INFO-I 399 CURRENT TOPICS IN INFORMATICS (1-3 CR.)
Variable topics. Emphasis is on new developments and research in informatics.
6 classes found
Fall 2025
| Component | Credits | Class | Status | Time | Day | Facility | Instructor |
|---|---|---|---|---|---|---|---|
| LEC | 3 | 8376 | Open | 12:45 p.m.–3:00 p.m. | MW | AC C102 | Pierz D |
Eight Week - First / In Person
LEC 8376: Total Seats: 25 / Available: 6 / Waitlisted: 0
Lecture (LEC)
- Above class meets first eight weeks only
- TOPIC:PROBLEM SOLVING
Topic: Problem solving
| Component | Credits | Class | Status | Time | Day | Facility | Instructor |
|---|---|---|---|---|---|---|---|
| LEC | 1–3 | 9390 | Open | 12:45 p.m.–3:00 p.m. | MW | AC C102 | Pierz D |
Eight Week - Second / In Person
LEC 9390: Total Seats: 25 / Available: 11 / Waitlisted: 0
Lecture (LEC)
- Above class meets second eight weeks only
- TOPIC: PROBLEM SOLVING
Topic: Problem solving
| Component | Credits | Class | Status | Time | Day | Facility | Instructor |
|---|---|---|---|---|---|---|---|
| LEC | 1–3 | 8666 | Open | ARR | ARR | WB WEB | Horton A; Gonin M |
Eight Week - Second / 100% Online All
LEC 8666: Total Seats: 60 / Available: 60 / Waitlisted: 0
Lecture (LEC)
- TOPIC: CEWIT U-GRAD RESEARCH METHODS
- Above class open to students in the CEWiT Research Experience
- You should select 3 credit hours. If you have concerns, please contact the instructor.
- Above class meets second eight weeks only
- This is a 100% online class taught by IU Bloomington. No on-campus class meetings are required. If IU e-Texts are not used for this class, textbooks and other materials are available at your home campus bookstore.
- Above class requires permission of Department
- Above section is the first semester of the CEWIT research course. Students take the first semester in the Fall and the second semester in the Spring. Please see instructor for details.
Topic: Cewit u-grad research methods
| Component | Credits | Class | Status | Time | Day | Facility | Instructor |
|---|---|---|---|---|---|---|---|
| LEC | 3 | 34698 | Open | 10:00 a.m.–1:00 p.m. | R | IF 4111 | Lukefahr A |
Regular Academic Session / In Person
LEC 34698: Total Seats: 32 / Available: 31 / Waitlisted: 0
Lecture (LEC)
- TOPIC: Digital Chip Design
- Above class meets with a section of ENGR-E 599
| Component | Credits | Class | Status | Time | Day | Facility | Instructor |
|---|---|---|---|---|---|---|---|
| LEC | 3 | **** | Open | 9:35 a.m.–10:50 a.m. | MW | IF 4063 | Gordon M |
Regular Academic Session / In Person
LEC: Total Seats: 30 / Available: 26 / Waitlisted: 0
Lecture (LEC)
- TOPIC: PYTHON FOR DATA ANALYSIS
Topic: Python for data analysis
This course teaches students how to use Python for data analysis through hands-on projects involving data manipulation, visualization, statistics, and machine learning. Students will work in Jupyter Notebooks and gain experience with AI-assisted coding tools such as Copilot or ChatGPT to enhance productivity. Key Python libraries: NumPy, Pandas, Matplotlib, and more, are used to explore and visualize data. The course introduces both supervised and unsupervised learning methods, including regression, classification, and clustering. Designed for students in informatics, computer science, business, or related fields, the course emphasizes real-world applications and culminates in a final project. Prerequisite: Basic programming knowledge or completion of an introductory Python course.
| Component | Credits | Class | Status | Time | Day | Facility | Instructor |
|---|---|---|---|---|---|---|---|
| LAB | 3 | 29686 | Open | 9:35 a.m.–10:50 a.m. | F | LH 004 | Gordon M |
Regular Academic Session / In Person
LAB 29686: Total Seats: 30 / Available: 26 / Waitlisted: 0
Laboratory (LAB)
- TOPIC: PYTHON FOR DATA ANALYSIS
This course teaches students how to use Python for data analysis through hands-on projects involving data manipulation, visualization, statistics, and machine learning. Students will work in Jupyter Notebooks and gain experience with AI-assisted coding tools such as Copilot or ChatGPT to enhance productivity. Key Python libraries: NumPy, Pandas, Matplotlib, and more, are used to explore and visualize data. The course introduces both supervised and unsupervised learning methods, including regression, classification, and clustering. Designed for students in informatics, computer science, business, or related fields, the course emphasizes real-world applications and culminates in a final project. Prerequisite: Basic programming knowledge or completion of an introductory Python course.