I'm sorry to give such a low rating to the course, but I couldn't get myself to finish it, as it is just one giant ad for generative AI to fuel the AI hype train, without teaching anything substantial, and repeating the same points over and over. While this introduction course does go over some very basic concepts in data science (e.g., what are data inconsistencies and some common "traditional" methods to deal with them), it does so without definitions. I expected it to at least dive into how these big AI models work then, but it doesn't do that, either. Here are some highlights of why I believe this course should be seriously reworked: - It goes over the same concepts numerous times: once in a video, then in a very very lengthy reading of the very same thing. - The course material is seriously biased towards setting up generative artificial intelligence as the ultimate solution to our problems, with one or two times (after several videos) warning the student that human validation is needed. Other times, each video is usually all about how genAI will solve this and solve that, and it can "identify nuances that humans can't", but nothing concrete. - The course gives us examples, like: imagine we have medical records and want to diagnose a rare disease, but we only have a hundred patients' medical records. Generative AI can "understand the nuances" of the data and generate much more new records that helps us analyze the data and train models for it. Seriously? One of the most important things taught on statistics courses is that extrapolation is very dangerous. The video never mentions that this can introduce bias or completely derail research. What if the devised treatment would've worked on the LLM's patients, but made matters worse for the real ones? These cases are barely covered in some of the reading material, but even those did not come back to the medical data example. - Videos that demonstrate the AI (Microsoft Copilot) in action actually show that it's not doing what it was asked to do: in one video, they ask it to generate a box plot, and Copilot (on the second try) generates a bar chart. But the video keeps going as if it was a box plot. In the same video, they say "the AI has identified several missing data points", where on the video, there is clearly one missing data point in the data and one that it identified. In another video, the voice over says that the AI (Copilot) generated a chart, yet no plot appeared throughout the demonstration - we could see pivot tables in the Copilot chat window, but no generated plots. These demonstrations videos are a pain to watch and I'm surprised that nobody has validated that the recorded footage and the voice over are actually of the same thing. - Assignments that ask you to work with datasets and "ask a genAI model" do not point you anywhere: you'll have to find ChatGPT (or Copilot) or Claude yourself and input the data, and pray that it outputs what the expected answer is for the question - or just do it yourself, without the AI (spoiler: neither of them gave a good answer, I did the analysis myself to get the good results). Sure, it's a great way to learn it yourself, but it shouldn't be a practical assignment then. - Other assignments sometimes ask you about things that were not or barely mentioned in the course (like which type of machine learning model (i.e., RNN, GAN, etc.) would you use for this type of data analysis task), or are just the same "advertisement" type of questions where you always pick the option that says genAI is the ultimate solution that uses advanced machine learning algorithms to uncover hidden patterns in your data. All-in-all, I was hoping that this course would actually go a bit deeper into understanding these new AI models instead of just treating them as magical black boxes that solve all our data analysis problems. I was also expecting it to give me a balanced view of the pros and cons, along with practical knowledge of incorporating AI into a data analysis workflow instead of a Copilot ad gone wrong. I would recommend students to pick a course that teaches the actual inner workings of LLMs (with definitions and practical guides), provides a less biased view on the topic, and then find another course to learn how to use these LLMs with data. Once again, sorry for being this critical, but I believe this course really only teaches one to believe that genAI will do so many things we can't. Which may be true, but you don't teach that through a course, but instead you form that opinion yourself through experience.

This course has the flavor of AI-generated content. All the readings are written in a very smooth tone, unfocused, repetitive, and rambling. There is something to learn hidden in the readings, but getting past all the flowery AI junk was a chore. I started the second course, but it turned out to follow the very same trend. You could scramble all the reading assignments and not notice much of a difference in course consistency. There are also some AI hallucinations in the assessments. So, overall, two stars, since there was a little bit that I learned. But for me, this course is not worth the time and effort.

A huge amount of repeating information. Disappointing and frustrating.

This is marketing content for Microsoft products.

a lot of repetition

Course Review: Introduction to Generative AI for Data Analysis Dear Instructors, My name is Muhammad Inzamam Khan, and I am currently pursuing my Master’s in Industrial and Organizational Psychology at the University of Indonesia. I wanted to take a moment to thank you for the excellent course, Introduction to Generative AI for Data Analysis, and to share my feedback about my experience. Why I Took This Course As someone deeply interested in the intersection of psychology and data analysis, I was keen to explore how generative AI could enhance my understanding and application of data-driven insights. This course was the perfect opportunity to learn about cutting-edge tools and techniques in the growing field of AI. Key Takeaways Throughout the course, I gained foundational knowledge in: Basic Statistics: Refreshing my understanding of essential statistical concepts. Prompt Engineering: Learning how to effectively interact with AI systems to extract meaningful insights. Data Visualization: Mastering techniques to present data in a clear and impactful way. Ethical Perspectives: Gaining a deep appreciation for the importance of ethical considerations in AI applications. What I Loved About the Course The course was well-structured, with clear and engaging explanations of complex concepts. The practical examples and exercises allowed me to directly apply what I learned, which significantly enhanced my skills. The insights into ethical AI were particularly enlightening, helping me understand the broader implications of using AI responsibly. Final Thoughts This course has been incredibly beneficial for my academic and professional growth. I now feel more confident in using generative AI tools for data analysis and integrating them into my research in industrial and organizational psychology. Thank you for your dedication and effort in designing such a valuable and impactful course.

I thoroughly enjoyed this course! The content was well-structured and easy to follow, providing a perfect balance between theory and practical applications. The explanations were clear, engaging, and supported with real-world examples that made complex concepts easy to understand. The interactive exercises and assessments helped reinforce my learning effectively. I feel much more confident in applying these skills in real projects now. Highly recommended for anyone looking to deepen their knowledge and gain hands-on experience!

Easy to understand with good examples, resulting in applying the lessons immediately for creating GenAI prompts and workflows.

Overall it was really good, but theory was too much. Much appreciate if include more practical practices.

So honored to be part of it and learned a lot from it

Excelente inicio para utilizar una IA responsable

nicee

nice

nice

good

easy

Too many assignments - some of these can be removed. Mostly theory and missing good practical examples and demos.

Algunas de las practicas son confusas o estan mal traducidas ya que nose comprende lo que realmente solicitan.

This is very good course for anyone planning to use Gen AI for data analysis tasks.

Bastante basico, pero introduce un buen sustento para iniciarse.