the vice president of data science

Our guest Andrzej Wolosewicz has had years of experience helping companies define and build machine learning and analytical solutions that have a measurable impact on the business, and he shares with us his experience and expertise. He shares with us the biggest pitfalls he sees companies fall into over an over as they try to implement these initiatives. The problem was there was a lot of activity every month that they were doing, but in terms of progressing, their analytic capabilities were really kind of being able to to grow and be more effective. They weren't, they weren't able to do that. As the saying goes, they had a lot of action but not a lot of progress. Ginette Methot: I'm Ginette. Curtis Seare: And I'm Curtis, and you are listening to Data Crunch, a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Andre Wolosewicz: My name is Andre Wolosewicz. I am currently the director of sales at HEXstream. We are Chicago based analytics and data

Today we’re going to see how a clever idea and the skillful use of data is starting to disrupt how people get credentials. The use case here has the potential to remove gender and racial bias in the hiring process, help companies understand specific talent gaps in their workforce, and help learners find lucrative educational pathways they can take.