Data is everywhere. Almost any electronic transaction generates data. Even simple things like going for walk, looking at your phone or buying coffee create data. This class teaches you how to use data to solve problems and answer questions. That is a really broad statement, we are not going to teach you how to use the internet – our focus is using data to make inferences about the world. An inference is “a conclusion reached on the basis of evidence and reasoning.” Statistics is simply a formal way of reasoning with data. We’re going to teach you to use data to make decisions and to evaluate the decisions made by other people. This class does not require a lot of mathematics. Instead of teaching statistics via mathematics we focus on teaching via intuition and computer simulation. While you will not have to do a lot of math in this class you be learning to think statistically and to program ‘R’ a statistical programming language.
Statistics is the art and science of finding patterns in data. This class teaches you about patterns in data: How to conceptualize patterns and how to use computers to identify them. This is a first course in statistics. Lectures aim to provide an intuitive understanding of statistical concepts. The goal is to teach you how to think about statistical problems.
The course is taught from a computational perspective. In this class you will develop a fundamental understanding of abstract statistical concepts such as uncertainty and variability. These abstract concepts will be useful because they’ll help us use computers to develop solutions to real world problems. Computers are machines and they are very good at repetitive tasks like calculating a formula but they’re not very good at thinking. In this class we will pair your critical statistical thinking skills and your computer’s ability to crunch lots of numbers. We will learn how to apply quantitative data to real world problems and how to accurately state what the data tells us about the problem at hand.
You will learn computer programming while you are learning statistics. This might seem like a bummer, you have to tackle two complex subjects at once. This approach has advantages, I will not ask you to be a computer – you won’t have plug numbers into formulas or do anything terrible like arithmetic. You will however have to master the concepts behind the computation and you’ll have to learn how to “speak” a simple programming language. My aim as the instructor is to teach you to think computationally; to give you the ability to conceptualize statistical problems and exploit computers to solve them.
I’m an Associate Professor of Geography at the University of Colorado and Data Scientist at Apple Maps. My professional experience has ranged from the hyper digital world of Silicon Valley to the insanely analog practice of being the sole proprietor of an antiquarian bookshop in Manhattan.
As an undergrad at CU, I floated between the applied math, geography, and economics departments, learning techniques for data analysis in each. Geography is the one I’ve decided to make home, and I’m excited to be back as a grad student. My less-relevant passions include all things coffee & food, climbing & backpacking, and reading while in a hammock on Flagstaff.
|Molly Graber||Tuesdays, 1:45-3:45||GUGG 101|
|Seth Spielman||By Appointment||Seth's Office|
Participation is earned through participation in class, lab, or online discussions.
Labs will be graded for completeness and accuracy. There are 8 lab assignments. Each lab counts for ~5% of your total course grade. The labs are cumulative, each lab requires skills from the prior lab, missing a lab can have major consequences for your ability to succeed in the class. Late labs will have 10% point reduction per week.
Exams exam consists of completing a statistical analysis using the R programming language. The midterm from 2012 (the last time I taught this class) will be posted on prior to the exam.
Final Project is a major component of your overall grade. The final project is a group project that consists of 10 phases. A full description of the phases and deadlines is posted on the class website under “Final Project”.
Attendance in all classes and labs is mandatory. However, we will not take attendance. If you miss a class it is your responsibility to get the lecture/lab notes from a classmate.
Expectations and Etiquette:
Respect everyone. In my view, talking or messing with your phone class in class shows a lack of respect for me but more importantly the people sitting near you who are paying tuition to learn (about things other than your personal life).
Molly and I will do our best to make sure that the class operates smoothly but computers sometimes misbehave, please be patient.
Respect works both ways; I understand that this is a difficult class and that sometimes life is even less cooperative than computers. If you have a personal emergency, I will do my best to help you through the class, however under no circumstances can you excel in this class without doing ALL of the work it requires.
In accord with university policy if you qualify for accommodations because of a disability, please submit to me a letter from Disability Services in a timely manner so that your needs can be addressed. Disability Services determines accommodations based on documented disabilities. Contact: 303-492-8671, Center for Community N200, and http://www.colorado.edu/disabilityservices. If you have a temporary medical condition or injury, see guidelines at: http://www.colorado.edu/disabilityservices.
Campus policy regarding religious observances requires that faculty make every effort to deal reasonably and fairly with all students who, because of religious obligations, have conflicts with scheduled exams, assignments or required attendance.
Students and faculty each have responsibility for maintaining an appropriate learning environment. Those who fail to adhere to such behavioral standards may be subject to discipline. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with differences of race, color, culture, religion, creed, politics, veteran’s status, sexual orientation, gender, gender identity and gender expression, age, disability, and nationalities. Class rosters are provided to the instructor with the student’s legal name. I will gladly honor your request to address you by an alternate name or gender pronoun. Please advise me of this preference early in the semester so that I may make appropriate changes to my records. See policies at http://www.colorado.edu/policies/classbehavior.html and at http://www.colorado.edu/studentaffairs/judicialaffairs/code.html#student_code
All students of the University of Colorado at Boulder are responsible for knowing and adhering to the academic integrity policy of this institution. Violations of this policy may include: cheating, plagiarism, aid of academic dishonesty, fabrication, lying, bribery, and threatening behavior. All incidents of academic misconduct shall be reported to the Honor Code Council (email@example.com; 303-735-2273). Students who are found to be in violation of the academic integrity policy will be subject to both academic sanctions from the faculty member and non-academic sanctions (including but not limited to university probation, suspension, or expulsion). Other information on the Honor Code can be found at http://www.colorado.edu/policies/honor.html and at http://www.colorado.edu/academics/honorcode/