10 episodes

This is a podcast from the Statistics, Data Science and Data management (SDM) Core of the HIV Center for Clinical and Behavioral Studies at the New York State Psychiatric Institute and Columbia University. The SDM leadership, Martina Pavlicova, Cale Basaraba, and Richard Buchsbaum, along with guests have freewheeling discussions about statistics, data management, and analytical issues related to research and clinical trials.

The Analytic Angle SDM Core

    • Science
    • 5.0 • 1 Rating

This is a podcast from the Statistics, Data Science and Data management (SDM) Core of the HIV Center for Clinical and Behavioral Studies at the New York State Psychiatric Institute and Columbia University. The SDM leadership, Martina Pavlicova, Cale Basaraba, and Richard Buchsbaum, along with guests have freewheeling discussions about statistics, data management, and analytical issues related to research and clinical trials.

    Do we read papers the same way?

    Do we read papers the same way?

    In this episode, we talk about our different approaches to reading published scientific papers.

    • 10 min
    Has data quality improved over time?

    Has data quality improved over time?

    In this episode we chat about how storing and managing research data has changed in the last three decades, discussing what has improved and what has stayed the same over time.

    • 21 min
    Grant writing pet peeves

    Grant writing pet peeves

    In this episode, we discuss common issues we encounter when we help with grant applications, especially the pet peeves that really bother us.

    • 17 min
    Favorite science movies

    Favorite science movies

    In this episode, we discuss our favorite movies that depict scientific research, statistics, or data analysis.

    • 20 min
    How is data science different than statistics?

    How is data science different than statistics?

    Data science encompasses many different tools and statistical techniques. In this episode, we discuss data science and the differences between machine learning and traditional statistics.

    • 14 min
    How many "questionnaires" are too many?

    How many "questionnaires" are too many?

    In this episode, we discuss how many measures to include when designing a study from our perspectives as data scientists -- how many questionnaires are too many?

    • 15 min

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