Course Syllabus
| Data Analytics |
| Semester & Location: |
Fall 2025 - DIS Stockholm |
| Type & Credits: |
Core Course - 3 credits |
| Study Tours: |
Sweden, Amsterdam |
| Major Disciplines: |
Data Science, Computer science, Mathematics |
| Prerequisite(s): |
One year of computer science, a course in algorithms and data structures. A course in statistics and a course in linear algebra are recommended. Basic knowledge of a programming language is expected. We will be using Python in this course. If you have no experience coding in Python, you should either follow a Python tutorial before the course starts, or prepare to invest some hours getting up to speed with the basics of the language once we start. |
| Faculty Members: |
TBA (current students please use the Canvas Inbox) |
| Academic Support: |
csc-engr@disstockholm.se |
| Program Contact: |
Natalia Landázuri, Ph.D. |
| Time & Place: |
TBA |
Course Description
In our increasingly digitized society, with sensors embedded in our bodies, equipment, and surroundings, we are generating, collecting, and storing data at unprecedented rates. Within this vast sea of data lie insights crucial for understanding, predicting, and impacting every aspect of our existence, including human behavior, financial trends, sustainable development, and health and illness. Extracting these insights requires careful execution at each step in the data analytics pipeline.
Learning Objectives
By the end of this course, the student will be able to:
- Search and acquire relevant datasets for their research/project/work
- Apply the data analyst's pipeline on their dataset in order to get a clean, useful dataset in which they can explore existing trends, patterns and relationships
- Perform predictive analytics through the use of computational tools and modern machine learning algorithms and libraries
- Present and communicate their data analysis, exploration and insights through the completion of a relevant project on real-world data, for their portfolio
Faculty
TBA
Readings
Nelli. F. (2023). Python Data Analytics: With NumPy, Pandas and Matplotlib, 3rd Edition, Apress Standard, ISBN: 978-1-4842-9531-1
Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition. O'Reilly Media, Inc. ISBN: 9781098125974.
Additional sources include scientific papers, blog posts and videos available online.
Field studies
Possible visits can include:
- Visit to universities and research institutes to learn about the use of Data Analytics in research and the development of modern ML and other computational tools that enhance Data Analysis
- Visit to companies engaging, advancing and promoting Data Analytics
Approach to Teaching
The course is designed around the principle of constructive alignment. The major components in the course—the exams and the final project —implement this principle by stating clear goals and activities for every session and in the context of the course as a whole.
You are expected to engage actively in classroom discussions, presentations, exercises, and group work. In addition, you will participate in local field studies and extended course-integrated study tours in Sweden and the Netherlands. These visits give you the opportunity to learn first-hand from leaders in the field of machine learning, to speak with professionals about their cutting-edge work, and to better understand specific approaches to research, development, implementation and utilization of data analytics.
Leading up to each session, you are given a "preparation goal" and a suggested list of materials you can use to reach it. Sessions start with a short lecture that introduces the topic of the day. You work through a set of technical exercises. This gives you a clear outcome goal for each session: "show up prepared and complete the exercises". It gives you incentive to be prepared and focus on the work.
Core Course Week and Study Tours
Core course week and study tours are integral parts of the core course. The classroom is “on the road” and theory presented in the classroom is applied in the field. You will travel with classmates and DIS faculty/staff on two study tours: a short study tour during the core course week and a long study tour to relevant European destinations. You are expected to
- participate in all activities
- engage in discussions, ask questions, and contribute to achieving the learning objectives
- be respectful to the destination/location, the speakers, DIS staff, and fellow classmates
- represent self, home university and DIS in a positive light
While on a program study tour, DIS will provide hostel/hotel accommodation, transportation to/from the destination(s), approx. 2 meals per day and entrances, guides, and visits relevant to your area of study or the destination. You will receive a more detailed itinerary prior to departure.
Travel policies: You are required to travel with your group to the destination. If you have to deviate from the group travel plans, you need approval from the program director and the study tours office.
Expectations of the Students
You are expected to reach the preparation goal leading up to each session. If you have little or no experience coding in Python, you should either follow a Python tutorial before the course starts, or prepare to invest some hours getting up to speed with the language once we start. You should have a working laptop computer. It is advised that each machine has a least 4 GB of RAM and a reasonable processor (if it’s bought after 2014 you should be fine).
Evaluation and Grading
When assigning the final grades, your efforts will weigh as follows*:
- Active participation: 10% (includes participation in class, exercises and project, as well as behavior beneficial to the learning of your peers)
- Programming Assignments: 50% (two assignments, each counting for 25%)
- Final project: 40% (10 % project proposal, 30 % project report and presentation)
Active participation: It is mandatory to actively participate and engage in all scheduled sessions and activities of the course. You are expected to actively participate, engage in the topic and with the teacher, the guest lecturer, the hosts of the study visit etc. You are expected to show a behavior which is inducing to your peers' learning. In group exercises, you are expected to actively participate, engage, help your peers in their learning and contribute equally to the solution. Inappropriate, disrespectful and/or unprofessional behavior (e.g., sleeping during presentations, being rude towards our hosts during field studies) results in a score of 0 for participation for the entire semester.
Assignments: You are required to hand in two programming assignments (50% of the final grade, 25% each), which will cover the topics covered in class and exercises up until then.
Final project: From the beginning of the course, you are aware that an important outcome of the course is a project that could add value to your professional portfolio. To accomplish the project, you will work in groups. Each group selects a topic of their interest, and investigate it using data that needs to scraped or downloaded from the Internet. Student groups submit the project in two parts. First, each group must prepare a project proposal which demonstrates that they have a sound plan for their project and have clear hypotheses related to expected outcomes. The proposal presentation/video is a fun exercise that serves as a platform for sharing ideas between groups. We will view them all in class. In addition, it helps you start with a comprehensive idea of an outcome in mind. Second, once the project is finalized, student groups are required to communicate the results in the format of a blog post or a report and a presentation of their work and outcomes.
During the project, you are allowed to consult freely with any of the other students and the instructor. Contributions from other students, however, must be acknowledged with citations in your final report, as required by academic standards. Contributions to your presentations must similarly be acknowledged. Needless to say, the right to consult does not include the right to copy — programs, papers, and presentations must be your own original work.
You are expected to respect all submission deadlines. If an submission is turned in after the due date, the grade of the assignment will be reduced by 10 points over 100 for each day the submission is late.
*The percentages of the various grading parts, as well as the number of programming assignments are subject to change (check the final syllabus for the term).
Academic Regulations
Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on:
DIS - Study Abroad in Scandinavia - www.DISabroad.org