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EdJAMON Community

AI & Tech Community

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  • Is learning data structures and algorithms still necessary or can you rely on built-in libraries?
    K kalyanaraman k

    Analyze the following discussion about "Is DSA still necessary when modern programming languages provide built-in libraries?"

    Compare all participants' viewpoints. Identify:

    1. Common arguments supporting DSA
    2. Arguments supporting built-in libraries
    3. Differences in opinions
    4. Real-world software engineering perspective
    5. Examples where DSA knowledge is required despite libraries
    6. Examples where libraries are the better choice
    7. Final balanced conclusion

    Create a professional developer-level answer explaining why DSA and libraries are complementary, not replacements for each other.
    Focus on scalability, performance optimization, debugging, system design, and engineering decision-making.


  • Is learning data structures and algorithms still necessary or can you rely on built-in libraries?
    A Ashwin G

    Learning Data Structures and Algorithms (DSA) is still essential, even with powerful built-in libraries. Libraries simplify development and save time, but DSA provides the knowledge to choose the right data structure, analyze time and space complexity, and optimize code for performance. It also strengthens problem-solving skills and prepares developers for technical interviews. The most effective approach is to understand DSA fundamentals first and then leverage built-in libraries to build efficient, scalable, and maintainable applications. Libraries are tools, but DSA is the foundation that enables developers to use those tools effectively.


  • Is learning data structures and algorithms still necessary or can you rely on built-in libraries?
    Y yogalakshmi

    Learning Data Structures and Algorithms (DSA) is still necessary, even though modern programming languages provide powerful built-in libraries. While libraries make development faster and reduce the need to implement data structures from scratch, understanding DSA helps you choose the right data structure, write efficient code, analyze performance, and solve complex problems that libraries alone cannot address. It is especially important for technical interviews and software engineering roles. The best approach is to learn the fundamentals of DSA while using built-in libraries in real-world projects, combining theoretical knowledge with practical development skills.


  • Is learning data structures and algorithms still necessary or can you rely on built-in libraries?
    S shafina nisma

    "DSA is still essential. Libraries help, but algorithms teach you how to solve problems efficiently."
    "Built-in libraries save time, but understanding DSA helps you know when and how to use them."
    "Libraries are tools; DSA is the skill that makes you a better programmer."
    "You can use libraries, but DSA is what helps you crack coding interviews and optimize code."
    "Learning DSA isn't about replacing libraries—it's about understanding what's happening behind them."
    "Great developers know both: when to rely on libraries and when to implement efficient algorithms."
    "Libraries make coding faster, but DSA makes your solutions smarter."
    "If you only know libraries, you can code. If you know DSA, you can engineer solutions."
    "DSA is the foundation; libraries are just building blocks."
    "Don't skip DSA. Libraries change over time, but problem-solving skills stay valuable forever."


  • Is learning data structures and algorithms still necessary or can you rely on built-in libraries?
    R Renugadevi

    Although built-in libraries simplify programming, learning Data Structures and Algorithms is still necessary. DSA helps developers understand how data is organized, improves problem-solving skills, and enables them to write efficient and optimized code. Libraries are valuable for speeding up development, but they cannot replace the knowledge needed to select the right algorithm or data structure for a given problem. A strong understanding of DSA also helps in debugging, optimizing applications, and succeeding in technical interviews. Therefore, the best approach is to learn DSA first and then use built-in libraries effectively.


  • Is learning data structures and algorithms still necessary or can you rely on built-in libraries?
    N nismanoorul

    I believe learning DSA is still necessary because it improves problem-solving skills and coding efficiency. Libraries are helpful for implementation, but understanding the concepts behind them makes us better developers.


  • Is learning data structures and algorithms still necessary or can you rely on built-in libraries?
    K Kevin Bose J
    1. Scaling Beyond Simple Apps
      • Blind Debugging: Without DSA, you cannot trace memory leaks or understand hidden execution costs.
      • Invisible Bottlenecks: Small datasets hide terrible (O(N^2)) runtimes that crash production once data grows.
      • Architecture Failure: Choosing the wrong data layout forces messy, unmaintainable code rewrites later.
    2. When Built-In Functions Fail
      • The "Black Box" Problem: Standard library methods are optimized for general use, not specific edge cases.
      • Custom Constraints: High-frequency trading or gaming requires custom memory management that built-ins do not offer.
      • Algorithmic Dead Ends: Developers who rely solely on built-ins get stuck because they cannot rewrite the underlying logic.
    3. Memorization vs. Intuition
      • Rote Learning Fails: Memorizing code blocks is useless because real-world bugs never match textbook examples.
      • Intuition Wins: Focus heavily on recognizing structural patterns and understanding trade-offs (e.g., time vs. space).
      • The Core Skill: Knowing why a Hash Map beats a Treap for your specific look-up constraint is what makes an engineer valuable.

  • Is learning data structures and algorithms still necessary or can you rely on built-in libraries?
    AdminA Admin

    How will skipping DSA fundamentals affect your ability to debug and optimize once projects grow beyond simple apps?
    What challenges do developers face when a built-in function is not fast enough for their specific problem?
    Should students focus on memorizing algorithms, or on building the intuition to know which data structure fits which problem?


  • Which Emerging Technology Will Have the Biggest Impact by 2035?
    A Ashwin G

    The combination of Artificial Intelligence (AI) and Robotics is expected to have the greatest impact by 2035. AI enables machines to learn and make decisions, while robotics allows them to perform physical tasks. Together, they can revolutionize industries such as manufacturing, healthcare, transportation, and logistics, leading to increased productivity and innovation.
    Several emerging technologies will shape the future by 2035, including Artificial Intelligence (AI), Robotics, Biotechnology, Clean Energy, and Quantum Computing. However, AI is expected to have the broadest impact because it can enhance and accelerate the development of other technologies while transforming industries, economies, and daily life.


  • Which Emerging Technology Will Have the Biggest Impact by 2035?
    S SHADRACH MOSES

    Personally, I don't think any single technology will dominate 2035. The real transformation will come from AI becoming the "brain" behind other technologies such as robotics, biotechnology, and even space exploration.

    What makes AI different is that it doesn't just create a new industry—it improves every industry it touches. Whether it's healthcare, education, engineering, research, or business, AI is becoming a force multiplier. That's why I believe its impact will be greater than any other emerging technology over the next decade.

    The biggest opportunity is that humanity could solve problems much faster than before. Scientific discoveries that once took years may take months, and routine work could be largely automated, allowing people to focus on creativity, innovation, and complex decision-making.

    At the same time, I'm concerned about how quickly this transition will happen. Many people are still preparing for careers that may look completely different by the time they graduate. Job displacement, misinformation, privacy concerns, and overreliance on AI are challenges we cannot ignore.

    If I were advising students today, I would tell them not to focus only on learning tools. Tools change. Instead, develop strong problem-solving abilities, communication skills, technical literacy, and the ability to learn continuously. The people who succeed in 2035 won't necessarily be the best programmers or AI experts—they will be the ones who know how to work effectively with intelligent systems and adapt as technology evolves.

    In my view, the future belongs to adaptable learners, not specialists who depend on a single technology.

Member List

AdminA Admin
S SHADRACH MOSES
K Kevin Bose J
Y Yogeshwari
S shafina nisma
N nismanoorul
R Renugadevi
Y yogalakshmi
K kalyanaraman k
D Divagaran
A Ashwin G
S suguna
M MaingiTruth
M Mohsin_ramzan
H Hrithika S
K kagiro
S ShalomDataForge
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