What’s Behind "No Prior Coding Experience Required AI and Data Science Courses"?

In recent times, the market has been flooded with courses on AI and generative AI, often marketed with the enticing promise of high-paying jobs and no coding background required.

As a technical interviewer, I have encountered numerous candidates with 1-2 years of experience demanding salaries of 18-20 LPA. While there is no shame in demanding such pay if you possess the required skills, the reality is often far from this ideal.

The Disconnect Between Expectations and Reality

Many candidates, despite having taken popular courses, struggle to solve basic programming problems. It is not uncommon for them to justify their salary demands by claiming they already have a similar offer from another company. This raises a critical question: how can someone with 1-2 years of experience and no coding skills secure such offers?

The Pitfalls of “No Coding Required” Courses

Consider a scenario where a company hires you for a data science position with a package of 16 LPA. You have completed a course that claims to teach data science without any coding background. However, once on the job, you are required to write scripts to pull, parse, transform and analyze data. The templates provided by the course are insufficient, and you need to write custom code. Without a solid foundation in programming, you struggle to implement the required logic, relying heavily on resources like Stack Overflow or ChatGPT. Eventually, your lack of skills is exposed, leading to termination and a repetitive job search cycle.

The Role of Advanced Code Generation Models

Some may argue that advanced code generation models like GitHub Copilot or ChatGPT can write code on their behalf. While these tools are indeed helpful, they are not foolproof and require tweaking. Understanding the generated code, validating its correctness, and ensuring it covers all test cases still demand a good grasp of programming. As the saying goes, “Nakal ko bhi akal ki zarurat hai” (Commonsense or skill is required even to copy something).

Building a Strong Foundation

The solution lies in building a strong foundation. Start with the basics of coding, solve problems on platforms like LeetCode, and understand fundamental concepts like recursion, trees, and graphs. In data science, begin with traditional machine learning concepts before moving on to deep learning, reinforcement learning, transformers, and large language models. This structured approach ensures a solid understanding and quicker mastery of advanced topics.

Conclusion

The journey to becoming proficient in AI and data science is not easy, but it is achievable with the right approach. Here are four examples to illustrate the importance of a strong foundation:

Real-world Examples Of Candidates With Strong Foundation:

Candidate A: With a solid understanding of programming and traditional ML concepts, Candidate A quickly adapted to new challenges and secured a high-paying job.

Candidate B: After mastering traditional ML concepts & artificial neural networks, Candidate B successfully transitioned to deep learning and became a valuable asset to their team.

Candidate C: By consistently solving problems on LeetCode, Candidate C developed strong problem-solving skills, leading to multiple job offers.

Candidate D: With a comprehensive understanding of data science, Candidate D effectively utilized advanced code generation models, enhancing productivity.

Real-world Examples Of Candidates Without Strong Foundation:

Candidate E: Lacking a coding background, Candidate E struggled with basic tasks and was unable to meet job expectations, resulting in termination.

Candidate F: Despite completing a popular course, Candidate F’s inability to write custom code led to repeated job search cycles.

Candidate G: Over-reliance on code generation models without understanding the underlying logic caused Candidate G to produce faulty solutions.

Candidate H: Without a strong foundation, Candidate H failed to progress beyond basic concepts, limiting career growth.

while the allure of high-paying jobs with minimal effort is tempting, the reality is that a strong foundation in coding and data science is essential for long-term success. Invest in building your skills, and the rewards will follow.