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Excellent course, excellent instructor. THE GOOD: the instructor, Ximena, provided examples throughout of how a concept was applied in the real world, which made it (the concept) easier to grasp. As always, the hands-on approach mixed with the rest of the material really enhanced the learning experience. Illustrations of form, such as the one for an email, was great and more of that should be provided for beginners to take with them and use & develop as part of their own tool kits moving forward. WHAT COULD BE IMPROVED: how questions in quizzes and challenges are worded. I got dinged in a challenge because according to your site I got the answer wrong; however, the question is not clearly worded which led me to answer incorrectly. This is the second time this type of incident has occurred as it happened in the first course, and what's worse is that there's no communication avenue where I can reach out to and raise this concern. I take this course seriously as it is about my career what is affected, so scores and outcomes in marked challenges do matter to me. I still have not heard from the first time I raised my concern which was extremely difficult to connect with someone here on Coursera. Secondly, there should be material relating to the readings that can easily be downloaded for the student's future reference; at the moment I see some documents like forms and glossary, which is great, but nothing relating to the discussions in the videos. Videos are fantastic but notes that supplement these discussions could go a long way. Otherwise, this has been a great course delivered by a fantastic instructor and in a very professional way. Kudos Coursera/Google!

The "Ask Questions to Make Data-Driven Decisions" course is an insightful introduction to the importance of questioning in the data analysis process. It emphasizes the role of curiosity, critical thinking, and structured inquiry in uncovering valuable insights from data. Key Highlights: ✅ Understanding Stakeholders – The course explains how to identify and consider the needs of stakeholders while making data-driven decisions. ✅ Effective Communication – It covers best practices for presenting findings, asking relevant questions, and ensuring clarity in reporting. ✅ Meeting and Collaboration Skills – Provides strategies for leading productive meetings, respecting people’s time, and ensuring online meetings run smoothly. ✅ Decision-Making Frameworks – Introduces structured approaches to problem-solving using data, making it easier to analyze and interpret findings. ✅ Conflict Resolution – Teaches how to navigate workplace conflicts by reframing discussions and engaging in meaningful dialogue. Pros: ✔ Beginner-friendly and easy to follow ✔ Real-world scenarios to apply concepts ✔ Emphasizes both technical and soft skills ✔ Interactive quizzes to reinforce learning Cons: ➖ Some concepts may feel basic for experienced professionals ➖ Limited hands-on practice with data Final Verdict: This course is great for beginners looking to develop a structured approach to data-driven decision-making. It not only teaches data analysis fundamentals but also highlights the importance of communication and critical thinking in business settings. Highly recommended for aspiring data professionals! ⭐⭐⭐⭐⭐

Some questions in the quizes are ambiguous. English could be a barrier because certain terms are a little vague and are associated with different meanings in other languages ("problem", "issue", "manage", "responsible", etc). When digging into certain definitions, there isn't always one correct response or one correct way of saying the same things.

For example, a numeric range can't be properly defined as a quantitative variable because it lacks the quantum. It can be ordered for sure, but it have to be treated like a qualitative variable that can be recoded as a quantitative discrete variable.

I would like also to pinpoint an example of wrong response in the final quiz:

"Using historical data to make informed decisions about how things may be in the future is an example of spotting something unusual". The right response is "False" but in the real world in order to spot something unusual you have to analyze data and if this data is historical then the answer is True. This also applies to a similar question from the same quiz: "Using historical data to make informed decisions about how things may be in the future is an example of making predictions". The right response is "False", but it shouldn't because predictions are built on historical data (otherwise how do you make predictions? data is always historical, as per definition).

In other words, as far as this course is made for both beginners and advanced users, there are some missing concepts that lead to different opinions, especially for an advanced user. Just to pretend I know what I'm saying, I have a Master Degree in statistics.