Preparing for Data Science Interview at Google with ChatGPT

Author(s): Sarvesh Talele

Originally published on Towards AI.

This will assist you in preparing for an interview with ChatGPT AI.

The recent excitement surrounding ChatGPT and its impressive capabilities has sparked a buzz among people as they consider the possibilities and advancements that future technologies may hold. Many are fascinated by ChatGPT’s unique features and are eager to see how it and other cutting-edge technologies will continue to evolve and improve over time. With ChatGPT and similar models, we are given a glimpse into a future where artificial intelligence and natural language processing play a more significant role in our daily lives, enabling us to interact with machines in a more intuitive and human-like manner. Whether it is through virtual assistants, chatbots, or other innovative applications, ChatGPT and its successors have the potential to change the way we communicate and interact with technology significantly.

What is ChatGPT?

ChatGPT is a variant of the GPT (Generative Pre-training Transformer) language model developed by OpenAI. It is designed to generate human-like text in a conversational context, allowing it to engage in natural language conversations with humans. Like other language models, ChatGPT is trained on a large dataset of text and uses machine learning techniques to predict the next word in a sequence. However, ChatGPT is specifically optimized for conversational text and can generate responses that are more contextually appropriate and coherent than those generated by other language models. ChatGPT can be used in a variety of applications, including chatbots, virtual assistants, and language translation systems. It has the potential to revolutionize the way we interact with computers and can be used to create more natural and intuitive communication between humans and machines.

After practicing and figuring out how ChatGPT works, I decided why not utilize it to practice for interviews or simply to enhance my technical abilities. Assume the following scenarios: a candidate is preparing for an interview for a Data Science position as a fresher, As a beginner, I asked ChatGPT one of the most basic questions:

How can I prepare for an interview for a Data Science career as a Fresher?

ChatGPT’s Response:

If you are a fresher (i.e., someone who is new to the field of data science), there are a few specific things you can do to prepare for an interview:

Build up your skills: Make sure you have a solid foundation in programming languages like Python and SQL, as well as statistics and machine learning. There are many resources available online for learning these skills, including online courses and tutorials.

Work on projects: Hands-on experience is crucial in data science. Consider working on small projects on your own or with a group to get practical experience and build up your portfolio.

Practice your communication skills: Data science involves explaining complex technical concepts to non-technical stakeholders. Practice explaining your work and the results of your analysis to others in a clear and concise manner.

Review common data science interview questions: There are many resources available online that list common data science interview questions. Reviewing these can help you get a sense of the types of questions you may be asked and give you an opportunity to practice your responses.

Overall, the key to preparing for a data science interview as a fresher is to build up your skills and demonstrate your enthusiasm and potential for the field. Good luck!

Following in the footsteps of ChatGPT, the most significant exercise for a Fresher is to enhance problem-solving abilities utilizing data science languages such as Python or SQL. However, explicitly doing programming before learning a language may not be as effective. As a result, we will strengthen the foundations and increase critical thinking skills by practicing Quantitative Aptitude and Quantitative Reasoning with the use of ChatGPT.

Quantitative Aptitude:

In this blog, I will use the Indiabix site to receive questions for learning the fundamentals, but if I get lost and there is no relevant explanation for a particular issue, the actual concern is whether we can trust ChatGPT to handle aptitude problems accurately. Below are some examples related to aptitude questions:

ChatGPT’s Response:

Result: Out of 6 different types of questions I have asked ChatGPT, it was able to answer only one correct answer, which too was a simple number systems problem.

These results are really surprising. ChatGPT has many calculative errors while solving Quantitative aptitude questions, but don’t think that ChatGPT provides fraudulent or random answers. The process ChatGPT takes to tackle every problem is credible but may lose sight in between solving the problem, but we must remember that ChatGPT is not an AI built to answer academic questions, it is built on real-world data while academic questions assume many basic things.

Nonetheless, in the end, you may need to practice aptitude questions based on your abilities; however, asking particular questions, such as the formula for compound interest, average, median, and so on, may be the best way to learn and grow with ChatGPT.

Programming :

ChatGPT, a variation of OpenAI’s GPT language paradigm, appears to have been actively discussed by programmers and used to produce code in numerous blogs. ChatGPT appears to be widely regarded as creating correct code. ChatGPT is intended for use in chatbots and other conversational AI systems, and it can interpret and create text akin to human writing. ChatGPT is most likely being used by programmers to produce code for a range of applications, and the results have been largely favorable. It is crucial to highlight, however, that ChatGPT and other language models have limits and may not always generate correct or suitable outcomes, especially in situations requiring more complex language understanding. Below are some Programming questions I took from Leetcode and the solution given by ChatGPT,

ChatGPT’s Response:

1. Two sum codes for leetcode (Input in ChatGPT)

2. Add Two Numbers leetcode (Input in ChatGPT)

However, the accuracy of the code generated by ChatGPT may depend on how the user interacts with the model. For example, if the user provides clear and concise instructions to ChatGPT, the model will likely be able to generate accurate code. However, if the instructions are ambiguous or incomplete, ChatGPT may struggle to generate code that meets the user’s needs. Additionally, ChatGPT and other language models have limitations and may not always produce accurate or appropriate results, particularly in tasks that require more advanced language understanding. Overall, it is important to carefully consider the inputs and limitations of ChatGPT and other language models when using them to generate code or other types of output.

Interview Prep:

In this step, it is preferable to choose a specific organization to better comprehend ChatGPT’s response and to have a clear purpose for what we are preparing. The following are the questions I asked ChatGPT. It is beneficial for people to comprehend the underlying essential aspects as well as the process of growing their thinking ability.

How can I prepare for an interview for a Data Science career at Google?

ChatGPT’s Response:

Here are some tips for preparing for an interview for a data science career at Google:

1. Familiarize yourself with the company and its products: Research the company’s mission and values, as well as its products and services. This will help you understand the types of problems that the company is trying to solve and the technologies it uses.
2. Review the job description and requirements: Make sure you understand the specific skills and qualifications that the company is looking for in a candidate. This will help you tailor your responses to the specific needs of the role.
3. Practice technical skills: Depending on the level of the role, you may be asked to solve problems using programming languages such as Python or SQL. Make sure you are comfortable with these tools and can demonstrate your proficiency.
4. Prepare for behavioral questions: Google often asks behavioral questions to gauge how you handle situations and work with others. Think about your past experiences and how they relate to the job and be prepared to provide concrete examples.
5. Review common data science interview questions: There are many resources available online that list common data science interview questions. Reviewing these can help you get a sense of the types of questions you may be asked and allow you to practice your responses.

Overall, the key to preparing for a data science interview at Google is to be well-prepared and confident. Good luck!

When comparing our previous question, How can I prepare for an interview for a Data Science career as a Fresher, with its specific question, How can I prepare for an interview for a Data Science career at Google? Simply asking a precise and goal-oriented query improved the AI’s response; instead of a more broad answer, the AI provided more particular solutions such as specific areas to focus on and which programming languages are frequently utilized. Now for the following question:

Possible questions which may be asked in an Interview for a Data Science Role at Google

ChatGPT’s Response:

Here are some possible questions that you may be asked in an interview for a data science role at Google:

1. Can you explain the difference between supervised and unsupervised learning?
2. How do you handle missing data in your analysis?
3. Can you explain how a decision tree algorithm works?
4. Can you explain how a random forest model works?
5. How do you evaluate the performance of a machine-learning model?
6. Can you explain how boosting algorithms work?
7. Can you explain how a neural network works?
8. How do you handle imbalanced classes in your dataset?
9. Can you explain how collaborative filtering works?
10. Can you explain how a recommendation system works?

Keep in mind that these are just a few examples and the specific questions you will be asked will depend on the role and the company. It’s always a good idea to review common data science interview questions and practice your responses in advance.

Let's twik our question a little,

Here are 50 possible questions that you may be asked in an interview for a data science role at Google:

ChatGPT’s Response:

1. Can you explain what data science is and how it is used in the industry?
2. How do you go about exploring and understanding a new dataset?
3. Can you describe a machine learning model you have worked with and explain how it works?
4. How do you handle missing or incomplete data in your analysis?
5. How do you evaluate the performance of a machine learning model?
6. Can you describe a time when you had to deal with a large dataset and how you went about analyzing it?
7. Can you explain the bias-variance tradeoff in the context of machine learning?
8. Can you describe a challenging machine learning problem you have worked on and how you approached it?
9. How do you go about selecting features for a machine learning model?
10. Can you explain the concept of overfitting and how it can be mitigated?
11. How do you deal with imbalanced classes in a machine learning dataset?
12. Can you describe a project you have worked on where you had to communicate your findings to a non-technical audience?
13. How do you go about choosing the right evaluation metric for a machine learning problem?
14. Can you describe a time when you had to handle a large amount of data and how you did it?
15. Can you explain the concept of regularization and how it is used in machine learning?
16. Can you describe a time when you had to work with a team to solve a data-related problem?
17. Can you explain the concept of cross-validation and how it is used in machine learning?
18. How do you go about handling outliers in a dataset?
19. Can you describe a machine learning model you have used that required feature engineering?
20. Can you explain the concept of ensemble learning and how it can be used to improve model performance?
21. How do you go about handling class imbalance in a dataset?
22. Can you describe a time when you had to use data visualization to communicate your findings?
23. Can you explain the concept of deep learning and how it differs from other machine learning techniques?
24. How do you go about selecting the right machine learning algorithm for a given problem?
25. Can you describe a time when you had to use natural language processing (NLP) in your work?
26. Can you explain the concept of transfer learning and how it can be used in machine learning?
27. How do you go about handling missing or incomplete data in a dataset?
28. Can you describe a time when you had to use unsupervised learning in your work?
29. How do you go about selecting the right evaluation metric for a given machine learning problem?
30. Can you explain the concept of boosting and how it can be used to improve model performance?
31. How do you go about handling imbalanced classes in a dataset?
32. Can you describe a time when you had to use reinforcement learning in your work?
33. How do you go about selecting the right features for a machine learning model?
34. Can you explain the concept of feature selection and how it can be used to improve model performance?
35. How do you go about handling outliers in a dataset?
36. Can you describe a time when you had to use feature engineering to improve model performance?
37. How do you go about selecting the right machine learning algorithm for a given problem?
38. Can you explain the concept of overfitting and how it can be mitigated?
39. How do you

ChatGPT is used to generate answers to a series of questions related to data science. However, the model stopped providing answers after the 39th question. This may be because ChatGPT is designed for use in chatbots and other conversational AI systems, and may not be well-suited for answering a large number of questions in a row. Alternatively, it could be that the questions became too difficult or specific for ChatGPT to handle. In any case, it seems that the series of questions was intended to help people learn more about data science and explore the capabilities of ChatGPT. The questions were likely chosen to cover a wide range of topics and to challenge the model’s understanding of data science concepts. Overall, it seems that ChatGPT and other language models can be useful tools for learning and exploration, but it is important to understand their limitations and use them appropriately.

Lessons I Learned:

1. ChatGPT, in my opinion, is an AI that will enhance our thinking process. Using it in daily life will also increase the clarity of questions and our perception of any concept.
2. When it comes to technical difficulties and solutions, asking open-ended inquiries may not be very useful; instead, asking precise queries.
3. Content generation: ChatGPT may be used to create text that looks like human writing, which can then be utilized to generate content for websites or social media.
4. Text summarization: ChatGPT might be used to create systems capable of condensing large bits of text into shorter, more succinct summaries.

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Published via Towards AI