Spring 2023 - CMPT 889 G100
Special Topics in Interdisciplinary Computing (3)
Class Number: 7704
Delivery Method: In Person
The objective of this course is to introduce and give students experience to the steps necessary to apply data science in a business context; what comes before and what comes after using an advanced technique/model. Students will learn and practice how to get familiar with a business context, how this will inform the design of the analysis/model that would give the right answer for the business problem, when to use data science vs not, and how to deliver the outcomes of the analysis/model in a way that is meaningful for the business.
Most companies looking for data scientists will not need or use the latest algorithm or the newest distributed storage and computing framework; they are looking for a broad profile (proficient with many data science tools and able to use them to answer a business problem) rather than a deep profile (expert able to solve highly technical problems).
By the end of the course, the students’ end-to-end data science skills:
Business acumen: how to measure performance and value created by models
Critical thinking, skepticism, perseverance
Communication (translating tech language into storytelling, presentation, visualisations)
will be improved and students will be a big step closer to being ready-to-go analytical data scientists.
As these skills are mostly learned by experience, students will get to practice a lot through many real-life business scenarios where they will go through the full life cycle of an analytical data science project: understanding the business context, connect the dots between the problem and the technical solution, design the analysis, build the technical solution, do qualitative QA, produce the insights, construct a story and deliver it to different audiences (Manager, Director, VP) with the appropriate level of detail.
Industry guests are invited to share their experiences and give students perspectives on what different people/roles expect from analytical data scientists (Startup CEO, Video Game Data Scientist, Customer Analytics Director, Financial Data Scientist, People Analytics VP....)
This course is designed for Big Data Master Program students who:
- Have completed "Programming for Big data 2"
- Have working knowledge of Data science techniques
- You will build models – but not optimize them
- Are proficient in Python or R
- You will need to use one of these
- Experience and basic understanding of the corporate world
- Be ready to speak/present in front of the class
There will be 4 group assignments (real-life scenarios) where students are graded on understanding of the business problem, solving of the problem and presenting the results to the right audience. There will also be 2 individual assignments about individual presentation and storytelling with data.
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
Graduate Studies Notes:
Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html