Course:  DATA220G - Data Analysis with R
Semester:  Spring 2018
Instructor:  Bob Rudis
Contact Email:  rrudis@ccsnh.edu
Day/Time:  Online
Office hours:  By appointment (virtual)
Textbook:  R for Data Science / Advanced R (see below)

Catalog Description (adjusted)

This course is an applied data science & statistics course that introduces students to key, modern topics in including data acquisition, data exploration, statistical data analysis and communicating the results of data analyses. Major topics include advanced R programming language concepts, working as a standalone data analyst and within a team, organizing analysis projects, finding data on the internet, web scraping & APIs, and a survey of modeling, clustering, classification and time series analysis and forecasting tools & techniques. Prerequisites: C or better in DATA210G (or by department approval) and MATH235G (may be co-requisite).

Desired Student Competencies

Students will:

Grading/Assessment

College Email

I send all class announcements to you at your college email address through Blackboard/Canvas. You are responsible to check your college email regularly for any and all communication sent to you. Email addresses can be found at the Student Information (SIS) site. Any official correspondence (i.e. notification of absence, withdrawal, etc) not using college email is subject to quarantine and/or removal from the CCSNH email server and will not be answered. If there are technical questions (apart from accessing the RStudio Server setup for the class) that need to be answered, please access the helpdesk. The address is http://greatbay.edu/helpdesk. Personal email addresses cannot be used because SIS and Blackboard/Canvas are not set up to accommodate them. I check email regularly and will do my best to reply to your email within 24 hours. Again, I only reply to official email from your college address.

Class Time

Class will be instruction, homework/project review and discussion of new projects. The class will be delivered remotely and the instructor will be available for ad-hoc office hours as needed. The instructor will also work out a 1:1 weekly online “check-in” schedule with the students to ensure there is a dedicated time to answer questions.

Student Essentials

Attendance/Participation Policy

Videos will be made and posted online. You are responsible for viewing the content. If there are any issues with the content or accessing the content you are responsible for notifying the instructor when you encounter the issue.

Registrar Procedures Add/Drop Deadlines and Procedures:

Please see the official Planning Calendar (available in all the usual places you’re familiar with) for deadlines with the registrar: deadline to drop with full refund, deadline to drop with a “W”, and deadline to drop with “WP/WF”.

In order to drop this class you must fill out an add/drop form and return this form to the One Stop office for processing. Simply ceasing to attend class or contacting the instructor does not constitute officially dropping the course.

If you are receiving Financial Aid, you should speak with that office before deciding to withdraw.

You may withdraw from the course and receive the grade of W, which does not impact your GPA (see Planning Calendar on the last page for this deadline date). No signature from the instructor is required. If past this deadline, the next option is to drop with a “WP/WF”. This option does require an instructor signature and can affect GPA.

Classroom Decorum/Code of Conduct

In order to create a healthy and productive learning environment there are expectations of all of us. These include:

Weather Alerts

If school is cancelled for any reason, you should receive an email and/or a phone message from GBCC alerting you. Sign up for “weather alerts” online if you would like a text message. The text messaging service is called “GBCC Alerts” and you will find information to register here: GBCC Alerts

This should not apply to us.

Cancellations

If an emergency arises, I will notify Academic Affairs that any scheduled interactive sessions will be cancelled and I will send you an email to your college email address via BB/Canvas.

Student Support and CAPS

The College is committed to providing support for students with disabilities. Any student with physical, learning, attention, and/or psychological disabilities is encouraged to visit the Center for Academic Planning and Support (CAPS) and make an appointment with the Coordinator of Disability Support Services. More information is available at http://greatbay.edu/student-services/academic-support/disability-services

The Center for Academic Planning and Support (CAPS) has a variety of services to support your academic success at Great Bay Community College. These include:

Check with CAPS to see all that is available for support for this class.

Texts

Working in data science means keeping up with advancements in the field (and it is a broad field). Here are some key supplemental texts we will be using as part of the course:

All of my references to reading assignments will be from the online version of these books. You will be responsible for translating back to physical book references.

I’m going to be taking excerpts from Applied Predictive Modeling and presenting them during the class. You don’t need to buy the book. Having said that, this is a seminal reference that I keep on my desk at all times.

These are some good sites to keep as reference:

Other resources

Planning Calendar (will be finalized after receipt of questionnaires)

See the official calendar for the add/drop & official vacation info.

      January           
Su Mo Tu We Th Fr Sa    
14 15 16 17 18 19 20  Week 1: Classes start 01-16  
21 22 23 24 25 26 27  Week 2
28 29 30 31             
 
      February      
Su Mo Tu We Th Fr Sa 
             1  2  3 Week 3
 4  5  6  7  8  9 10 Week 4
11 12 13 14 15 16 17 Week 5
18 19 20 21 22 23 24 Week 6: NO CLASSES 02-19
25 26 27 28         
 
       March
Su Mo Tu We Th Fr Sa
             1  2  3 Week 7
 4  5  6  7  8  9 10 Week 8
11 12 13 14 15 16 17 Week 9: **Spring Break NO CLASSES
18 19 20 21 22 23 24 Week 10
25 26 27 28 29 30 31 Week 11
 
       April         
Su Mo Tu We Th Fr Sa 
 1  2  3  4  5  6  7 Week 12
 8  9 10 11 12 13 14 Week 13
15 16 17 18 19 20 21 Week 14
22 23 24 25 26 27 28 Week 15
29 30                
 
        May         
Su Mo Tu We Th Fr Sa
       1  2  3  4  5 Week 16: LAST DAY OF CLASS 05-04
 6  7  8  9 10 11 12 Week 17: Grades due 05-08

Syllabus

Week 1

Instruction

By the end of Week 1, you should:

Project 01

  • Send your GitHub account (or just GitHub account handle) link to mailto:rrudis@ccsnh.edu
  • Create a folder either on the RStudio Server or your own workstation (or both!) named `data220g’.
  • Create an RStudio project in that folder named week01.
  • After reviewing this week’s video (link TBD):
    • Copy the CSV file mentioned in the video to the week01 folder you created above. You can also find it at: https://greatbaycommunitycollege.github.io/data220g/week01/2017-2008-nx-wx.csv or in the course GitHub.
    • Create an R script named week01.R that:
      • reads in the CSV file
      • finds and reports the year (only the year) with the lowest, average temperature
      • finds and reports the year+month+day with the highest, average temperature (converted to Celsius)
      • computes and reports the mean and median averages temperatures per-month
      • using the rounding technique from the video, computes the nearest-10 value for each average monthly temperature then counts and reports how many average readings were in each 10-“bucket”
      • BONUS: Creates a simple plot showing the average monthly temperature over time.
    • The code must run without me having to tweak it at all. I will tweak it to get it to run if necessary, but it will not receive full credit.
    • We won’t use GitHub for submissions this week (unless you are comfortable with that already). Make a ZIP file of your week01 folder and submit it via Blackboard (I’m still working on Canvas access). If you are comfortable with GitHub, create a release and send me a link to it.
  • Submission is due by 23:59:59, Tuesday, January 23rd, 2018.

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Week 11

Week 12

Week 13

Week 14

Week 15

Week 16

Week 17