Mastering the Software Engineering Interview

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Below are the top discussions from Reddit that mention this online Coursera course from University of California San Diego.

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Taught by
Mia Minnes
Assistant Teaching Professor
and 2 more instructors

Offered by
University of California San Diego

Reddit Posts and Comments

0 posts • 10 mentions • top 7 shown below

r/cscareerquestions • post
92 points • IAmDumbQuestionAsker
Classes on technical interviews

The question about boot camps for doing well on technical interviews didn't go anywhere, so here's a thread for general classes about them.

UC San Diego has one on Coursera: Mastering the Software Engineering Interview

r/bioinformatics • comment
4 points • niemasd

> as well as a double dose of imposter syndrome

I feel like most (if not all) people in STEM encounter imposter syndrome at some point. It's totally normal, and you just need to keep trudging on! Dr. Christine Alvarado at UCSD made an excellent video on the subject for her online course series

> "there are too many bioinformatics PhD graduates are the number of positions"

I'm not sure about this. I'm in UCSD's Bioinformatics and Systems Biology Ph.D. program, and every person I personally know who graduated had a nice job lined up immediately upon exiting

> "you'll be overworked and the pay sucks, get used to it"

This has some truth to it, but I don't fully agree. I think, on average, Bioinformatics jobs don't pay as much with regard to how much knowledge you need to have in comparison to jobs like pure software development, but that's not to say that the pay "sucks"

> I've been applying for internships at Illumina, Monsanto, etc. and I still haven't received a single interview, whereas all of my friends applying to Microsoft, Google, Facebook, etc. all have 2-3 interviews lined up

Bioinformatics internships are typically rare for undergrads, especially an undergrad so early in the game. For success in a bioinformatics job, especially at these big-name companies, you need a solid background in programming, data structures, algorithms, molecular biology, genetics, chemistry, statistics, etc., and it's unlikely that you have expertise in all of these so early in your undergraduate career. I'd recommend focusing your first 2-3 years of undergrad on research, and once you have explicit concrete bioinformatics skills, you'll have a better shot at those internships

> I have 4 years of bioinformatics research, an active GitHub, and a publication on which I am a middle author in a reputable journal, but I'm concerned that I seem to be missing something

This is a pretty solid resume for someone so early in their academic career. Just keep it going!

> am I wasting my time? If I'm not, how do I land that first internship?

If you feel like you genuinely want to do work in bioinformatics, I don't think you're wasting your time. You're on the right track, and I think your primary issue right now is that you're still pretty early in your academic career, something a lot more important for bioinformatics than it is for a lot of software engineering internships. If you don't get an internship for this summer, try to focus purely on research, and maybe try to see if you can lead your own (small) project? A middle authorship is nice to have, but if you have 4 years of experience in bioinformatics research, I think you should have the ability to get a first authorship under your PI's guidance

r/cscareerquestions • comment
5 points • slythfox

I see LeetCode as a compliment to other technical interviewing resources and exercises out there. You may find that you have gaps in your knowledge or some skills to build upon. In this way it seems to be about setting common ground for people of different backgrounds.

You might survey some other resources to help you prepare. For example, Coursera has a 4-week Mastering the Software Engineering Interview course which sets expectations about the interview process. In contrast, Udacity has a Technical Interview course.

r/cscareerquestions • comment
3 points • DirdCS

You have a long time so I agree with /u/smaiyul about including Sedgewick's Coursera pretty much covers most of his book

16/10/2017 I started learning programming again (5 years without touching OOP languages; only rarely touching PHP/batch script during then) and learning DS&A. I wasted a lot of time reading & watching unnecessary stuff. My suggested route to you would be:

  1. Read Grokking Algorithms. It's a very nice intro to the topic but covers less than Sedgewick which covers less than CLRS. It's the closest I've found to a Head First for DS&A

  2. Watch Sedgewick's Coursera videos. His website also has very concise code examples. If you're a math nerd you might enjoy the MIT course; if not Sedewick's course & book are better than MIT/Dasgupta/CLRS and their obsession with strange squiggles (equations)

  3. Read the CTCI sections that describe each data type but ignore the questions for now

  4. Do Codewars until you're unable to solve problems often. The early problems are easier than Leetcode & I like the UI/the idea I'm a ninja

  5. Do some of the most solved LeetCode Easy & Codeforces problems (No higher than Div 2 C, more likely not higher than Div 2 D). Allocate n days to each site & do as much as you can. Make sure you check the most popular 2-3 solutions for each problem on LeetCode (whether you got a correct solution or not)...this will teach you random shit like XOR tricks and Gauss. Do at least 30-50 LeetCode Easies this way

  6. Filter LeetCode by category, make a schedule for doing a chunk of each category at least to'll start seeing patterns to questions here like frequency counts for String Qs and the general backtracking solution for permutation Qs. This is when you'll probably make the most improvement as when you see future questions you'll start thinking in that way (hashmap? can I use 2 pointer for O(1) space?). Go as low in frequency as Graph (inclusive), or lower if you want

  7. Watch Interview videos like Coursera, Google...partly as interview prep but also to take a break from coding. Can review your resume here

  8. Start going through CTCI/EPI/both. You don't need to code solutions, just think to yourself how you'll do it & compare against what they suggest. Their code is pretty hit & miss (some of the same questions are on leetcode with much cleaner solutions) but you should have similar solutions or be confident that you had the right idea based on #6 experience

  9. Target weak areas identified in #6 e.g. DP or trees, try to get more comfortable with them...maybe YouTube examples if need be; some are good at explaining it & have visual examples

  10. Review the APIs of the most common Java DSs on javadocs. Maybe try as some more practice.

I can't really vouch for how successful this routine is as I'm on #9-10 (pre-applications) but at this point I get optimal/acceptable for most leetcode mids & just did the first 3 questions of Codejam pretty comfortably

This solely covers phone interview prep. There's be a bunch of other resources to review for on-sites (system design books/websites/videos)

r/cscareerquestions • comment
0 points • kcvis

Hey I don’t know if you’ll see this at all because there’s so many comments but one of the resource I used to prep for coding interview was actually from UCSD. I felt out of the many things I tried this was the most newb friendly. When I graduated I came into the mess of coding interview with absolutely no experience, but they break it down to me so I at least know what to expect and how to behave in an interview situation.

r/rutgers • comment
1 points • rucsh

You should be familiar with data structure and algorithms for most internships. Here is a specialization which you can audit for free:

r/MLQuestions • comment
2 points • kermit7315

Do not fix it in your mind that you are going to ruin your interview. If you think so, you will.

Start practicing on whiteboards. Try for 20 mins, then look up the solution, then try again. Use CTCIthat.

This Coursera course might help you gain some confidence -


>how many times will I come across DS questions in a data science or ML interview

If you are aiming for big giants which use ML heavily - like FB, Google, Microsoft, DSA can form a vital part of the interview. You do not want to disappoint them when they ask how do you sort a list or do graph traversals.