Learn Data Structures and Algorithms with Python

Bottom Line

A genuinely solid, hands-on DSA course for Python learners who are tired of theory-only tutorials—but you'll need to budget more than 26 hours if you actually want the concepts to stick.

📊 Course Snapshot

🖊️ Editorial Analysis

Let me be upfront: I'm compressing a lot of nuance into a few lines here, because DSA courses live or die on one thing—whether students actually implement the structures themselves, or just watch someone else do it. Codecademy's version leans hard into the former. That's the right call.

The course covers nodes and linked lists, doubly linked lists, queues, stacks, and hash maps—and it does so in Python, which is increasingly the default language for technical interviews at mid-to-large companies. The inclusion of practice projects like Towers of Hanoi and Blossom isn't just decoration. Those projects force you to think algorithmically under mild pressure, which is exactly the kind of friction that builds real retention. (A course without friction is basically a long YouTube video with a certificate at the end.)

At 26 hours listed and a 4.4/5 across 161 reviews, this sits in a comfortable middle zone—not a quick weekend sprint, not an overwhelming semester-long commitment. The review count is modest, which means you should weight individual reviews carefully. But a 4.4 without a huge sample size is actually harder to fake than a 4.7 with 50,000 drive-by ratings.

The AI-assisted guided coding help is a genuinely useful addition—not because it does the work for you, but because it lowers the "I'm stuck at 11pm and have no one to ask" barrier that kills completion rates. I've seen that barrier take down otherwise motivated students more times than I'd like to admit.

One honest note: this requires Codecademy's subscription, which means you're not buying a course—you're buying platform access. That's fine if you plan to use other Codecademy paths. It's less ideal if DSA is your only goal and you're budget-conscious. And the prerequisite is real: if you haven't finished something like "Learn Python 3," the early modules will feel like reading a menu in a language you're still sounding out.

This is the part that makes me weirdly happy: the course doesn't try to cover everything. No red-black trees, no advanced graph theory shoehorned in for prestige. It stays in its lane—core structures, clean implementations, practical projects. That restraint is a design choice, and it's the right one for the target audience.

💼 Career & Salary Context

DSA fluency isn't optional if you want a Python developer role at most companies that do technical interviews. It's table stakes. According to Glassdoor data (as of early 2026), the median total pay for a Python developer in the US is $129,000—with hourly rates averaging around $59/hr and ranging from roughly $22 to well over $80/hr depending on experience and specialization.

Relevant roles where this course's content shows up directly in interviews: Software Engineer, Backend Developer, Data Engineer, Machine Learning Engineer, and Site Reliability Engineer. The linked list and hash map implementations in this course mirror the exact problem categories that appear in LeetCode-style screens at tech companies.

This course alone won't get you hired—but skipping DSA prep almost certainly will get you filtered out. Think of it as the minimum viable foundation, not the finish line.

⏱️ Real Time Investment

DSA is the category of content where the listed time and the real time diverge the most. The 26 hours covers watching, reading, and completing the guided exercises. But if you're actually debugging your own linked list implementation at midnight—which you will be—add another 15–20 hours of practice, re-doing problems, and cross-referencing documentation. At a realistic pace of 5–6 hours per week, expect 8–10 weeks to finish with real comprehension. That's not a criticism. That's just how this material works.

🎯 Skills You'll Build