Data Literacy for PMs (Product Data Analysis)

I have only listened to half of it, but I am leaving a review in advance because it is such an impressive lecture. I am a student working as a UXUI designer at a large company. After moving from an agency to an in-house position, there were many parts that I did not understand the reasons for decision-making or the contents of meetings, so I decided to take the course to learn the language of PM and practice looking at problems from a business perspective. In fact, I started studying thinking that the content would be difficult because it was an unfamiliar field, but thanks to the language that was easy for the other person to understand, my understanding of data has greatly improved to the point where I can apply it right away in my work. After listening to the lecture, my perspective on existing work documents completely changed. In addition, I was very impressed by the part where he answered the questions of the students and briefly mentioned persuasion and negotiation. I felt that I wanted to learn a lot about his personality as well as his job competency because I could feel his attitude towards work and colleagues. I will definitely read Nonviolent Communication and People Outside the Box. After taking this lecture, I want to take other lectures! Thank you! (I think this is something that not only PMs but also designers would benefit from knowing, so I think it would be good to write down something that designers would also benefit from hearing when marketing! I would like to share it with everyone in the neighborhood!)

Thank you so much, Kyle School!! I listened to everything except the parts I left to listen to when I actually encountered it. It's a great lecture that I can always refer to when I run into a problem. Thank you for making it! I keep recommending it to people around me. I'm always grateful for the sincere advice..!! https://sowhatmylifeismine.tistory.com/263 This is a summary of the parts I used while listening to the lecture! It's not enough, but I hope it will be of some help to others!

The lecture content was very helpful to me, so I am leaving a detailed review in case it will be of some help to those who are considering taking the course :) [Background for taking the course] - I work at a digital marketing agency to help accurately measure and improve advertising performance by better utilizing Ad Tech and data. - However, since the team was newly formed and we were working without a mentor, the process for solving problems based on data was not well established, so I took the course to stabilize the team's work system. [How I took the course] - I thought it would be virtually impossible to complete the course with just my own will, so I gathered study members on Discord...! (There were more people like me than I thought, lol) - I recruited people with similar goals and wills and opened a study group, and operated it for the purposes of 1) completing the course, and 2) sharing each other's experiences. - There were often cases where the content I needed right away was covered in the course while I was working, so I put the course up and used it as a handbook while working. - I organized the newly learned information or the information I wanted to share with my team members into a document in Jira Confluence and shared it, and spread the knowledge within the team. [What improved after listening to the lecture] - Other existing data-related lectures seemed to deal with technical aspects, but rather than these technical aspects, I was able to build the soft skills that are most needed in the field. - Communication became clearer as I gave names to frameworks and methodologies that I had been practicing without even knowing the names. - In particular, the problem definition framework and data log design part were the parts that were closest to my domain, so I received a lot of help in this area. - As I learned about various methodologies that I had not known before, I was able to approach and solve problems in new ways. [Who would you recommend it to] - I think it would be more helpful to current employees than job seekers (there are so many moments where you really agree and sympathize while watching the lecture...) - I think it would also be of great help to marketers who communicate based on data (you can gain insight into how to utilize trackers such as GA or Appsflyer) - I also recommend it to everyone who wants to solve problems based on data. Fighting to you all!

The data literacy lecture was actually given while I was preparing for a job change. I had been looking forward to Kyle’s lecture so much that I even took at least two lectures while reducing my sleep during the process of preparing for a job change. In fact, I knew that working at a company and preparing for a job change were already overwhelming, but this lecture really helped me a lot not only with the job change but also with adjusting to a new company. In fact, I thought that the process of preparing for a job change was more like a time of converging and organizing what I had done for the past two years. So rather than trying to add more input, I focused on organizing what I had done and producing output (portfolio, resume, etc.). However, taking this lecture really helped me prepare for a job change and an interview, so I would like to tell others to make time to input at least 2-3 hours on the weekend during the period of preparing for a job change. (I really recommend this lecture!) The reason why I think input is important is as follows. 1. You get a frame of mind - When you are actually trying to write a portfolio or resume, how do you organize it? It starts to feel overwhelming. It was difficult to organize various tasks such as dashboards, data requests, metric settings, and project progress into one framework because the work was slightly different. However, the lecture organized the framework or workflow for each task, which helped me organize my thoughts and career a lot. 2. You get to learn a lot of good sentences. - Kyle's lecture starts with "For PM~", but in fact, since Kyle went through the career of data analyst → data analyst, there are many sentences in the lecture about what a data analyst does. Among them, the sentence I often used in interviews is "I think what I do is to help reduce someone's uncertainty. (Hereinafter, my thoughts)". I used to simply express what I do as helping with decision making, but after listening to the lecture, I learned that the same thing can be expressed in such a cool way, and in that sense, I think it's good to read the thoughts or writings of people who have been in the field. If you're curious about my detailed job change review or method, you can check out the link below! Lastly, Kyle's lecture with his know-how was so helpful! (Thank you so much!) I think it would have been really helpful if I had been able to take this lecture when I was working as a data analyst alone in my first or second year. It seems to be useful for single-chapter analysts as well! https://lunadata.oopy.io/c3be04e4-887d-44c2-bae9-17f04640d767

I am currently working as a data analyst. As soon as I heard that Kyle's lecture was uploaded, I paid for it and quickly watched the interesting parts first. I think this lecture will be helpful not only for PMs but also for those in the data field. In particular, I think it would be good for those who are interested in the data literacy capacity and data culture of an organization.

These days, PM is a given, and I think the core competency of a data analyst is not simply the ability to use tools, but the ability to solve problems. I think this lecture has a great influence on developing the ability to solve service problems based on data, such as 'problem selection', 'setting key indicators', 'what data should I look at?'. I think it was really lucky to have found this lecture. Thank you!

I really liked the lecture because it focused on how to solve problems and make decisions with data, rather than on data analysis skills. I really liked how the lecture was structured so that you could think and act based on situations that could actually happen through actual case studies, rather than just talking about ways of thinking or mindsets. Maybe that’s why I tried it out with my team members right away and got help. It was also really great that you could get 1:1 coaching from Kyle if you took more than 70% of the course. I hope that many people will learn data problem solving and decision-making processes through this lecture and become good workers at their companies. Thank you so much for creating such a great lecture.

This is definitely the best lecture among lectures related to data analysis. Other lectures mainly deal with theoretical contents/techniques such as statistics, so I often had a hard time putting it into practice in places that did not have a data culture. I wondered why I could not properly apply all this know-how in practice, but through this lecture, I was able to get a sense of how to fill that gap. In particular, the part where each part provides cases that can be experienced in an organization, and I think it was easy to find application points in the company while practicing with a virtual app called Foodie Express. I would like to say that this is a lecture that everyone who deals with products in the IT industry should definitely watch :)