Department of Mathematics
United States Naval Academy
Annapolis, MD
I am teaching:
Section 3001 MWF3 (0955 - 1045), MI006,
Section 4001 MWF4 (1055 - 1145), MI006.
Table of Contents
If you are consistently missing classes, it is YOUR RESPONSIBILITY to stay on top of the course material, as well as stay ahead of the course material. If you need an extension on an assignment, you need to contact me and ask for my permission. An absence does not mean I will automatically give you an extension on an assignment. Also see this link on LATE WORK.
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Bring laptop chargers to class. If your laptop dies during class, I will NOT automatically give you an extension to submit your quizzes and tests at a later time.
Bring a mouse to class if that will help you (and speed things up) when coding.
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Important: for all the python files that you are uploading to Blackboard, make sure to:
replace LASTNAME in the file name with your last name and
in the python file, replace [...,...,...] with your alpha [123456].
Otherwise, you will receive 0 on that assignment because a computer will not be able to detect that you submitted an assignment.
Collaboration/discussion is encouraged, but each person must upload your lab and homework to Blackboard.
You do not need to cite your classmates/peers.
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If you registered for this course late, please read everything on this page. Also talk to your peers to find out what you missed. You can also talk to me before and after class, as well as during EIs to find out what you missed.
The main text is Computational and Inferential Thinking, The Foundations of Data Science by Ani Adhikari and John DeNero with contributions by David Wagner and Henry Milner. It is available for free in an interactive online version at: https://www.inferentialthinking.com/chapters/intro.html. We have supplemented the text with readings created specifically for midshipmen. As well, bring your computer to class each day.
Online Textbook: Computational and Inferential Thinking: The Foundations of Data Science
Lessons Link: Spring 2024 Data Science for Decision Making ⭐★★★★⭐
Reference Sheet: Basic Lines of Code
Installing Python: Python Installation Instructions
Spring 2024 Course Policy Statement: Information about the Course
Welcome to SM208 Data Science for Decision Makers (formerly known as SM219 Introduction to Statistics), in MI006, 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.
There are four goals for this course, in brief:
Call on standard statistical tools. [This includes conducting hypothesis tests, constructing confidence intervals, fitting predictive models such as linear regression or k-nearest neighbors.]
Identify appropriate methods. [This includes both statistical and visualization methods.]
Properly interpret statistical results. [In particular, interpret p-values, z-scores, t-scores, and confidence intervals.]
Critically assess statistical analysis. [Using the vocabulary of the Paul-Elder model for critical thinking.]
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 (but please let me know after class if you plan to stop by). I will have them every week:
Mondays: 1230-1330 in CH342
Wednesdays: 1230-1330 in CH342
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.
Math Lab will start on the week of 15 Jan 2024. See below for the schedule.
MGSP will begin on the week of 15 Jan 2024 in CH104.
MIDN Dean Arnold (3/C)
Available on Thursdays at 2030-2130.
MIDN Thomas DeVito (2/C)
Available on Wednesdays at 2100-2200.
MIDN Addy Schofield (2/C)
Available on Sundays at 2100-2200.
MIDN Loren Steinberg (2/C)
Available on Mondays at 2030-2130.
For one-on-one tutoring, contact the Academic Center directly for their availability.
Section 3001:
Section Leader: MIDN Jamison Wheeler
Assistant Section Leader: MIDN Samantha Murphy
Assistant Section Leader: MIDN Nicolas Krause
Section 4001:
Section Leader: MIDN Aidan Kehoe
Assistant Section Leader: MIDN Camille Johnson
Assistant Section Leader: MIDN Conor Farrell
Here is how I will compute your percentage grade:
Homework (10, dropping the lowest homework at the end of the course) 10%
Labs (4) 10%
Quizzes (5, dropping the lowest quiz at the end of the course) 10%
Tests (2) 40%
Final Exam (1) 30%
Please upload your lab, homework, quizzes, tests, and the final exam onto Blackboard.
I am currently trying to change the setting in Blackboard so that you have multiple (many) attempts to upload your assignments into Blackboard in case you accidentally upload a wrong python file. Please give me several days to do this. Blackboard re-vamped itself over the winter break so it is currently difficult to find the settings to make this change.
I have edited Blackboard so that you can now re-upload your files in case you upload a wrong python file.
Your lowest grade in Homework and Quizzes will be dropped from your weighted final grade.
A percentage grade will determine your letter grade:
A (grade ≥ 90.0),
A- (≥ 87.0),
B+ (≥ 83.0),
B (≥ 80.0),
B- (≥ 77.0),
C+ (≥ 73.0),
C (≥ 70.0),
C- (≥ 67.0),
D+ (≥ 63.0),
D (≥ 60.0),
F (< 60.0).
These cut-offs may be reduced (but never raised) at the end of the course.
Though most class information is located on our course website, grades will be published at instructor discretion. Late work without a previously agreed-upon extension will be docked 25% credit and will not be graded if turned more than 7 days past the original due date. Six and 12 week grades will be computed using the above proportions, adjusted because of the absence of some items. I understand that you sometimes need to miss class for movement orders. You will need to make up any graded work that you missed, but do note that we drop some grades at the end of the course.
If you were absent, you may ask me for an extension to make-up your work. I will thus give you an extention by ONE class day. So it is YOUR responsibility to follow-up with me if you were absent and need to get an extension. As you are in a university, it is ALWAYS your responsibility to regularly go to MGSP, Math Lab, and talk to other midshipmen and stay on TOP of your work.
Sat 11 May: Have a wonderful and safe summer!
Day 1 (Tues 9 Jan): Course Introduction, Course Folder Setup, Anaconda Troubleshooting
NO MIDDAY BREAK schedule due to the weather.
No EI this afternoon.
Day 2 (Wed 10 Jan): Jupyter Notebooks
TWO HOUR DELAYED START schedule today due to the weather.
No EI this afternoon.
LAB 00 assigned
Day 3 (Fri 12 Jan): Univariate Data: Tables, Arrays, Histograms
Continue Day 02 Lecture Notes.
Start on Day 03 Lecture Notes.
Day 4 (Wed 17 Jan): Univarite Data: Manipulating Tables
LAB 00 due by 2359
HW 00 assigned
Please go through Day 3 and Day 4 lecture notes in small groups.
You may also work on your lab00 or hw00. Collaboration is allowed.
Section 3001 substitute: Professor Marius Ionescu
Section 4001 substitute: Professor Marius Ionescu
Day 5 (Fri 19 Jan): NO CLASSES TODAY [SNOW DAY] ❄ ❅ ❆ ❄ ❅ ❆
Day 6 (Mon 22 Jan): Univariate Data: Categorical, Histogram, Bar Graphs
Go over Day 4: Univarite Data: Manipulating Tables
Day 7 (Wed 24 Jan): Multivariate Data: Sorting, Scatterplots, Exploratory Data Analysis (EDA)
Go over Day 4: Univarite Data: Manipulating Tables [finish the last few questions at the end]
Example Zip File
Go over Day 5: Univariate Data Simple Models [finish this lecture notes on your own (there is just 1 problem here); the answers are posted on Day 6]
HW 00 due by 2359
HW 01 assigned
Day 8 (Fri 26 Jan): Data Manipulation, Functions
Go over Day 7: Multivariate Data: Sorting, Scatterplots, Exploratory Data Analysis
QUIZ 00 [this quiz will cover Day 01 - Day 05 material]
You have 20 minutes to complete and upload the quiz to Blackboard.
Will be given as a password-protected zip folder.
I will write down the password on the board on the day of the quiz.
If you cannot get jupyter notebook open by yourself on the first quiz, you will receive 10% penalty.
To ensure you are set-up for success, there is a practice zip folder to test on under Day 07 Website Folder. The password is "password". If you can unzip it, save it in a local folder, and open the jupyter notebook, you should be good to go for the quiz.
Objectives that may be on Quiz 00:
how to read a data table from a CSV file
how to use the .column, .select, and .with_column functions
how to use .column with another function (such as .mean) to calculate an average
how to use .where and a predicate (such as are.equal_to) to filter a data table
how to use .sort to sort the values of a table from low to high or high to low
make visualizations such as scatter plot, horizontal bar graph, and histogram
interpret a histogram
Day 9 (Mon 29 Jan): Multivariate Data: Applying Functions
Go over Day 8: Data Manipulation, Functions
Go over Day 9: Multivariate Data: Applying Functions
HW 02 assigned
Day 10 (Wed 31 Jan): Multivaritate Data: Lab
Section 3001 ONLY: finish last two questions on Day 9 Lecture Notes
LAB 01 assigned
HW 01 due by 2359
Day 11 (Fri 2 Feb): Advanced Data Manipulation: Joining and merging
Day 12 (Mon 5 Feb): Advanced Data Manipulation: Pivot Tables
HW 02 due by 2359
Day 13 (Wed 7 Feb): Advanced Data Manipulation: Branching and Function Wrapping
Go over Day 12: Advanced Data Manipulation: Pivot Tables
Go over Day 13: Advanced Data Manipulation: Branching and Function Wrapping
QUIZ 01
LAB 01 due by 2359
HW 03 assigned
Day 14 (Fri 9 Feb): Advanced Visualization: Storytelling
Go over Day 12: Advanced Data Manipulation: Pivot Tables
Go over Day 13: Advanced Data Manipulation: Branching and Function Wrapping
Go over Day 14: Advanced Visualization: Storytelling [go through this on your own]
Day 15 (Mon 12 Feb): Review
Go through any of the 4 practice tests in preparation for Friday's in-class test.
Day 16 (Wed 14 Feb): Review
Go through any of the 4 practice tests from Day 15 in preparation for Friday's in-class test.
HW 03 due by 2359 [this is NO LONGER due by Mon 12 Feb]
Day 17 (Fri 16 Feb): Test 00
This is an in-class exam during your regularly scheduled class.
Day 18 (Wed 21 Feb): Probability
Day 19 (Fri 23 Feb): Probability
HW 04 assigned
Day 20 (Mon 26 Feb): Expectation and Variance (Split Day 19/20 into 3 lectures)
Day 21 (Wed 28 Feb): Binomial Random Variables
Let's talk about variance and standard deviation a little bit more before moving onto binomial random variables.
Complete Probability Practice (it's under Day 21/22) and upload no later than Wed 6 March by 2359 for +10 bonus points on your lowest LAB grade.
Day 22 (Fri 1 Mar): Normal Random Variables
HW 04 due by 2359
HW 05 assigned
Complete Probability Practice (it's under Day 21/22) and upload no later than Wed 6 March by 2359 for +10 bonus points on your lowest LAB grade.
Day 23 (Mon 4 Mar): Sampling and Iteration
Day 20: let's talk about the z-score before moving onto Day 23 notes.
Complete Probability Practice (it's under Day 21/22) and upload no later than Wed 6 March by 2359 for +10 bonus points on your lowest LAB grade.
Day 24 (Wed 6 Mar): Center and Spread and Central Limit Theorem
QUIZ 02 (25 minutes, 8 problems)
Complete Probability Practice (it's under Day 21/22) and upload no later than Wed 6 March by 2359 for +10 bonus points on your lowest LAB grade.
Day 25 (Fri 8 Mar): Variability of Sample Average
Day 24 Notes: complete it
HW 05 due by 2359
Have a great Spring Break!!
Day 26 (Mon 18 Mar): Hypothesis Testing: One Variable
HW 06 assigned
Day 27 (Wed 20 Mar): Hypothesis Testing Lab
LAB 02 assigned
Everyone, please re-download radios.csv file. You've been using an old radios.csv file. But all the work that you did should remain the same.
Day 28 (Fri 22 Mar): Quiz, HW/Lab Review
Complete Practice Quiz 03 by Wed 27 Mar at 2359 for +5 bonus on your highest quiz grade.
Day 29: (Mon 25 Mar): Hypothesis Testing: Two Sample, A/B Testing
Complete Practice Quiz 03 by Wed 27 Mar at 2359 for +5 bonus on your highest quiz grade.
HW 06 due by 2359
HW 07 assigned
Day 30: (Wed 27 Mar): Hypothesis Testing: Two Sample, A/B Testing
Complete Practice Quiz 03 by Wed 27 Mar at 2359 for +5 bonus on your highest quiz grade.
QUIZ 03 (will be given at the beginning of class), 25 minutes, 8 questions.
Day 31 (Fri 29 Mar): Bootstrapping
Section 3001 substitute: Professor Jad Salem
Section 4001 substitute: Professor Marius Ionescu
LAB 02 due by 2359
Day 32 (Mon 1 Apr): Bootstrapping and Review
HW 07 due by 2359
4 practice tests
Day 33 (Wed 3 Apr): Review
4 practice tests
Day 34 (Fri 5 Apr): Test 01 (17 questions, worth a total of 23 points)
Section 3001 substitute: Maj Mitch Graves
Section 4001 substitute: Maj Mitch Graves
Day 35 (Mon 8 Apr): Introduction to Prediction
Day 36 (Wed 10 Apr): Introduction to Correlation
Day 37 (Fri 12 Apr): Regression Lines
4th period only: MIDN Zeta Williamson will complete Day 36 Correlation notes (the last 3 questions)
Additional notes from Day 36: Correlation does not imply causation
Day 38 (Mon 15 Apr): Regression Lines Variability
HW 08 assigned
Day 39 (Wed 17 Apr): Inference for Regression
Day 40 (Fri 19 Apr): Machine Learning: Train/Test Sets, Classification, Accuracy
HW 09 assigned
Day 41 (Mon 22 Apr): Ethical Issues in Data Science
HW 08 due by 2359
Work on HW08
Work on HW09
Go through today's python file
Go through Practice Quiz 04 python file
I'm taking several midshipmen to the U.S. Air Force Academy. Please be on your best behavior with Professor Traves!
Section 3001 substitute: Professor Will Traves
Section 4001 substitute: Professor Will Traves
Day 42 (Wed 24 Apr): Khost Call Lab You'll be working on a DIFFERENT project (see below for Lab 03 instructions)
Partner List; upload 1 python file and 1 csv file per group to Blackboard.
Points: LAB03 is worth 2 LABS, i.e., 200 points.
Python collaboration website: cocalc.com
Details:
Find a data set with at least 2 variables.
For full credit, use techniques from ALL three sections of the course listed below, and then ANSWER ALL of the questions below. All techniques and answers must be in your python file.
Due: Monday 29 April 2024 at 2359
Late Work: will be docked 25% of your grade. All assignments are due by Wednesday 1 May 2024 at 2359 (last day of the semester). USNA faculty members are not allowed to accept any assignment after 1 May.
Data Analysis
Visualization (.hist, .barh, .scatter, .plot)
.group (Day 5)
Functions and applying them using .apply (Day 9)
.where to filter through the table (Day 11)
.sort (Day 7)
Hypothesis Testing
General hypothesis testing (Day 26)
A/B testing (Day 29)
Bootstrapping of slope of line of best fit, with results explained on a confidence interval you deem is appropriate. (Day 31)
Linear Regression and Machine Learning
Correlation (Day 36)
Regression (Day 37 and 38)
Predictions using Regression (Day 39)
k-nearest neighbors clustering (Day 40)
Questions to Answer (answer each question in a separate line in python)
Which visualizations help us to see the big picture quickly?
Does time of year or temperature influence your data set?
What affects your data set?
Are there any correlations among your variables?
Compute the average for each variable.
Can we get a sense of how the averages may change with new data?
What sort of predictions can you make?
Day 43 (Fri 26 Apr): Khost Call Lab You'll be working on a DIFFERENT project (instructions are under Day 42)
QUIZ 04
HW 09 due by 2359
Day 44 (Mon 29 Apr): Review & SOFs
SOF
LAB03
Partner List; upload 1 python file and 1 csv file per group to Blackboard.
Points: LAB03 is worth 2 LABS, i.e., 200 points.
Python collaboration website: cocalc.com
Details:
Find a data set with at least 2 variables.
For full credit, use techniques from ALL three sections of the course listed below, and then ANSWER ALL of the questions below. All techniques and answers must be in your python file.
Due: Monday 29 April 2024 at 2359
Late Work: will be docked 25% of your grade. All assignments are due by Wednesday 1 May 2024 at 2359 (last day of the semester). USNA faculty members are not allowed to accept any assignment after 1 May.
Data Analysis
Visualization (.hist, .barh, .scatter, .plot)
.group (Day 5)
Functions and applying them using .apply (Day 9)
.where to filter through the table (Day 11)
.sort (Day 7)
Hypothesis Testing
General hypothesis testing (Day 26)
A/B testing (Day 29)
Bootstrapping of slope of line of best fit, with results explained on a confidence interval you deem is appropriate. (Day 31)
Linear Regression and Machine Learning
Correlation (Day 36)
Regression (Day 37 and 38)
Predictions using Regression (Day 39)
k-nearest neighbors clustering (Day 40)
Questions to Answer (answer each question in a separate line in python)
Which visualizations help us to see the big picture quickly?
Does time of year or temperature influence your data set?
What affects your data set?
Are there any correlations among your variables?
Compute the average for each variable.
Can we get a sense of how the averages may change with new data?
What sort of predictions can you make?
Review
Day 45 (Wed 1 May): Review
Section 3001 substitute: Professor Marius Ionescu
Section 4001 substitute: Professor Marius Ionescu
Review
Final Exam (Mon 6 May): 1300 - 1600
Section 3001: CH002 (20 midshipmen)
Section 4001: CH001 (20 midshipmen)
Alternate Final Exam (Fri 10 May): 0755 - 1055
Room: CH160 (2 midshipmen, with Professor Mark Magsino)
Wed 24 Jan:
Lecture Notes answers will be posted on the Lessons Link at the end of each day so that you can continue to work through them at your own pace.
Day 05 Lecture Notes answer:
Question 1.
# We've broken the computation down into smaller steps -- always a good idea when coding
nba_grouped = nba.group('Position', ...)
nba_fewer_cols = nba_grouped.drop(..., ...)
nba_renamed = nba_fewer_cols.relabel(make_array(..., ...),
make_array(..., ...))
nba_renamed.... # make horizontal bar graph
Answer to Question 1.
# We've broken the computation down into smaller steps -- always a good idea when coding
nba_grouped = nba.group('Position', np.mean)
nba_fewer_cols = nba_grouped.drop(1, 4)
nba_renamed = nba_fewer_cols.relabel(make_array("Height mean", "Weight mean"),
make_array("Mean height", "Mean weight"))
nba_renamed.barh(0) # make horizontal bar graph
Mon 29 Jan:
Bring your laptop charger to class, as well as a mouse, if these will help you when using python. Also, I will not automatically give you an extension on an assignment, quiz, etc. all because your laptop died in the middle of the class.
Make sure to bring the reference sheet to class every day.
Wed 31 Jan:
LAB00, HW00, and Quiz00 have been graded today. Feedback will be emailed out to you after this Friday afternoon 2 Feb 2024.
Fri 2 Feb:
There are BUGS in the python autograder (if you got a problem correct, it is marked as wrong while an incorrect answer is marked as partially correct)! So I will re-grade LAB00, HW00, and Quiz00 over the weekend.
Sat 3 Feb:
I started re-grading your Quiz00 yesterday. It should be done today and feedback will be emailed to you by tonight.
Your Quiz00 has been re-graded today! You should have received an automated email with feedback.
Sun 4 Feb:
I will re-grade LAB00 and HW00 today, and get these feedback emailed out to you.
HW00 has been re-graded today. I gave everyone +1 bonus point and I did not deduct 25% off of your grade if you uploaded your assignment late.
LAB00 has been re-graded today. I gave everyone +1 bonus point and I did not deduct 25% off of your grade if you uploaded your assignment late.
Tues 6 Feb:
For Quiz 00, the correct solution to #6 create a scatterplot of Sodium vs Fat (g) should have been:
starbucks.scatter('Fat (g)', 'Sodium')
So you have to flip the order for a scatterplot.
Our class average is: 52.6776%.
Wed 7 Feb:
HW01 has been graded and feedback has been emailed to you.
Please DOUBLE CHECK to make sure you are not typing your alpha INCORRECTLY! Continuing to make this mistake will result in a 0 for that assignment.
Please do NOT delete the square brackets around your alpha, or else, you will get a zero on your assignment:
global alphas # leave this line alone and replace ...
# in the next line with your alpha number, e.g. alphas = [230000]
alphas = [123456]
Please do NOT put multiple alphas in your python file. Just put YOUR and ONLY YOUR alpha on your python file.
Please complete your assignments much in advance-- if you do not upload and submit your file 1 day before the due date, I will not continue to override and give you +1 bonus point.
If you upload a WRONG file to Blackboard, that is an automatic 0. So please double-check what you upload to Blackboard.
Keep in mind that I will accept late work (HWs and LABs), up to 7 days after the due date. After this 7 day period has ended, I will NOT accept any late work (see my website on Late Work as well as Course Policy Statement).
Thurs 8 Feb:
QUIZ01 has been graded and feedback has been emailed to you.
Our class average is: 72.8895%.
You MUST upload your quizzes and tests to Blackboard before you leave the classroom. If you upload your file AFTER you leave the classroom without my authorization, this is an AUTOMATIC 0.
Tues 13 Feb:
HW02 has been graded and feedback has been emailed to you.
Thurs 15 Feb:
LAB01 has been graded and feedback has been emailed to you.
Sat 17 Feb:
TEST00 has been graded and feedback has been emailed to you.
Our class average is: 81.7451%.
This is also the very first time that EVERY SINGLE ONE OF YOU uploaded your file correctly, on time, and you didn't omit your alpha, type wrong alpha, remove square brackets from your alpha, etc. Great job!
Have a wonderful 3-day weekend!
Tues 20 Feb:
Your 6-week grades and MAPRs have been submitted to MIDS.
Thurs 22 Feb:
HW03 has been graded and feedback has been emailed to you.
Sat 24 Feb:
HINTS for HW04:
I didn't read the instructions correctly and thought that "A" in a stack of cards is a face card. I have fixed these solutions.
The first 4 digits after the decimal sum to:
Question 1. 13
Question 2. 18
Question 3. 20
Question 4. 7
Question 5. 12
Question 6. 11 15
Question 7. 24 25
Question 8. 13
Question 9. 21
Question 10. 8
Question 11. 19
Question 12. True or False?
P(cloudy) = 18/31, P(cloudy and Friday) = 3/31, and P(Friday) = 4/31 or 5/31, depending on the year (some years have 4 Fridays in January while other years have 5 Fridays in January).
Tues 27 Feb:
Complete Probability Practice (it's under Day 21/22) and upload no later than Wed 6 March by 2359 for +10 bonus points on your lowest LAB grade.
Wed 28 Feb:
HINTS for Probability Practice (it's under Day 21/22). Complete this python file and upload this to Blackboard no later than Wed 6 March by 2359 for a bonus of +10 points. If your solution is different from mine, then contact me. It is possible that I may have a typo.
The first 4 digits after the decimal sum to:
Question 1. 16
Question 2. 12
Question 3. 21
Question 4. 10 (assume that you are choosing two cards with replacement)
Question 5. 0
Question 6. 26
Question 7. 15
Question 8. 13
Question 9. 10
Question 10. 25
Question 11. 4
Question 12. 19
Question 13. 17
Question 14. 21
Question 15. 13
Question 16. 27
Thurs 7 Mar:
QUIZ02 has been graded and feedback has been emailed to you.
Our class average is: 72.7531%.
Thurs 7 Mar:
Probability Practice-BONUS has been graded.
Sat 9 Mar:
HW04 has been graded and feedback has been emailed to you.
Sat 23 Mar:
HW05 has been graded and feedback has been emailed to you.
Thurs 28 Mar:
Practice Quiz 03 has been graded. I'll aim to grade Quiz 03 by tonight.
Quiz 03 has been graded and feedback has been emailed to you. Our class average is 64.6886%.
Sat 30 Mar:
HW06 has been graded and feedback has been emailed to you.
Sat 6 Apr:
LAB02 has been graded and feedback has been emailed to you.
TEST01 has been graded and feedback has been emailed to you.
Our class average is: 76.3932%. This includes a bonus of +2.17 points.
Your 12-week grades have been submitted to MIDS!
Tues 9 April:
All MAPRs have been submitted today.
Fri 12 April:
The autograder to grade your python files has quite a lot of errors, including it has been deleting all of my feedback to you! I did not know about this until last week during an EI. Therefore, feedback has been emailed to you for:
Test00 material: Quiz 00, Quiz 01, Lab 00, Lab 01, HW00, HW01, HW02, HW03, Test00.
Test01 material: Quiz 02, Quiz 03, Lab 02, HW04, HW05, HW06, Test01.
I still can't grade HW07 since the autograder is still broken. Once the Course Coordinator encodes the autograder into the solutions, then I will grade HW07.
Sat 13 April:
HW07 has been graded and feedback has been emailed to you.
Sat 27 April:
HW08 has been graded and feedback has been emailed to you.
Mon 29 April:
QUIZ04 has been graded and feedback has been emailed to you.
Tues 30 April:
HW09 has been graded and feedback has been emailed to you.
Tues 30 April - Fri 3 May:
Your Lab03 - PROJECT has been graded. Your LAB03 average is 89.7561%.
Sat 4 May:
Your Lab03 - PROJECT feedback has been emailed to you.
Tues 7 May:
Your Final Exam has been graded. Our class average is 78.2506%.
Your final grades have been submitted to MIDS.
I will work on MAPRs in the next several days.