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SWThe best data structures and algorithms resources you need if you’re studying computer science #coding #learntocode #dsa #datastructuresandalgorithms #cs #computerscience #codingforbeginners
@swerikcodes
#Data Structures And Algorithms Course
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CSdata structures & algorithms #cscareeradvice #softwareengineerjobs #datastructuresandalgorithms
@cs.aar0n
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PIComment “LINK” to get links!
🚀 Want to learn Data Structures and Algorithms in a way that actually sticks? This mini roadmap helps you go from confused beginner to solving problems confidently with the right mental models.
🎓 DSA Visualizer
Perfect first step if you get lost in theory. You can visually understand how stacks, queues, trees, heaps, and sorting actually move step by step. Great for building intuition before you grind LeetCode.
📘 VisuAlgo DSA
Now level up your understanding with interactive animations and explanations for classic algorithms and data structures. This is amazing for topics like BFS, DFS, shortest paths, hashing, heaps, segment trees, and complexity intuition.
💻 USFCA CS Lectures
Time to learn the real foundations. These university style notes and visuals help you understand data structures, recursion, runtime analysis, and algorithm design patterns properly so you are not just memorizing solutions.
💡 With these DSA resources you will:
Understand core data structures with visual intuition
Learn common algorithm patterns for interviews
Improve problem solving for LeetCode and coding assessments
Build a strong base for system design and backend engineering
If you are serious about software engineering interviews, competitive programming, or becoming a stronger developer, mastering DSA is one of the highest ROI skills.
📌 Save this post so you do not lose the roadmap.
💬 Comment “LINK” and I will send you all the links.
👉 Follow for more content on DSA, coding interviews, and software engineering.
@pirknn
1.0M
GRHow to Learn Data Structures & Algorithms For FREE - AlgoMap #java #software #softwarejobs #softwareengineer #datastructures #leetcode #programming #javadeveloper #datastructuresandalgorithms #python #softwaredeveloper #code #FAANG #coding #javascript #javascriptdeveloper #codingisfun #codinginterview #js #html #css #sql
@greghogg5
105.9K
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🔥 Trying to learn Data Structures & Algorithms without a clear mental model is how people end up memorizing solutions instead of understanding them. If DSA feels confusing, overwhelming, or useless, the problem isn’t you—it’s how it’s usually taught. This mini roadmap fixes that.
🧱 Data Structures Explained for Beginners
A ground-up explanation of arrays, linked lists, stacks, queues, trees, and more—without assuming you already “get it.”
⚡ Data Structures & Algorithms in 15 Minutes
A fast, high-level map of the DSA landscape so you finally understand how everything fits together.
🎯 DSA Patterns for Coding Interviews
Learn the patterns behind LeetCode problems so you stop brute-forcing and start recognizing solutions.
💡 With these DSA resources you will:
🧠 Build intuition instead of memorizing answers
🚀 Solve problems faster by recognizing patterns
🏗 Write cleaner, more efficient code
💼 Prepare properly for interviews (not randomly)
📈 Level up your problem-solving skills as an engineer
If you want to move from “I’ve seen this problem before” to “I know exactly how to approach this”, DSA fundamentals are non-negotiable.
📌 Save this post so you don’t lose the roadmap
💬 Comment “DSA” and I’ll send you all the links
👉 Follow for Backend Engineering, System Design, and Career Growth
@emrcodes
189.0K
SAhow I ACTUALLY learned data structures & algorithms #softwareengineer #datastructures #algo #fyp
@sajjaad.khader
14.3K
ASThis is the EXACT order I would learn Data Science in 2026.
Hi 😊 my name is Dawn. I’ve been a Data Scientist at Meta, Patreon and other startups. And have coached 20+ clients into landing their dream Data jobs in the past year.
1️⃣ Learn SQL
SQL is a must-have skill for every data professional because it’s the primary way you get data OUT of a database.
It’s also a very easy coding language to learn, so I would start there.
Use Interview Master to learn and practice SQL (link in bio):
→ Learn SQL: www.interviewmaster.ai/content/sql
→ Practice SQL: www.interviewmaster.ai/home
2️⃣ Start building Product Sense & Business Sense
Product sense & business sense basically means you know how to use Data to solve real problems.
I would start building this “soft” skill early because (1) it takes time to really learn this, and (2) as you’re learning Stats and Python, you already have context on how these might be used in the real world.
I found the book: Cracking the PM Career to be super helpful before I landed my first Data Science job.
3️⃣ Learn Statistics
How much Stats do you need for Data Science? Just the foundations, but you need to know it really really well.
→ Descriptive statistics
→ Common distributions
→ Probability and Bayes’ Theorem
→ Basic Machine Learning models
→ Experimentation concepts
→ A/B experiment design
Check out Stanford’s Introduction to Statistics, which is free on Coursera.
4️⃣ Learn Python
Python is the #1 skill for Data Scientists in 2025, but I put it 4th on this list because I find that it builds on skills 1-3.
I learned Python on my own using DataCamp’s Python Data Fundamentals (link in bio).
5️⃣ Use AI-assisted coding tools
Many data scientists are already using tools, like Claude Code & Cursor, to 2x their productivity.
And also many companies are evaluating you on your use of AI during interviews.
#datascience #datascientist
@askdatadawn
1.3M
EMComment “DSA” and I’ll send it.
If you’re trying to truly understand algorithms and data structures, not just memorize them for interviews, you need to see how they work.
I’ve put together a set of powerful visualization tools that make complex concepts click.
This resource collection helps you:
• Visualize sorting, trees, graphs, and pathfinding step by step
• Understand time & space complexity intuitively
• Strengthen your DSA foundation for interviews and real-world engineering
• Learn by interacting, not just reading
Stop guessing how algorithms behave.
Start watching them run.
Comment “DSA” and I’ll send the full list.
@emrcodes
493.3K
COFREE Courses by Google:
1. Crash course on Python
2. Master Data Structures and Algorithms
3. Machine Learning
4. Cyber security Course
5. Google UX Design
6. Master Git and Github
7. Generative AI
8. Google Data Analytics
9. Google Project Management
10. Cloud Database Engineer
Course Links: https://www.codehype.in/courses.html
Follow @codehype_
#courses #google #2025
@codehype_
1.5M
VOComment "Link" to get the links!
You Will Never Struggle With Data Structures & Algorithms Again
🔗 Explore these free visualization tools:
1️⃣ visualgo.net
2️⃣ cs.usfca.edu
3️⃣ csvistool.com
Stop memorizing code blindly. See every algorithm in action — arrays, linked lists, stacks, queues, trees, graphs, sorting, searching, and more. These interactive platforms show step-by-step exactly how data flows and how operations work.
Whether you’re preparing for coding interviews, studying computer science, or just starting with DSA, this is the fastest way to master the fundamentals.
Save this, share it, and turn complex algorithms into simple visuals you’ll never forget.
@volkan.js
1.8M
MAI won’t be mad if you copy this entire roadmap…
#dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #wfh #workfromhome #wfhjobs #remotejobs #remotework #excel #sql #tableau #python
@marytheanalyst
243.9K
JEComment “project” for my full video that breaks each of these projects down in detail with examples from my own work.
If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude!
Here are the 3 types of projects you need:
↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables
↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one.
↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it
Save this video for later + send to a data friend!
@jessramosdata
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