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[퇴근후딴짓] 빅데이터 분석기사 실기 (작업형1,2,3)
퇴근후딴짓
비전공자, 입문자가 빅데이터 분석기사 실기를 빠르게 취득할 수 있도록 안내해드려요! 이론은 가볍게, 실전은 확실하게 복잡한 배경지식 없이도, 기출문제를 중심으로 시험에 꼭 나오는 포인트만 집중 학습합니다.
입문
빅데이터분석기사, 빅데이터, Python
This course was created for those who are preparing for the Big Data Analyst practical exam. It will help you pass the exam by learning as concisely as possible, and it will also provide an opportunity for those who are considering their career path to lightly learn about coding and machine learning.
Python
Machine Learning
Big Data Analysis Engineer
Big Quarterly Practice
Colab
A chance for those who don't know how to nose it 🙌
Challenge the Big Quarter practical skills with just one lecture!
Materials prepared to teach my girlfriend who is not good at koalas
I don't want to keep it to myself, so I'm sharing it as a lecture.
Learn just what you need, perfectly!
The Big Data Analyst Certification Exam, which debuted in late 2020, has been garnering significant attention!
However, for those who are new to coding and machine learning, it may sound like a distant dream.
This course is an introductory course on data/machine learning designed for those people.
Our goal is to provide guidelines for even the most novice Python learners to pass the Big Data Analyst practical exam with minimal knowledge. During the approximately 10-hour lecture period, we focus on the essentials, ensuring you pass the exam without missing anything.
The curriculum is designed to allow for repeated learning simply by listening to the lectures .
Master the content perfectly and pass the exam with minimal time and effort!
Machine Learning &
Python coding
First time encountering
Big Data Analysis Engineer
Practical test
Applicant
Even if you have to memorize it
The will and desire to pass
Anyone who has
Time and cost
I want to cherish
Lightning strike group
I passed the Big Data Analyst practical exam last year without any textbooks or practice questions. This course was carefully designed based on my own experience preparing for the exam, taking into account various issues and environmental constraints. I received feedback from my girlfriend, who had no prior coding knowledge, to develop the course, and I plan to further refine it through Q&A sessions with you.
This course covers the essential core concepts concisely enough to make you wonder , "Is this really enough?" I'll help you prepare for the exam and pass it just by memorizing it mechanically. 😊
We require only the bare minimum of learning from students. It's hard to believe that any course with less than this level of learning will guarantee a successful exam. We conduct training in an environment identical to the actual exam (Google Colab) , tailored precisely to the level of preparation required for the exam. We've eliminated unnecessary program installations and unnecessary learning due to configuration or testing environment constraints.
Based on feedback from my girlfriend, who had no prior coding knowledge, I've prepared this course with a difficulty level and structure appropriate for non-majors and beginners . We've also recommended various books and practice questions to reference during the learning process, providing even more diverse learning opportunities for those who need more than just exam preparation.
👉 If you are considering a career change in a related field,
We help you achieve realistic 'take-and-eat' !
We will explain how to use Colab and the future direction of the lecture.
Learn the basics of Python usage, data types, etc.
Learn about Python's basic grammar, including conditional statements, loops, and exception handling.
Learn the basics of Pandas, a library for handling data.
Learn how to manipulate data with Pandas.
Learn how to solve more challenging problems using Pandas (some of the tasks in Task 1 fall into this category).
Learn about the overall structure and techniques of machine learning.
We solve problems with a high probability of appearing on the exam using machine learning.
As our student body grows, we will continue to add more practice exam questions!
Q. How are the lectures conducted?
This course is based on the Notion textbook. To minimize the burden on students, the curriculum is designed to encourage self-directed learning within the course. Lecture videos for additional mock exams may be updated in response to additional questions and requests.
Q. What is the lecture material?
The course materials were personally created by me, drawing on various codes, study books, and reviews from actual test takers. Even those unable to attend the lectures can still learn through the annotations and explanations in the textbook. They're presented in Notion pages, allowing you to solve continuously updated problems.
Q. Are there any notes regarding the course (environment requirements, other precautions, etc.)?
This course is conducted on Google Colab, so you'll need a Google account. Otherwise, any computer capable of running Colab will do.
Who is this course right for?
For those new to coding
People who want to take the Big Data Analysis Engineer practical exam
People who are new to machine learning etc.
A person who has the will and desire to pass even if it means memorizing everything
People who want to pass the exam by cramming
Non-major
Beginner
All
29 lectures ∙ (13hr 20min)
Course Materials:
All
8 reviews
4.5
8 reviews
Reviews 2
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Average Rating 5.0
5
김동규 강사님, 안녕하세요. 본 강의를 통해서 이번 4회 빅데이터분석기사 실기에서 91점(단답형 21, 작업형 1유형 30, 작업형 2유형 40) 취득하게 되어 글을 남김니다. 확실히 빅데이터분석기사 취득을 위한 내용으로 포커스가 맞추어져 있다고 생각됩니다. 코랩으로 실습한 내용을 저장하여 복습으로 1~2번 더 쭉 읽어본 것이 반복학습이 되었고, 특히 실제압축코드를 기본으로 자기만의 최적화된 코드를 숙달한 것이 도움이 되었습니다. 강사님, 좋은 강의 많이 부탁드리고 더욱 번창하세요. 감사합니다.
도움이 되셨다니 다행입니다. 화이팅하세요!!
Reviews 16
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Average Rating 3.9
Reviews 2
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Average Rating 4.0
3
새로운 유형나왔는데 업로드되는것도 없고 화면은 너무 큰데 코드는 너무작아서 모니터가 커도 불편해요... 너무아쉬워요...
리뷰 감사합니다~ 3유형은 기존의 필기를 대체하는 형태고, 제가 직접 시험을 쳐본 방식이 아니다보니 조금 조심스럽습니다. 본 강의가 최소 시간 투입해서 작업형 1, 2유형 다 맞아서 필기(현재는 3유형)는 다 틀려도 합격하자는 취지라 더욱 그렇습니다. 강의보다는 강의를 통해 개념을 이해하고, 공유해드린 노션을 통해 자체적인 공부와 복습하는 부분에 특히 신경쓰느라 강의 화면 중 코딩이 잘 안보이는 문제는 차마 예상치 못했는데, 다시 한 번 검토해보겠습니다. 마지막으로 강의 들어주셔서 감사하고, 시험 잘 보셔서 나중에 현업에서 뵙겠습니다~
Reviews 1
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Average Rating 5.0
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