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UCSD EC79

Program Overview​

There are quite a few hardware courses. One of the core requirements is a mandatory hardware course — if you truly have no EE background, plan to spend extra time on it.

EC79 is already the most career-change-friendly track. UCSD ECE overall has a noticeably heavier workload and higher course difficulty than UCSD MSCS, requiring a greater time commitment.

Admission Threshold & Data Points​

The threshold is slightly lower than CS75. Overall, it is career-change friendly — CS/EE students with a GPA of 87+ can give it a shot.

Job Outcomes & Data Points​

Among people around me, roughly half landed offers.

Life​

The campus scenery is exceptionally beautiful and the climate is pleasant, which helps ease some of the stress. Apart from a bit more rain in winter, the weather is very comfortable year-round. Off-campus rent is quite expensive, but grad housing is relatively affordable — at least you have a place to live. The school also offers many recreational courses such as surfing, boxing, archery, and more, which can enrich your life outside of studying. UCSD students can ride city public transit for free, and on weekends the school runs a free grocery shuttle to several shopping areas, making daily life quite convenient.

Course Reviews​

  1. ECE 143 Programming for Data Analysis with Prof. Unpingco, Jose H. Recommendation: 5/5 Assignment: 4-6 programming problems assigned weekly, plus a randomly-grouped project requiring you to select a dataset, clean it, analyze it, and write a report. For example, one group chose an e-commerce user spending behavior dataset, cleaned the data by removing duplicates and errors, then conducted in-depth analysis of purchase frequency, spending distribution, etc., producing a thorough report. Workload: Light — 5-8 hours per week to complete assignments and project tasks. Grade: Generous grading. This course is required for EC93, focusing on Python from basic syntax to Python data processing. Very helpful for career changers, with low course pressure and no final exam. Highly recommended.

  2. ECE 269 Linear Algebra and Application with Prof. Pal, Piya Recommendation: 2/5 Assignment: Three homework assignments and three projects. Both require deep application of linear algebra — for instance, using matrix transformations for image rotation and scaling in image processing. Workload: Very heavy — 15-20 hours per week. Grade: Poor grading. Required for EC80, 82, and 93. Despite the name "Linear Algebra," the difficulty far exceeds the undergrad level. Two lectures and one discussion per week are all packed with content. Exams are strict — even though some problems come from discussions and homework, the average score is only around 30. By comparison, Prof. Xie, Pentao's ECE 269 is slightly easier, but unfortunately it was not offered in 2022-23.

  3. ECE 276A Sensing & Estimation Robotics with Prof. Atanasov, Nikolay A. Recommendation: 3/5 Assignment: Three homework assignments and three projects, centered on robot perception and estimation — for example, designing robot localization algorithms that estimate the robot's position in an environment based on sensor data. Workload: Heavy, especially in the second half of the semester. About 12-15 hours per week. Grade: Average. Required for EC80, elective for EC93. A foundational course in the robotics field. Essential if you plan to work or do research in robotics. However, given its difficulty, choose carefully if it is not required for you.

  4. CSE 202 Algorithm Design and Analysis with Prof. Impagliazzo, Russell Recommendation: 3/5 Assignment: Five homework assignments and one project. Focused on algorithm design and analysis practice — for example, designing efficient sorting algorithms and analyzing their time and space complexity. Workload: Heavy — about 10-12 hours per week. Grade: Generous grading. This course is less practically useful than the undergrad Data Structures (CSE 100) or Algorithms (CSE 110).

  5. ECE 250 Random Processes with Prof. Siegel, Paul H. Recommendation: 3/5 Workload: Moderate — about 8-10 hours per week. Grade: Poor grading. While not required for any specific track, it is an elective for most tracks, and many other courses reference related material. Two midterms and one final exam, with strict grading where small mistakes cost significant points. The content is practical, but choose carefully.

  6. ECE 285 Intro to Mathematical Finance with Prof. Sworder, David D. Recommendation: 4/5 Assignment: Group project. One group conducted a stock portfolio optimization project, using mathematical methods to analyze how to allocate different stocks to minimize risk and maximize returns. Workload: Light — about 5-7 hours per week.