Data Science for Decision Makers

This course is taught at the United States Naval Academy, Annapolis, MD.

Spring 2022 Course Policy Statement

Online Textbook: Computational and Inferential Thinking: The Foundations of Data Science.

Lessons Link: Data Science for Decision Making.

Welcome to SM208 Data Science for Decision Makers (formerly known as SM219 Introduction to Statistics), in CH102, at the U.S. Naval Academy. We will be learning how statistics can be used to support decision making. This is in line with recommendations made by the 2016 GAISE College Report, endorsed by the American Statistical Association. It is also a direct response to remarks by our Superintendent, Vice Admiral Sean Buck, who encouraged us to develop a data science curriculum to make midshipmen more effective officers. The material in this course is based off pioneering work in the Data8 program at U.C. Berkeley but we have modified their curriculum to adapt to the needs of the Naval Academy, the Navy and the Marine Corps. Our curriculum development efforts have been supported by two generous grants from the Office of Naval Research.

Data science is a modern approach to statistics that blends computation with statistical theory. We will use Python, the industry-leading programming language for data science. Despite its broad capabilities, our course will focus on using Python for data manipulation. visualization, and statistical computation. Students wishing further instruction in computer programming are encouraged to take SI286: Programming for Everyone. Course content includes data organization and manipulation, data visualization with an emphasis on briefing senior leadership, probabilities and Bayes' Rule for updating probabilities in light of new information, hypothesis testing, confidence intervals via bootstrapping, applications of the Central Limit Theorem and an introduction to distributions, regression and inference for regression, predictive modeling and an introduction to machine learning, an overview of ethics in machine learning, and classes devoted to critical thinking in the context of decision making with data science.

This course is mainly taken by midshipmen with majors in the School of Humanities and Social Science and many examples have been chosen from these areas, as well as in applications of interest to the Navy and the Marine Corps.

Online Textbook: Computational and Inferential Thinking: The Foundations of Data Science.

Lessons Link: Data Science for Decision Making.

Professor Mee Seong Im

Office: Chauvenet Hall 342, Department of Mathematics, United States Naval Academy, Annapolis, MD 21402

Office Phone Number: (410) 293-6776

Email: im [at] usna [dot] edu

Weekly Extra Instructions (E.I.): You may come and go as needed. I will have them every week via Google Meet.

Monday nights: 2000-2100 [use the Google Meet link for our class]
Wednesday nights: 2000-2100 [use the Google Meet link for our class]

Extra Instructions (E.I.): Please request for them at least 2 working days in advance (this excludes evenings, weekends, and Federal Holidays) in order to give Professor Im enough time to do the scheduling. My preference is to meet in-person in my office, rather than over Google Meet or Zoom. Each EI will be scheduled for a maximum of 30 minutes.

For ALL EIs, please attend prepared, with homework attempted in advance and with specific questions in mind.

Mathematics Lab: I will be at the Math Lab (free tutoring room in Chauvenet Hall, Room 130) on Mondays during the 3rd period (0955 - 1045).

Midshipmen Group Study Program (MGSP; free peer-tutoring and assistance from three amazing leaders), in CH107.

Sundays, 2100-2200, with MIDN Brett Bonin
Tuesdays, 2000-2100, with MIDN Caroline Bilbray-Kohn
Thursdays, 2100-2200, with MIDN Alexander Turner

Math Lab, in CH130. This is run by the Department of Mathematics faculty members.

The Math Lab is open Mondays through Fridays, 1st through 6th period: 0755-1520. Walk-ins are welcome!
I will be at the Math Lab on Mondays during the 3rd period (0955 - 1045).

Academic Center, US Naval Academy, Annapolis, MD. Faculty and Staff (Professional Tutors).

The Academic Center offers free one-on-one professional tutoring during the day (appointments may be necessary).
Free evening professional tutoring is available (walk-ins are encouraged).
The Academic Center also offers tutoring via online platforms, like Google Meet and Zoom, if you cannot leave your room due to covid or some other reason.

Study Groups.

There are at least 200 midshipmen taking this course. You are ENCOURAGED to form study groups among your classmates.

This Math Lab schedule highlights those periods where instructors or former instructors of SM208 staff the lab.

Online Textbook: Computational and Inferential Thinking: The Foundations of Data Science.

Lessons Link: Data Science for Decision Making.

Spring 2022 Academic Calendar

Class Periods

Additional Course material in the Google Drive.

The Section Leaders and the Assistant Section Leaders will take class attendance. Please type A if they are Absent, L(min) if they are late, along with the number of minutes, and D(min) if they have departed early, along with the number of minutes.

Section 5001, 1330 - 1420, CH102:

Section Leader: MIDN Kiera Kinsey
Assistant Section Leader: MIDN Deonte Hayes
Assistant Section Leader: MIDN Soleil Xie

Section 6001, 1430 - 1520, CH102:

Section Leader: MIDN Bernardo (Benny) Ortiz
Assistant Section Leader: MIDN Xinbo Wang
Assistant Section Leader: MIDN Kaylah Gillums

Breakdown of the points:

Final Exam (1): 25%

Exams (2): 30%

Homework (11; I will drop the lowest homework grade): 15%

Lab (9; I will drop the lowest lab assignment. List of lab partners): 10%

Reading Quizzes (32; I will drop the lowest reading quiz assessment): 05%

Quizzes (6; I will drop the lowest quiz grade): 10%

Class Participation 05%

Late work will be docked 25% credit.

If you miss any graded work, it is your responsibility to come and see me to make-up any graded assignment.

Upload all assignments to Blackboard. Your grades will be regularly posted on Blackboard also. Note that I went ahead and set-up Blackboard so that it automatically drops your lowest Homework, Lab, Reading Quiz, and Quiz grades from the Weighted Total (Final Average).

If you complete your assignments and upload them onto Blackboard on time (all uploads are time-stamped), you will get a bonus point (this bonus opportunity depends on the assignment so make sure to read all instructions on each file).

Depending on how the midshipmen do in the course, there MAY be a curve on your FINAL grades.

Upload all assignments onto Blackboard.

Labs: open notes, book, python, search engines (internet). Collaboration is allowed.

If you are collaborating on a lab assignment, only one assignment per pair needs to be submitted but everyone must work on the lab from their own computers (key in their own answers). Up to two names and two alpha may be on one submitted lab. You must have a different partner for each lab or points will be docked. List of lab partners.

Homework: open notes, book, python, search engines (internet). Collaboration is allowed.

Whether you are collaborating or not, each person must submit the homework assignment.

Reading Quizzes: open notes, book, python, search engines (internet). Collaboration is not allowed.

Take the reading quiz before your reading.
Take the same reading quiz after your reading.
I will allow you to take the Reading Quiz as many times as you want, and I will record your highest grade as your Reading Quiz grade. So when you obtain the grade of your choice, stop (since Google Form will record the last submitted quiz as your grade).

Quizzes: open notes, book, python, search engines (internet). Collaboration is not allowed.

Exams: open notes, book, python, search engines (internet). Collaboration is not allowed.

Final Exam: open notes, book, python, search engines (internet). Collaboration is not allowed.

On all of these graded assessments, in particular, where collaboration is not allowed, please feel free to ask me questions if you need further clarification on a problem.

Online Textbook: Computational and Inferential Thinking: The Foundations of Data Science.

Lessons Link: Data Science for Decision Making.

If you are off the yard, you need to VPN in order to link Jupyter and Google Drive. You can VPN using Cisco AnyConnect Secure Mobility Client or https://sslvpn.usna.edu/ . Then use a Command Prompt to link them: mklink /J "C:\Users\m123456\DFS_Link_Jupyter" "G:\My Drive"

We will use the python template from each day for interactive lectures and discussions. However, do NOT delete cells as you may run into errors or you may introduce bugs when you run your python template. Also, when I grade your homework, labs, quizzes, exams, etc., my python codes may not read or misread your solutions as well. So if you accidentally delete some cells, the best course of action would be to download the (original) python template again from the Lessons Link.

This is NOT a programming course; this is a data manipulation course.

Create folders for each lesson/day, i.e., Day 01, Day 02, Day 03, Day 04, etc.

Practice exams will be available in order to better prepare you for your exams.

For every 1 hour of class, it is recommended that you put in 2 hours of individual or small group study time outside of class. This is not mandatory but it is just a recommendation.

You are NOT allowed to use your cell phones during class, including doing anything that is not related to this class (you are wasting my time and everyone else's time). This include texting, browsing social media, news, online shopping, etc. websites.

You are also NOT allowed to do other course homework assignments, send out emails, or do other work in my class.

If I find you dozing off in my class, I will ask you to get some water and stand for the remaining of the class. There have been a number of midshipmen dozing off in my class in previous semesters, which resulted in poorer performance since they are not paying attention; it soon followed that many of them are unable to keep up with the course material. Get enough rest each night, and learn to manage your time (if you need help with time management, see me).

Keep the Midshipmen Honor Code in mind every day: the midshipmen will NOT lie, cheat, and steal. Cheating and plagiarism in any form will NOT be tolerated, including submitting other students' files or work. So do not email or share your files.

You may have gotten the few points if you cheat or plagiarize but in the long run, you are getting NOTHING out if it. Lying, cheating, or stealing will make you a weak, indecisive, unreliable, and untrustworthy leader with a lack of knowledge and preparedness to speedily lead other men and women.

ALL opinions and questions are VALUABLE. Treat each other with dignity and respect. Do NOT use inflammatory or offensive language, sarcasm, or raised voices.

Online Textbook: Computational and Inferential Thinking: The Foundations of Data Science.

Lessons Link: Data Science for Decision Making.

Additional Course material in the Google Drive.

Grade Quality Points Point Values

A 4.00 93 - 100.0

A- 3.70 90 - 92.99

B+ 3.30 87 - 89.99

B 3.00 83 - 86.99

B- 2.70 80 - 82.99

C+ 2.30 77 - 79.99

C 2.00 73 - 76.99

C- 1.70 70 - 72.99

D+ 1.30 67 - 69.99

D 1.00 60 - 66.99

F 0.00 0 - 59.99

Online Textbook: Computational and Inferential Thinking: The Foundations of Data Science.

Lessons Link: Data Science for Decision Making.

Additional Course material in the Google Drive.

Announcements

Lesson 1 (~~Tues 11 Jan~~): online

Cameras must be on for the duration of the class.
The students in Bancroft should be in the uniform of the day and utilize headphones.
The students who are at home should be in professional attire (“business casual”), and should attend from an appropriate workspace, e.g. seated at a desk or table in a quiet room.
To be counted as present, a student must be in an appropriate workspace and visible on camera for the duration of the class, i.e., logging in while driving somewhere does not count.
It was very nice meeting you all! Remember that if you have any questions, don't be afraid to ask! =o)

Lesson 2 (~~Wed 12 Jan~~): online

Cameras must be on for the duration of the class.
The students in Bancroft should be in the uniform of the day and utilize headphones.
The students who are at home should be in professional attire (“business casual”), and should attend from an appropriate workspace, e.g. seated at a desk or table in a quiet room.
To be counted as present, a student must be in an appropriate workspace and visible on camera for the duration of the class, i.e., logging in while driving somewhere does not count.
We will use Breakout Rooms so that you can work on lab00 in pairs. Since we have odd number of midshipmen in each section, I will allow a group of 3 to work together.

Additional Info (~~Wed~~ 12 ~~Jan~~):

I will hold an EI via Google Meet starting tonight: 2000-2100.

Lesson 3 (~~Fri 14 Jan~~): online

Cameras must be on for the duration of the class.
The students in Bancroft should be in the uniform of the day and utilize headphones.
The students who are at home should be in professional attire (“business casual”), and should attend from an appropriate workspace, e.g. seated at a desk or table in a quiet room.
To be counted as present, a student must be in an appropriate workspace and visible on camera for the duration of the class, i.e., logging in while driving somewhere does not count.

Additional Info (~~Mon 17 Jan, Federal Holiday~~):

I will hold an EI via Google Meet tonight: 2000-2100.

Lesson 4 (~~Wed 19 Jan~~):

Lesson 5 (~~Fri 21 Jan~~):

Lesson 6 (~~Mon 24 Jan~~): I will be on an aircraft carrier over the weekend and today.

I will be not be at the Math Lab today. LCDR Melissa Szurovy will be at the Math Lab today during the 3rd period.
5th period: LCDR Timothy Brock will be your instructor. Your quiz 00 will be emailed to you. All instructions are in the email.
6th period: Professor Rob Curry will be your instructor. Your quiz 00 will be emailed to you. All instructions are in the email.

Additional Info (~~Mon 24 Jan~~): Professor Im will be on an aircraft carrier so she will not hold an EI tonight (we will be back very late on Tuesday ~~or Wednesday~~). ~~Monday's evening EI will be postponed to this~~ ~~Tuesday or~~ ~~Thursday evening~~.

Additional Info (~~Tues 25 Jan~~):

~~If Professor Im is back in Annapolis by Monday night, then she will hold an~~ ~~EI tonight, at~~ ~~2100-2200~~ ~~(so that my EI does not conflict with MIDN Caroline~~ ~~Bilbray-Kohn's MGSP~~). ~~I will confirm this EI on Monday night or Tuesday morning via here and via an email~~. ~~I have been notified that we will hopefully return by Tuesday night [posted on Thurs 20 Jan 2022 at 2045]~~. ~~It's possible that there could be a delay by another day in getting back to Annapolis but hopefully, we will be on schedule [posted on Fri 21 Jan 2022 at 1530]~~.
~~If Professor Im is not back in Annapolis by Monday night, then this is because she is traveling back today with other USNA faculty members. So she will postpone her~~ EI to ~~Thursday night, at~~ ~~2000-2100~~ ~~(so that my EI does not conflict with MIDN Alexander Turner's MGSP).~~ ~~I will confirm this EI~~ ~~by Tuesday night or Wednesday morning~~ ~~via here and via an email~~.
Professor Im is back from USS George H.W. Bush CVN 77.

Lesson 7 (~~Wed 26 Jan~~):

~~If there is a substitute during class today, then that means that I'm still on route to Annapolis. This means I won't be back in time for Wednesday evening EI~~.

Additional Info (~~Wed 26 Jan~~):

Professor Im will have her regularly scheduled EI tonight, via Google Meet, ~~as long as she's back in town by Tuesday night~~.

Additional Info (~~Thurs 27 Jan~~):

~~Professor Im will have~~ an EI ~~tonight, via~~ ~~Google Meet, at~~ ~~2000-2100~~.
No Google Meet EI tonight. Professor Im is helping with Math Open House. She will be in Chauvenet Hall 155 from 1900 - 2100. Please come and see me in person for an in-person EI if you have any questions!

Lesson 8 (~~Fri 28 Jan~~):

Lesson 9 (~~Mon 31 Jan~~):

3rd period: I will be at the Math Lab today.
4th period: I will be at the Math Lab today.
We will have an in-class Quiz 01 today, at the start of the class. Review the following topics and be ready to write code or answer questions about them:
- - - Identify variables as categorical or quantitative (reading required prior to Day 06, available on Day 05; or see Day 06 lecture notes)
    - Use the where method to filter a table, keeping only the rows you want (Lab 01 on Day 05)
    - Use the num_rows method to determine the number of rows in a table (Lab 01 on Day 05)
    - Make and interpret a histogram (Day 04 lecture notes, completed version with solutions attached to this email and now available on Day 04 class website).
    - Sorting a table (reading required prior to Day 08, available on Day 07)
    - Extracting data from a table (Day 04 lecture notes)
    - Using the select and drop commands appropriately (We talked about this in class on Day 08 but I can't find a precise reference; if you are confused about this please look at this section of the textbook)
    - Drawing horizontal bar graphs (reading required prior to Day 06, available on Day 05)
    - Grouping (Day 06 lecture notes)
    - Print out the Reference Sheet, which you may use on your quizzes and exams.
For the remainder of the semester, we plan to scale back the amount of assistance as the semester wears on.

Lesson 10 (~~Wed 2 Feb~~):

Lesson 11 (~~Fri 4 Feb~~):

I have emailed out feedback on your HW00, LAB00, LAB01, and QUIZ01 via an attached text (txt) file; each midshipman was given a blanket +1 bonus point on each of the assignments (except QUIZ01), even if you did not submit your assignment early (as requested on the file). This email was sent out from gmail since that is linked to our Excel and python files. So please check your spam folder.

Lesson 12 (~~Mon 7 Feb~~):

Upload your python file "Storytelling with Data" to Blackboard to get today's class participation credit (20 points).
Since some of you did not receive at least one of the following files (feedback on HW00, LAB00, LAB01, and QUIZ01) on Friday 4 Feb 2022, I have made some edits to my Excel and python files and emailed out the feedback again. If you still have not received them, please check your spam folder. Then please let me know via an email, during class, or during an EI.

Additional Info (~~Tues 8 Feb~~):

Feedback to LAB02 has been emailed out to everyone; each midshipman was given a blanket +1 bonus point on LAB02, even if you did not submit your assignment early (as requested on the file).

Lesson 13 (~~Wed 9 Feb~~):

Feedback to HW01 has been emailed out today. From this point on, there won't be a blanket +1 bonus point for everyone. You must submit your assignment on time in order to receive a +1 bonus point.
Thank you for letting me know that you haven't received any of my feedback, except feedback on HW00. I have contacted someone about my python files and we will troubleshoot them as soon as possible.

Lesson 14 (~~Fri 11 Feb~~):

Redo Quiz 00 today, at the beginning of class. The maximum out of your two Quiz 00 scores will be your Quiz 00 grade.

Additional Info (~~Sun 13 Feb~~):

Quiz 00 and Quiz 00 redo were graded today. The maximum of the two grades has been posted as your Quiz 00 grade.
A personalized Google Drive folder has been created for each midshipman. All the feedback, including feedback to QUIZ00, are in your Google Drive folder, and I will continue to drop all feedback to your python notebooks into your Google Drive folder.

Lesson 15 (~~Mon 14 Feb~~):

LAB03 has been graded. Some feedback is in your individualized Google Drive folder.
HW02 has been graded. Some feedback is in your individualized Google Drive folder.
No evening EI tonight. Professor Im is on her way to Brown University.

Lesson 16 (~~Wed 16 Feb~~): Midshipmen-Driven Review (study/review at your own pace)

There are excellent practice tests on the Lessons Link. You can also go through python lecture notes, put together notes/summaries (you can write on your reference sheet), and make corrections to HWs, quizzes, and labs with other midshipmen. As you saw on Wednesday (via a class discussion), googling rarely helps us when we are looking for specific python command lines! So start organizing your files because your notes are the best reference, and this is indeed the best way to study!
5th period: Professor Nick Moore will be substituting for me today.
6th period: Professor Van Nguyen will be substituting for me today.
No evening EI tonight. Professor Im is at Brown University.
Starting from all assignments that are due on or after Wed 23 Feb, I will begin to take off points if you submit your assignments after the due date. So please manage your time accordingly and submit them on time.

Lesson 17 (~~Fri 18 Feb~~): Test 00

All resources are allowed on Test 00, even googling. However, collaboration is not allowed. Check your email for Test 00 and when you are done, upload Test 00 to Blackboard; make sure to hit "submit" or "ok" button on the lower right corner of your browser so that your file gets sent to me.
5th period: LT Kevin Hawxhurst will be substituting for me today.
6th period: LT Kevin Hawxhurst will be substituting for me today.
Starting from all assignments that are due on or after Wed 23 Feb, I will begin to take off points if you submit your assignments after the due date. So please manage your time accordingly and submit them on time.

Additional Info (~~Sun 20 Feb~~):

TEST00 has been graded and your feedback is in your Google Drive folder.

Additional Info (~~Mon 21 Feb~~):

No EI tonight since today is a Federal Holiday. But if you really need an EI tonight, please email me to let me know that you plan to be there.
A generous curve has been applied to your TEST00.
Your 6-week grades have been submitted and Midshipman Academic Performance Reports (MAPRs) have been submitted.
Remember to submit your assignments early for 1 bonus point on your labs and homework.
Starting from all assignments that are due on or after Wed 23 Feb, I will begin to take off points if you submit your assignments after the due date. So please manage your time accordingly and submit them on time.

Lesson 18 (~~Wed 23 Feb~~):

Remember to submit your assignments early for 1 bonus point on your labs and homework.
Starting from all assignments that are due on or after Wed 23 Feb, I will begin to take off points if you submit your assignments after the due date. So please manage your time accordingly and submit them on time.

Additional Info (~~Thurs 24 Feb~~):

HW03 and LAB04 have been graded. The feedbacks are in your Google Drive folder.
From Professor Will Traves.
- - - There is an opportunity to complete a similar test to Test 00 to obtain (or keep) your grade adjustment. Here are the details: Complete the attached ipynb file (using the five attached csv files; check your email or go here) and upload to Test 00 Redo on Blackboard.
    - Due date: Thursday March 3 at 2359. After the due date, this opportunity will be removed from Blackboard.
    - If you scored above 18 on Test 00 (out of 20), then you need not submit a test redo.
    - If you are between 10 and 18, then your grade will be raised 1.5 points if you complete the test redo and get a grade equal to or higher than 75%.
    - If you scored below 10, then your current grade will rise to 60% after doing the test redo (so you will receive 2 additional points to your test grade).

Lesson 19 (~~Fri 25 Feb~~):

~~6th~~ 5th period (1430-1520) only: Public Affairs Office (PAO) camera crew will visit us and record you (there will be no more than 3 people with 1 camera for about 25 minutes of footage). Please be on your best behavior.
Remember to submit your assignments early for 1 bonus point on your labs and homework.
Starting from all assignments that are due on or after Wed 23 Feb, I will begin to take off points if you submit your assignments after the due date. So please manage your time accordingly and submit them on time.

Lesson 20 (~~Mon 28 Feb~~):

3rd period: I will be at the Math Lab today.
4th period: I will be at the Math Lab today.
Remember to submit your assignments early for 1 bonus point on your labs and homework.
Starting from all assignments that are due on or after Wed 23 Feb, I will begin to take off points if you submit your assignments after the due date. So please manage your time accordingly and submit them on time.

Lesson 21 (~~Wed~~ 2 ~~Mar~~):

~~We have in-class QUIZ02 today. It will cover probability and distributions~~. Today's quiz has been postponed to Friday.
Remember to submit your assignments early for 1 bonus point on your labs and homework.
Starting from all assignments that are due on or after Wed 23 Feb, I will begin to take off points if you submit your assignments after the due date. So please manage your time accordingly and submit them on time.

Lesson 22 (~~Fri~~ 4 ~~Mar~~):

We have in-class QUIZ02 today. It will cover probability and distributions.
Remember to submit your assignments early for 1 bonus point on your labs and homework.
Starting from all assignments that are due on or after Wed 23 Feb, I will begin to take off points if you submit your assignments after the due date. So please manage your time accordingly and submit them on time.

Additional Info (~~Sat 5 Mar~~):

QUIZ02 has been graded. Some feedback is in your individualized Google Drive folder.
HW04 has been graded. Some feedback is in your individualized Google Drive folder. Starting from today, 25% has been deducted if you submitted your assignment late.
TEST00 REDO has been graded. Some feedback is in your individualized Google Drive folder. Another bonus of 1.5 or 2 points have been added to your TEST00 grade.

Lesson 23 (~~Mon 7 Mar~~):

I will be not be at the Math Lab today. LCDR Melissa Szurovy will be at the Math Lab today during the 3rd period.

Lesson 24 (~~Wed 9 Mar~~):

A few of you are behind on your Reading Quizzes. If you are behind, please complete them and then take a snapshot of your screen, making sure that it includes your name, Reading Quiz number, and your grade. Save your snapshots and then email them to me.

Lesson 25 (~~Fri 11 Mar~~): "No Midday Break" schedule

5th period: 1155 - 1245
6th period: 1255 - 1345
QUIZ03 today. This is not a group quiz.
A few of you are behind on your Reading Quizzes. If you are behind, please complete them and then take a snapshot of your screen, making sure that it includes your name, Reading Quiz number, and your grade. Save your snapshots and then email them to me.

Additional Info (~~Fri 11 Mar~~):

HW05 has been graded. Some feedback is in your personalized Google Drive folder.
LAB05 has been graded. Some feedback is in your personalized Google Drive folder.
QUIZ03 has been graded. Some feedback is in your personalized Google Drive folder.

Spring Break (~~Sat 12 Mar - Sun 20 Mar~~):

A few of you are behind on your Reading Quizzes. If you are behind, please complete them and then take a snapshot of your screen, making sure that it includes your name, Reading Quiz number, and your grade. Save your snapshots and then email them to me.
Please keep in mind that Friday 29 Apr is the LAST DAY to submit ALL of your late assignments since I need to put together many python, csv, Excel, and txt files, even if I am grading just one late assignment. So please work on your assignments now and submit them as early as possible.
Have a wonderful Spring Break!

Additional Info (~~Sat 12 Mar~~):

Assignments that were submitted late have been graded.

Additional Info (~~Mon 14 Mar~~):

No evening Extra Instruction (EI) tonight since it's the week of Spring Break.

Additional Info (~~Wed~~ 16 ~~Mar~~):

No evening Extra Instruction (EI) tonight since it's the week of Spring Break.

Lesson 26 (~~Mon 21 Mar~~):

Please keep in mind that Friday 29 Apr is the LAST DAY to submit ALL of your late assignments since I need to put together many python, csv, Excel, and txt files, even if I am grading just one late assignment. So please work on your assignments now and submit them as early as possible.
Evening Extra Instruction (EI) will resume tonight.

Lesson 27 (~~Wed 23 Mar~~):

Evening Extra Instruction (EI) will resume tonight.

Lesson 28 (~~Fri 25 Mar~~):

Please keep in mind that Friday 29 Apr is the LAST DAY to submit ALL of your late assignments since I need to put together many python, csv, Excel, and txt files, even if I am grading just one late assignment. So please work on your assignments now and submit them as early as possible.

Lesson 29 (~~Mon 28 Mar~~):

Additional Info (~~Tues 29 Mar~~):

HW06 has been graded. Some feedback is in your personalized Google Drive folder.

Lesson 30 (~~Wed 30 Mar~~):

LAB06 has been graded. Some feedback is in your personalized Google Drive folder.
Quiz 04 today (note that Quiz 05 is the last quiz for the semester). Other instructors and I have decided that collaboration is not allowed on the last two quizzes.
QUIZ04 has been graded. Some feedback is in your personalized Google Drive folder.

Lesson 31 (~~Fri 1 Apr~~): Midshipmen-Driven Review (study/review at your own pace)

There are excellent practice tests on the Lessons Link.
- - - Do SM208 Practice Test 01A today. Solutions are posted at the above link.
Please keep in mind that Friday 29 Apr is the LAST DAY to submit ALL of your late assignments since I need to put together many python, csv, Excel, and txt files, even if I am grading just one late assignment. So please work on your assignments now and submit them as early as possible.

Lesson 32 (~~Mon 4 Apr~~): Midshipmen-Driven Review (study/review at your own pace)

There are excellent practice tests on the Lessons Link.
- - - Check your emails for SM208 Practice Test 01B. I will email out the solutions at the end of class.
Please keep in mind that Friday 29 Apr is the LAST DAY to submit ALL of your late assignments since I need to put together many python, csv, Excel, and txt files, even if I am grading just one late assignment. So please work on your assignments now and submit them as early as possible.

Lesson 33 (~~Wed 6~~ ~~Apr~~): Test 01

HW07 has been graded. Some feedback is in your personalized Google Drive folder.
TEST01 has been graded. Some feedback is in your personalized Google Drive folder.

Lesson 34 (~~Fri 8 Apr~~):

HW08 will be due on Monday 11 April by 2359 instead of today.
Friday 29 Apr is the LAST DAY to submit ALL of your late assignments since I need to put together many python, csv, Excel, and txt files, even if I am grading just one late assignment. So please work on your assignments now and submit them as early as possible.

Lesson 35 (~~Mon 11 Apr~~):

HW08 is due today by 2359.

Additional Info (~~Tues 12 Apr~~):

HW08 has been graded. Some feedback is in your personalized Google Drive folder.

Lesson 36 (~~Wed 13~~ ~~Apr~~):

Lesson 37 (~~Fri 15 Apr~~):

Friday 29 Apr is the LAST DAY to submit ALL of your late assignments since I need to put together many python, csv, Excel, and txt files, even if I am grading just one late assignment. So please work on your assignments now and submit them as early as possible.

Lesson 38 (~~Mon 18 Apr~~):

Lesson 39 (~~Wed 20~~ ~~Apr~~):

Additional Info (~~Thurs 21 Apr~~):

HW09 has been graded. Some feedback is in your personalized Google Drive folder.
LAB07 has been graded. Some feedback is in your personalized Google Drive folder.

Lesson 40 (~~Fri 22 Apr~~):

Friday 29 Apr is the LAST DAY to submit ALL of your late assignments since I need to put together many python, csv, Excel, and txt files, even if I am grading just one late assignment.

Additional Info (~~Sat 23 Apr~~):

LAB08 has been graded. Some feedback is in your personalized Google Drive folder.

Additional Info (~~Sun~~ 24 ~~Apr~~):

HW10 has been graded. Some feedback is in your personalized Google Drive folder.

Lesson 41 (~~Mon 25 Apr~~):

Quiz 05 is today. This is the last quiz of the semester.

Additional Info (~~Tues 26 Apr~~):

QUIZ05 has been graded. Some feedback is in your personalized Google Drive folder.

Lesson 42 (~~Wed 27 Apr~~):

Watch a 23-minute video on an invention with consequences.
Work on "DC Police Case Study" as a class. Upload this python file to Blackboard at the end of class.
We did not finish the python file "DC Police Case Study". Complete it and (re)upload to Blackboard by Friday 29 April 2022 at 2359 to get 2 bonus points!

Lesson 43 (~~Fri 29 Apr~~):

Work on "Khost call" as a class. In your python file, you must clearly indicate your choice: carry out or cancel the mission. You must provide careful reasoning, supported by computations and data visualizations, that support your position. Upload your python file to Blackboard at the end of class for credit.
If you want me to update your Reading Quiz grades, take a screenshot of the Reading Quiz which includes the Reading Quiz number, your name and grade, and email the screenshots to me.
Today is the LAST DAY to submit ALL of your late assignments since I need to put together many python, csv, Excel, and txt files, even if I am grading just one late assignment. After today (at 2359), I will not have time to go back and grade 61+ assignments (so your grade for that assignment will remain a zero).
Update: I have decided to accept late assignments until Sunday 1 May 2022 at 2359. So please continue to upload any completed assignments to Blackboard until this Sunday night. Have a great weekend!

Lesson 44 (~~Mon 2 May~~): Midshipmen-Driven Review (study/review at your own pace)

Work on SM208 Practice Final Exam A together as a class. We completed 23-24 problems.
~~Last evening~~ ~~Monday Extra Instruction~~ ~~(EI) tonight. Please let me know if you plan to be there. If no one plans to attend tonight's EI, then it will be canceled~~. The evening EI has been cancelled. Email me to schedule an in-person EI.

Additional Info (~~Tues 3 May~~):

ALL late assignments have been graded. With the exception of Wednesday's in-class assignment which should be submitted to Blackboard, this is the grade you have upon entering the Final Exam room next week.

Lesson 45 (~~Wed 4 May~~): Midshipmen-Driven Review and Student Opinion Forms (SOFs)

Finish the rest of SM208 Practice Final Exam A together as a class. Upload this file to Blackboard when you are done.
- - - Submit your file for full credit.
    - Submit a completed file for full credit + 2 bonus points.
    - If you submit the solutions file from the course website, you will get 0 points.
Student Opinion Forms (SOFs)
~~Last evening~~ ~~Wednesday Extra Instruction~~ ~~(EI) tonight. Please let me know if you plan to be there. If no one plans to attend tonight's EI, then it will be canceled~~. The evening EI has been cancelled. Please email me to schedule an in-person EI.
Enjoy the concert on the yard tonight!

Final Exam (~~Tues 10 May~~): You have 3 hours to take the exam.

Please bring a pencil or a pen. A portion of the Final Exam will be on paper (you do not need to purchase the blue book from the Midshipmen bookstore; you should write on the test booklet and I will provide plenty of paper).
Bring your laptop charger on the day of your Final Exam.
Before the time is up (3 hours), you will need to hand in your paper exam booklet to me and upload your python file to Blackboard.
Section 5001 (21 midshipmen): 1300 - 1600 in CH110
Section 6001 (19 midshipmen): 1300 - 1600 in CH107
The following is the procedure for your Final Exam.
- - - STEP 1: Your personalized Final Exam (python and csv files) will be emailed to you, where your alpha code is embedded in the document itself. This eliminates the need for you to put in your own alpha code.
    - STEP 2: After answering each question, hit CTRL + S or the save icon. Failing to save your own file regularly or uploading a BLANK python file may result in a very low Final Exam Grade since you will NOT be allowed to resubmit your Final Exam after you leave the Exam Room.
Good luck!
I enjoyed having ALL of you in my class this spring! Best wishes on all of your exams and have a wonderful summer!

Alternate Final Exam (~~Wed 11 May~~): Professor Will Traves will proctor the Alternate Final Exam.

1930 - 2230 in CH155

When to use the t-distribution or the normal distribution?

You must use the t-distribution when the population standard deviation σ is not known or the sample size is small, i.e., n < 30. General Rule: If σ is not known, then using t-distribution is correct. If σ is known and n ≥ 30, then using the normal distribution is correct.

Online Textbook: Computational and Inferential Thinking: The Foundations of Data Science.

Lessons Link: Data Science for Decision Making.