Course syllabus#
Course info#
Day |
Time |
Location |
|
|---|---|---|---|
Lecture |
Wednesday |
11:45 am - 13:15 pm |
|
Lab |
Wednesday |
13:20 pm - 14:05 pm |
Learning objectives#
In this course you will gain data skills applicable to marketing. You will learn how to collect and analyze data and communicate your findings.
By the end of the semester, you will be able to…
use methods like decision trees, deep learning, clustering and text mining to gain insights from data.
collect data with web scraping and web APIs.
apply marketing metrics to evaluate marketing efforts.
use Google Analytics to create reports and analyze results.
Where to get help#
If you have a question during lecture, feel free to ask it!
Outside of class, any general questions about course content or assignments should be posted on the Moodle course forum.
Emails should be reserved for questions not appropriate for the public forum. If you email me, please include the name of our course in the subject line.
Check out the Support page for more resources.
Lectures#
A lot of what you do in this course will involve writing code, and coding is a skill that is best learned by doing. Therefore, as much as possible, you will be working on a variety of tasks and activities throughout each lecture.
Additionally, some lectures will feature application exercises that will be graded.
You are expected to bring a laptop to each class so that you can take part in the in-class exercises.
Assessment#
Assessment for the course is comprised of two components:
Application exercises#
Parts of some lectures will be dedicated to working on “Application Exercises” (AE). These exercises will give you an opportunity to practice apply the concepts and code introduced in the readings and lectures.
Note
AEs should be completed and submitted individually.
Exam#
There will be one E-exam.
Through this exam you have the opportunity to demonstrate what you’ve learned in the course.
More details about the exam will be given during the semester.
Grading#
The final course grade will be calculated as follows:
Category |
Percentage |
|---|---|
Application exercises |
25% |
Exam |
75% |
The final grade will be determined based on the following thresholds:
Grade |
Final Course Grade |
|---|---|
1.0 |
96 - 100 |
1.3 |
91 - 95 |
1.7 |
85 - 90 |
2.0 |
80 - 84 |
2.3 |
75 - 79 |
2.7 |
70 - 74 |
3.0 |
65 - 69 |
3.3 |
60 - 64 |
3.7 |
55 - 59 |
4.0 |
50 - 54 |
4.7 |
15 - 49 |
5.0 |
0 - 14 |
Course policies#
Academic integrity#
TL;DR: Don’t cheat!
All students must adhere to the academic integrity standard. Students affirm their commitment to uphold the values by signing a pledge that states:
I will not lie, cheat, or steal in my academic endeavors;
I will conduct myself honorably in all my endeavors;
I will act if the standard is compromised
Regardless of the course delivery format, it is your responsibility to understand and follow HdM policies regarding academic integrity, including doing one’s own work, following proper citation of sources, and adhering to guidance around group work projects.
Late work policy#
There is no late work policy for the application exercises and the exam.