Syllabus for Statistics 185
Professor: Mark McClure
Statistics vs data science

Check out Wikipedia's article on Data Science. Quoting from that article, I guess that
Data science is "The Sexiest Job of the 21st Century"
Wikipedia 
Also, the Bureau of Labor Statistics says that
Employment of computer and information research scientists is projected to grow 22 percent from 2020 to 2030, much faster than the average for all occupations.
Bureau of Labor Statistics
So... just what is this hot, new field?

According to the same Wikipedia article, data science is
a concept to unify statistics, data analysis and their related methods.

According to page 8 of our textbook,
Statistics is the study of how best to collect, analyze, and draw conclusions from data.

More bluntly,
data science is a sexedup term for statistics.
Nate Silver
And why is it so hot?
Simply put, statistics helps us understand so many things, from the fun, through the important, to the urgent.
 Sports analytics
 Politics
 Your health
And computer power makes it so easy to visualize and understand huge data sets today.
Course purpose
Statistics (or Data Science, if you prefer) is an applied field with a wide range of practial applications that is of growing importance in today's data driven world. In this course, you will:
 Learn the qualitative language of data: What are the basic structures, variables, summaries, and charts that we use to describe and understand data? What data can we find and how do we collect more?
 Learn how to analyze data quantitatively: What specific distribution should we use to model a specific data set? What do quantitative measures associated with a distribution tell us about the data set?
 Learn how to make inferences from the data: What conclusions can we draw from a given data set or collection of observations? And how confident can we be in those conclusions?
 Deal with large, real world data in all it's messiness: This is meant to be a practical class where you can develop some skills that help throughout your college career and beyond. As such, we'll learn how to use the computer to work with actual data.
Materials
 The Text:
We will use the Open Intro statistics text. This is an open text made freely available through a Creative Commons license.
 You may download our PDF for free.
 You are not required to purchase a hardcopy of the text but, if you want one, you can find links to several versions at the Open Intro website. There are also links to other ancillaries, like video summaries, computer tutorials, and data.
 Technology:
The number one thing you need for this class is a reasonably good computer and a reliable internet connection. Specifically, you'll need to access the following:
 Zoom The first week of class is already scheduled to be online. While I expect we will be back in person farily soon, we need to be prepared for the possibility of more Zoom.
 WebWork: We will have regular autograded homework using WebWork. You can login there, using the information described in class..
 Computational webpages: We'll have occassional, small computer labs set up within web pages. This will allow us to work with large, real world data sets.
 Discourse/span>: I've got an online forum called Discourse where we can discuss all aspects of statistics. Not only is this a great place ask HW questions but our computer labs will be turned in over the forum.
Grades
 Midterm exams: There will be three exams during
the term worth about 100 points apiece. Likely dates for the exams are:
 Wednesday, February 2
 Wednesday, March 2 and
 Wednesday, April 13.
 Final exam: There will be a comprehensive final exam on Friday, April 29 at 8:00 AM.
 Quizzes: There will be about five short quizzes on Fridays. Each quiz could be worth anywhere from 10 to 30 points.
 Homework: There will be several
types of homework:
 WebWork, which is online and automatically graded.
 Textbook assignments, which will not be collected but offer important practice.
 Computer labs, which will be posted and worked out on the forum.
 In class problems: We will frequently work problem sheets together in class. Quiz and exam problems will be closely related to these sheets. In addition, you will receive a 40 point class participation grade simply for participating regularly.
 Final grades: I will determine final grades using a scale not more stringent than the standard 90807060 scale. You will be apprised of your standing as the term progresses.
 Late work: In general, I don't accept late work.
 Cheating: I don't deal with cheating. If I suspect cheating strongly enough, I simply refer you to the provost and fail you for the class.
Advice
 Learning statistics: You don't have to be a mathematical genius to absorbs the fundamentals of statistics and learn how to apply statistics to real world problems. You do need to work fairly hard and consistently, however.
 Help:
You are not undertaking this challenging task alone.
Here are a few sources of assistance:
 Me: I like to talk to people about math and stat! That's why I chose this profession. You can find me on Zoom most days between 9:30 and 10:30, as well as on our forum.
 Your classmates: Most people learn best by talking things through with others. You will find that you can both learn from and help your fellow classmates. In particular, if your classmate is explaining a fine point to you, then you are helping them!
 Our class forum: A kinda combination of the previous two that never sleeps!
 The Math Lab: The Math Lab has always rocked! It's now available for both in person and online tutoring, as detailed on their webpage.
Your rights and responsibilities
It's worth understanding your rights and responsibilities as a student at UNCA. One of my responsibilities is to make sure you have the information that you need to do that. Since this is common to all classes, I've got that information on this legalese document.