{"id":1078,"date":"2023-01-23T16:19:23","date_gmt":"2023-01-23T16:19:23","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/?page_id=1078"},"modified":"2024-04-02T17:00:05","modified_gmt":"2024-04-02T17:00:05","slug":"meeting-of-analytics-program-directors","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/meeting-of-analytics-program-directors\/","title":{"rendered":"Meeting of Analytics Program Directors"},"content":{"rendered":"\n
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Your job is to help cultivate the data analytics leaders of tomorrow. What keeps you up at night? What are the most pressing needs for your program? Your colleagues at universities around the world probably have the same concerns and face similar challenges. The INFORMS Meeting of Analytics Program Directors (MAPD) has become the event for connecting and collaborating with colleagues in the field. Join us for the 7th Annual Meeting of Analytics Program Directors (MAPD) on April 13, 2024 where Analytics Program Directors from around the globe will engage with one another to tackle the issues that impact their programs.<\/p> <\/div>\n<\/div>\n <\/div>\n

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What to Expect From the Meeting<\/h3>\n <\/div>\n<\/div>\n
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The MAPD is a one of kind meeting where program directors and staff have a unique opportunity to meet and discuss relevant trends, challenges, pain points, best (and worst) ways to run an analytics program. The meeting includes programs directors of undergraduate, masters and doctoral programs. This year, we will be discussing how analytics programs have evolved since the first programs launched in 2007. We will discuss what has changed, what needs to change, and how we should be thinking about change. We will be addressing a wide range of questions that we all face – what skills do our students need to be successful after graduation? How should we be thinking about ethics and fairness in machine learning? What is the best way to engage in an interdisciplinary environment? What does a dissertation or thesis in analytics\/data science look like? How do we navigate issues related to intellectual property and confidentiality when collaborating with the private sector?<\/span><\/p> <\/div>\n<\/div>\n\n

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