
초보 기획자/PM을 위한 Test Case 작성 및 QA 노하우
플래터
첫 프로덕트의 출시를 앞둔 초보 기획자/PM을 위해 Test Case 작성 노하우와 실전 QA 노하우를 알려드려요.
초급
서비스 기획, Test Case, QA(품질보증)
Learn the problem definition and hypothesis formulation process, core competencies for data analysis!
Problem Definition Process Necessary for Data Analysis
The Essentials of Data Analysis Before SQL
Problem Definition Know-how for PM/PO/PA
When I first became interested in data, I was overwhelmed by tools like SQL, Python, R, statistical theories, and machine learning. I was anxious, so I took classes, studied, and obtained certifications, but I was still stuck. However, I realized while analyzing data in the field and improving products/services through it. The most important thing for data analysis is not tools, skills, or difficult theories, but defining the problem you want to find the answer to through analysis and designing a hypothesis for it .
So this lecture is designed for those who are learning technologies like SQL for data analysis at this very moment and have missed the most important problem definition and hypothesis design. Without difficult terms or tools, I will tell you what you really need to do for data analysis !
I recommend this to these people!
This is my first time doing data analysis
New~Junior Analyst or PM/PO
Beyond writing queries, discovering problems
Anyone who wants to grow into an analyst who verifies hypotheses
I learned SQL, but what do I do now?
I don't know what to do.
I learned SQL, but I have no experience with interviews and portfolios.
I keep getting dropped from feedback.
Am I an analyst?
I don't know if you are a SQL query writer.
What skills are most needed for an analyst?
I want to know and learn.
There are many things that are said to be necessary for the work of an analyst, such as SQL, Python, R, statistics, machine learning/deep learning, etc., but these are only tools and means. What is really necessary to grow into a data analyst who discovers and defines problems in a product, verifies hypotheses, and grows the product through this is not JOIN and Sub Query, but the ability to define problems and design hypotheses for analysis!
💡 This lecture!
Before we start writing queries, let's go through the process of defining what question we really want to answer.
Is there anything we already know to solve the problem we have defined? And do we really need to analyze all of it? We will look at the process of designing hypotheses and prioritizing numerous hypotheses.
Do you really need to write SQL queries to analyze the problems and hypotheses you have defined? And what numbers should you derive? Let's look at the things to consider when designing an analysis task.
How should we interpret the numbers derived from the analysis? And is our work finished once we have analyzed it? Let’s take a look at the key tasks that must be taken care of after the analysis.
Are advanced SQL skills necessary for PM/PO and PA who analyze data to grow products? Why do we analyze data in the first place? Let’s take a look at the core context of our data work.
Q. Can you teach me SQL?
This course will not explain SQL! Instead, I will explain the process of defining a problem and establishing a hypothesis, which is essential for data analysis. If you learn SQL, Python, or R based on this, you will be able to grow faster!
Q. As an analyst, isn’t technology important?
Isn't analysis ultimately a method or process for finding answers to our questions? If the problem we want to find answers to is unclear, no matter how advanced the query is or how difficult the statistical theory is, will we be able to find the right answer?
Q. So, are you saying that I shouldn't learn SQL or other tools?
Of course, defining a problem and establishing a hypothesis does not mean that analysis is done! I think that if you understand the process of defining a problem and establishing a hypothesis covered in this class well and then learn and master the necessary tools and instruments, you will be on your way to success!
✔️ Things to note before taking the class
Who is this course right for?
PMs/Planners: Data is needed too, they say... Should I learn SQL first?
For those who find data analysis difficult despite learning SQL
Those who blindly learn SQL first for data analysis.
601
Learners
31
Reviews
1
Answers
4.4
Rating
2
Courses
사수 없이 시작하고 성장하는 기획자, PM/PO, 분석가를 위한
역량 개발 실험실 플래터 워크랩을 운영하고 있습니다.
서비스 기획, 프로덕트 매니징, 프로덕트 데이터 분석의 지식과 노하우를 나눕니다.
- <성장하는 PM을 위한 프로덕트 매니저 가이드> 저
- <전략적 사용자 행동분석> 저
- 네이버 클라우드, 우아한형제들 등 기업 및 기관 강의 다수
- 300명 이상의 취업준비생 및 주니어 멘토링 및 강의
브런치 : https://brunch.co.kr/@539insight
뉴스레터: https://maily.so/platter.worklab
All
12 lectures ∙ (2hr 54min)
All
3 reviews
4.3
3 reviews
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Average Rating 5.0
Edited
Reviews 1
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Average Rating 3.0
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Average Rating 5.0
5
8년차 개발자입니다. PM으로 직무를 변경하였고 자료를 찾다가 이 강의를 듣게 되었는데요. 데이터분석을 하려면 SQL이 필수이고, 이미 쿼리를 짤 수 있지만 어떤 수치를 어떻게 볼 지 결정하는 것은 항상 고민이 되더라구요. 해당 데이터를 바탕으로 솔루션을 내야하기 때문에요. 내가 중요하다고 생각한 데이터를 다른 사람들에게도 설명할 만한 근거를 찾고 싶었습니다. 해당 강의를 들으면서 데이터 분석을 위해 먼저 문제 정의를 하는 방법을 알게되었고, 어떤 지표를 보면 좋을지 결과물은 어떻게 생성되어야할지, 또 어떻게 해석하면 좋을지에 대해 답을 얻게 되었습니다. 이미 PM을 하시는 분도, 준비하시는 분들이 들어도 좋을 강의입니다 :)
정성스런 리뷰 감사드립니다 :) 이후에 본 강의에서 제공한 예시 외에, 프로덕트에서 다루는 다른 분석 예시를 추가해서 문제 정의부터 액션 플랜 도출까지의 과정을 더 생생하게 만들어보겠습니다!
$38.50
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