HLA 311
Summer 2023
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Communication
Annoucements:
  • Final Grades have been posted...
  • In the end, we had very few missing assignments -- I appreciate your effort in completing the work for this course...
  • Enjoy the rest of your summer!

  • Other end of course details:
    • If you have not done so, please fill out a Course Evaluation Form: Link


Course Materials
Module Description Notes Videos
for Notes		 Guided
HW		 HW
4 4.1 Predictive Analytics Part I - Simple Linear Regression
- Notes 		Video  

Due: 07/07
HW Link


Data: Link
3 3.2 Data Viz- Gestalt Principles
- Notes 		Video No homework for Module 3.2
3.1 Data Viz- EPTs
- Notes 		Video No homework for Module 3.1
2 2.4: Data Verb: MUTATE
- Notes

Data: Link

 Video

Due: 07/01
HW Link


Video: Link
Data: Link 		 
2.3C: Data Verb: GROUP BY
- Notes

Data: Link

 Video  

Due: 06/21
HW Link


Data: Link
Video: Link
2.3B: Data Verb: SUMMARY - Quantities
- Notes

Data: Link

 Video

Due: 06/19
HW Link

  
2.3A: Data Verb: SUMMARY - Top 10 List
- Notes

Data: Link

 Video

Due: 06/14
HW Link


Data: Link
Video: Link
2.2: Data Verb: FILTER
- Notes

Data: Link

The =QUERY() function in Google Sheets is needed for Guided HW 2.2 		Video

Due: 06/09
HW Link



Data: Link
2.1: Data Verb: SELECT
- Notes

Data: Link

 Video No homework for Module 2.1
1 1.3: Importing Data
- Notes

Data: Link

Video

Due: 05/29
HW Link
1.2: Data File Structure
- Notes
Ignore video 21:20-21:50; Embedded Question #4 has been removed 		Video

Due: 05/26
HW Link
1.1: The Structure of Data
- Notes
1. Login before clicking
2. No specific due date 		Video

Due: 05/17
HW Link


Video Archive
# Link Description
53 Video Module 4 - Part 1: Predictive Analytics Part I - Simple Linear Regression
52 Video Regression Analysis via stats.blue online app [Novice]
51 Video Regression Analysis in Excel [Novice]
50 Video Regression Analysis in Google Sheets [Novice]
49 Video Scatterplots in Excel [Novice]
48 Video Scatterplots in Google Sheets [Novice]
47 Video Module 3 - Part 2: Data Viz - Gestalt
46 Video Module 3 - Part 1: Data Viz - EPTs
45 Video Module 2.4: MUTATE Data Verb
44 Video MUTATE - Split using Formulas [Spreadsheets]
43 Video MUTATE - Recode using Formulas [Spreadsheets]
42 Video MUTATE - Recode using a FILTER [Spreadsheets]
41 Video Module 2.3C HW - Example Video (for last problem)
40 Video Module 2.3C: SUMMARY Data Verb - GROUP BY / PIVOT
39 Video GROUP BY with Python [Advanced]
38 Video GROUP BY with =QUERY()[Intermediate]
37 Video GROUP BY with a Pivot Table [Novice]
36 Video GROUP BY with Formulas - Google Sheets [Novice]
35 Video Module 2.3B: SUMMARY Data Verb - Quantities
34 Video SUMMARY via Python [Advanced]
33 Video SUMMARY via =QUERY() - Google Sheets [Intermediate]
32 Video SUMMARY via Pivot Table - Excel [Intermediate]
31 Video SUMMARY via Pivot Table - Google Sheet [Intermediate]
30 Video SUMMARY with Formulas - Excel [Novice]
29 Video SUMMARY with Formulas - Google Sheets [Novice]
28 Video Module 2.3A: SUMMARY Data Verb - Top 10 List
27 Video Top 10 List: Python [Advanced]
26 Video Top 10 List: Power QUERY [Intermediate]
25 Video Top 10 List: =QUERY() Function [Intermediate]
24 Video Top 10 List: Spreadsheets [Novice]
23 Video Module 2.2: FILTER Data Verb
22 Video FILTER Action - Python [Advanced]
21 Video FILTER Action - Power Query [Intermediate]
20 Video FILTER Action - QUERY() Function [Intermediate]
19 Video FILTER Action - Excel [Novice]
18 Video FILTER Action - Google Sheets [Novice]
17 Video Module 2.1: SELECT Data Verb
16 Video Udpate Video #1
15 Video SELECT Action - Python [Advanced]
14 Video SELECT Action - Power Query [Intermediate]
13 Video MN Public Health Data Access Portal - Get Data
12 Video Module 1.3: Importing Data
11 Video Reading Data into Python [Advanced]
10 Video Power Query - Loading Data [Intermediate]
9 Video Power Query - Loading Data [Novice]
8 Video Tableau Prep - Loading Data
7 CSV File
TSV File		 Importing Data Files into Google Sheets; Comma Separated Values (CSV) and Tab Separated Values (TSV)
6 CSV File
TSV File		 Importing Data Files into Excel; Comma Separated Values (CSV) and Tab Separated Values (TSV)
5 Video Module 1.2: Data File Structure
4 Video Module 1.1: The Structure of Data
3 Video Submitting Your HW: Shows users how to submit their homework assignments
2 Video Submitting Your Notes: Shows users how to submit their online notes
1 Video Account Setup: Shows users how to setup an account at StatsClass.org.
0 Video Welcome / Syllabus


Item Description
Student Accounts In addition to D2L, an account is necessary at Statsclass.org for the submission of your course notes and the homework assignments for this course. The following video describes how a student account can be setup. [Video: Setting up an Account]
Course Notes A set of interactive course notes are being developed for this course. The course notes are divided into modules and parts. These interactive notes may require that you complete various tasks and/or answer questions that are embedded within the notes. After you submit your notes, the answers to questions and/or tasks are provided so that you can check your understanding of the content being covered. [Video: Submitting your Notes ]
HW Submission Most modules will include either a guided homework assignment or regular homework assignment. The homework assignments will be distributed via Google Docs and completed assignments must be saved to your Google Drive. After completing the assigment, you must login to your student account at StatsClass and submit a sharable link of your assignment. A brief video describing this process is provided here. [Video: Submitting your HW ]
Grades Your grade is determined by the completion and performance of the required work for this course.

You must complete the course notes for each module / part. The course notes will be scored as follows:
  • 0 pts: Did not complete tasks and/or answer questions posed
  • 3 pts: Attempted to successfully complete tasks and/or answer questions posed
After completing the set of course notes for a module / part, you will be required to complete either 1) Guided Homework Assignment and/or 2) Regular Homework Assignment.

- For the Guided Homework Assignment, scoring will be done as follows
  • 0 pts: Did not make a reasonable attempt to complete the assigment by the due date
  • 4 pts: Made a resonable attempt to sufficiently complete the assignment by the due date
  • 7 pts: Sufficiently completed the assignment by the due date
Solutions to guided homework assignments will be provided upon submission. You should carefully review these solutions before moving onto the next content to be covered.

- The Regular Homework Assignments will take more time to complete and are more typical in nature. These assignments will be worth about 20 points.

Note: You cannot submit homework assignments after solutions have been posted; thus, it is important that you submit your work before the specified deadline for the assignment.

Final grades will be determined using the following scale
  • F: Less than 60%
  • D: 60% - 70%
  • C: 70% - 80%
  • B: 80% - 90%
  • A: 90% and above
Learning Outcomes Students who successfully complete HLA 311 will be able to:
  1. The student will demonstrate the application of the data science cycle.
  2. The student will demonstrate knowledge of common project development issues.
  3. The student will perform, analyze, and critique various methods for the curation and management of data.
  4. The student will calculate, organize, and evaluate data summaries and visualizations.
  5. The student will be able to discuss and interpret outcomes from various predictive models and/or machine learning algorithms.
  6. The student will assemble and evaluate data products for the communication of outcomes.
Syllabus The WSU HLA 311 course syllabus is available here. HLA 311 Syllabus