DataFramed

DataFramed

By DataCampTechnology
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DataFramed episodes

  • #37 Data Science and Insurance
    Hugo speaks with JD Long, VP of risk management for Renaissance reinsurance, about applications of data science techniques to the omnipresent worlds of insurance, reinsurance, risk management and uncertainty. What are the biggest challenges in insurance and reinsurance that data science can impact? How does JD go about building risk representations of every deal? How can thinking in a distributed fashion allow us to think about risk and uncertainty? What is the role of empathy in data science?
    1 hr
  • #36 Data Science and Ecology
    Hugo speaks with Christie Bahlai, Assistant Professor at Kent State University, about data science, ecology, and the adoption of techniques such as machine learning in academic research. What are the biggest challenges in ecology that data science can help to solve? What does the intersection of open science and data science look like? In scientific research, what is happening at the interface between data science & machine learning methods, which are pattern-based, and traditional research methods, which are classically hypothesis driven? Is there a paradigm shift occurring here? Listen to find out!

    Links from the show

    • The Bahlai Lab of applied quantitative ecology
    • Christie Bahlai on twitter
    • Hugo's article on What Data Scientists Really Do in Harvard Business Review
    • Hugo's webinar on What Managers Need To Know About Machine Learning
      • 56 min
    • #35 Data Science in Finance
      Hugo speaks with Yves Hilpisch about how data science is disrupting finance. Yves’ name is synonymous with Python for Finance and he is founder and managing partner of The Python Quants, a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading and computational finance. Why are banks such as Bank of America & JP Morgan adopting the open source data science ecosystem? What are the major sub-disciplines of Finance that data science is and can have a large impact in? How has the rise of data science changed the financial world and how the work is done and thought about? Stick around to find out.
      1 hr
    • #34 Data Journalism & Interactive Visualization
      Hugo speaks with Amber Thomas about data journalism, interactive visualization and data storytelling. Amber is a journalist-engineer at The Pudding, which is a collection of data-driven, visual essays. We’ll discuss the ins and outs of what it takes to tell interactive journalistic stories using data visualization and, in the process, we’ll find out what it takes to be successful at data journalism, the trade-off between being being a generalist and specialist and much more. We’ll explore these issues by focusing on several case studies, including a piece that Amber worked on late last year called “How far is too far? An analysis of driving times to abortion clinics in the US.”
      57 min
    • #33 Pharmaceuticals and Data Science
      What are the biggest challenges in Pharmaceuticals that data science can help to solve? How are data science and statistics generally embedded in organizations such as Pfizer? What aspects of the pharmaceutical business run the gamut of nonclinical statistics? Hugo speaks with Max Kuhn, a software engineer at RStudio who was previously Senior Director of Nonclinical Statistics at Pfizer Global R&D. Max was applying models in the pharmaceutical and diagnostic industries for over 18 years.
      1 hr
    • #32 Data Science at Doctors without Borders
      Hugo speaks with Derek Johnson, an epidemiologist with Doctors without Borders. Derek leverages statistical methods, experimental design and data scientific techniques to investigate the barriers impeding people from accessing health care in Lahe Township, Myanmar. If you thought data science was all machine learning, SQL databases and convolutional neural nets, this is gonna be a wild ride as to get the data for their baseline health assessments, Derek and his team ride motorcycles into villages in northern Myanmar for weeks on end to perform in person surveys, equipped with translators and pens and paper because they can’t be guaranteed of electricity. Derek also researches the factors associated with the transmission of hepatitis C between family members and has helped to conduct studies in Uganda, Nepal, and India. All this and more.
      55 min
    • #31 Chatbots, Conversational Software & Data Science
      Hugo speaks with Alan Nichol about chatbots, conversational software and data science. Alan is co-founder and CTO of Rasa, who build open source machine learning tools for developers and product teams to expand bots beyond answering simple questions. Which verticals are conversational software currently having the biggest impact on? What are the biggest challenges facing the fields of chatbots and conversational software? What misapprehensions do we as a society have about these technologies that experts such as Alan would like to correct? And how can we all build chatbots and conversational software ourselves?
      58 min
    • #30 Data Science at McKinsey
      Hugo speaks with Taras Gorishnyy, a Senior Analytics Manager at McKinsey and Head of Data Science at QuantumBlack, a McKinsey company. They discuss

      • the role of data science in management consulting,
      • what it takes to change organizations through data science,
      • how the different moving parts of data science have evolved over the past decade and in which direction they’re heading.

        • You’ll see the impact that data science can have not only in tech, but also in such various verticals as retail, agriculture and the penal system. Taras will also take us through the 5 steps required to change organizations through data science, all of which are necessary. Can you guess what they are?
        We're really excited to have Taras on the show as DataCamp has had a long relationship with McKinsey, including that McKinsey uses DataCamp for training.

        57 min
      • #29 Machine Learning & Data Science at Github
        Omoju Miller, a Senior Machine Learning Data Scientist with Github, speaks with Hugo about the role of data science in product development at github, what it means to “use computation to build products to solve real-life decision making, practical challenges” and what building data products at github actually looks like.
        Machine learning has the power to automate so much of the drudgery around data science & software engineering, from automated code review to flagging security vulnerabilities in code, and from recommending repositories to contributors to matching issues with maintainers and contributors and identifying duplicate issues.
        And just in case that’s not enough, they'll discuss github as a platform for work, not just technical, and, as Omoju has called it, “a collaborative work environment centered around humans.”
        59 min
      • #28 Organizing Data Science Teams

        What are best practices for organizing data science teams? Having data scientists distributed through companies or having a Centre of Excellence? What are the most important skills for data scientists? Is the ability to use the most sophisticated deep learning models more important than being able to make good powerpoint slides? Find out in this conversation with Jacqueline Nolis, a data science leader in the Seattle area with over a decade of experience. Jacqueline is currently running a consulting firm helping Fortune 500 companies with data science, machine learning, and AI. This interview is with Jacqueline Nolis, but at the time of recording, she went by Jonathan Nolis.

         Links from the show 

        • Jacqueline Nolis' website 
        • You're relying on data too much: making decisions worse, not better, by Jacqueline Nolis 
        • Hiring data scientists (part 1): what to look for in a candidate, by Jacqueline Nolis 
        • Jacqueline on Twitter 
        • For more, see our page here 
        1 hr

      About DataFramed

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      Data science and machine learning are making the impossible possible—whether it is awe-inspiring results from language models, or huge gains in computational biology. However, as data science is…