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Laurel Miller wrote a new post, Weekly Question #7: Due October 23, on the site Honors Data Science 2 days, 19 hours ago
Leave your response as a comment on this post by the beginning of class on October 23. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]
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Jeremy Shafer wrote a new post, MIS3502.003 – Agenda for 10/16, on the site MIS3502.003 – Fall 2019 2 days, 20 hours ago
Moving on to assignment 8 today. Will also briefly review assignment 7 solution.
-Shafer -
Jeremy Shafer wrote a new post, MIS3502.002 – Agenda for 10/16, on the site MIS3502.002 – Fall 2019 2 days, 22 hours ago
Moving on to assignment 8 today. Will also briefly review assignment 7 solution.
-Shafer -
Sezgin Ayabakan wrote a new post, ICE 8.1 Identifying Key Performance Indicators, on the site Data Science – Fall 2019 – Section 6 2 days, 22 hours ago
Here is the guideline: ICE 8.1
This is not graded.-
Workload efficiency, amount of time spent on multiple tasks.
Harry Scuron and Nick Sarver
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Laurel Miller wrote a new post, In-Class Exercise 8.2: Visualizing KPIs, on the site Honors Data Science 3 days, 2 hours ago
Here is the exercise.
And here is the spreadsheet to complete the exercise [In-Class Exercise 8.2 – OnTime Airline Stats [Jan 2014].xlsx].
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Laurel Miller wrote a new post, 10/16 class, on the site Data-Centric Application Development 3 days, 3 hours ago
Hello everyone,
In class today we will work through an exercise that is very similar in format to an MIS2402 exam.
Also, please be advised that an exam 1 study guide is available here.
Exercise instructions
Zip file -
Chris Dupree‘s profile was updated 3 days, 10 hours ago
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Krishana Parikh and
Ashlin Bungalow Puthenveedu Cheriyan are now friends 3 days, 16 hours ago -
Aleksi Aaltonen wrote a new post, Week 8 in-class exercises, on the site Data Science 3 days, 19 hours ago
Here are the materials for in-class exercises for week 8:
8.1 Identifying Key Performance Indicators
8.2 OnTime Airline Stats.xlsx, Visualizing KPIs -
Aleksi Aaltonen wrote a new post, Week 8 in-class exercises, on the site Data Science 3 days, 19 hours ago
Here are the materials for in-class exercises for week 8:
8.1 Identifying Key Performance Indicators
8.2 OnTime Airline Stats.xlsx, Visualizing KPIs -
Ronak Patel and
Arsh Patel are now friends 3 days, 22 hours ago -
Krishana Parikh and
Arsh Patel are now friends 3 days, 22 hours ago -
Jeremy Shafer wrote a new post, MIS3502.001 – Agenda for class on 10/15, on the site MIS3502.001 – Fall 2019 3 days, 22 hours ago
We will work on assignment 7 today. Also, some important announcements about this week and next.
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Jaedon Rineer changed their profile picture 4 days, 7 hours ago
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Jaedon Rineer changed their profile picture 4 days, 7 hours ago
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Kadey Lam posted an update in the group
Temple MIS 4 days, 8 hours agoHi everyone! I am currently a sophomore majoring in MIS and minoring in ITIE.
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Kadey Lam posted an update 4 days, 8 hours ago
@tuj56821 Hi Elver! Welcome to the MIS community! I also just switched this semester, good luck with your semester.
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Kadey Lam posted an update 4 days, 8 hours ago
@tug64094 Hi Ikenna! I see that you are an MIS major, I am too. Goodluck with your semester!
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Elver Carlos Reyes posted an update in the group
Temple MIS 4 days, 9 hours agoHey Temple MIS! I was previously a CIS major and decided to switch to MIS. I’m excited to see what this brings to my future
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Elver Carlos Reyes joined the group
Temple MIS 4 days, 9 hours ago - Load More
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I usually wear a smart watch, which tells me plenty of KPIs relevant to my daily activity and health. One example is the step count it provides. This is specific and measurable, since the watch counts each step I take while wearing it. It is achievable, since I can walk more to meet different step goals I might have. It’s relevant, since being able to track steps and how much I move throughout the day can have an effect on my overall health. Finally, it is time-variant, since I am able to see step counts across any span of time through the app my watch connects to, and also get information such as weekly averages to compare.
I
I use a sleep cycle app that calculates a weekly average of how much I slept each night. For the SMART criteria, it is specific and measurable in the fact that it measures how many hours and minutes I spent asleep each night. It is achievable in that I can use the data to set new goals for sleep averages each week. It is relevant, as sleep is a big part of my daily life and directly affects my daily performance/energy levels. And it is time-variant, not in just the calculation, but in that I sleep at different times of each day and notice different trends with each night of the week’s sleep data.