BEST

[After-work activities] Big Data Analysis Engineer Practical (Work-type 1,2,3)

We will guide you so that non-majors and beginners can quickly acquire the practical skills of a big data analyst! We cover the essential Python, Pandas, and machine learning!

(4.9) 281 reviews

2,740 students

Engineer Big Data Analysis
Big Data
Python
Pandas
Machine Learning(ML)
Thumbnail

This course is prepared for Beginners.

What you will learn!

  • Big Data Analyst Practical Tips

  • Python Basics

  • Pandas Basics

  • Machine Learning Basics

  • Statistics Basics

❤️Notice❤️ 2024 8th exam type update completed , task type 1,2,3 mind map PDF provided
(The curriculum order may change due to content updates in September and October)

Changes in job competency and talent image, data analysis capabilities needed by everyone 💡 📑

As digital transformation accelerates, non-majors and other occupations are also required to have data analysis capabilities and an understanding of AI technology. Accordingly, the need for 'data-type talent' who can read and write data well has also arisen, and the recruitment of data analysts is also receiving significant attention.

When building a career in data analysis, a lot of experience is important, but certification is essential to be recognized for my expertise. Among them , the only national technical certification related to big data is 'Big Data Analysis Technician' .

What is a Big Data Analyst Certification? 💡

  • ✔️ It is gaining attention due to the increasing demand for big data analysis experts.
  • ✔️ This is the only national big data technology certification in Korea that was newly established and has a small number of successful candidates.
  • ✔️ You may receive preferential treatment when applying for a job/changing jobs/promotion.

Student Results and Reviews ✨


Content-based production verified by hundreds of people on the global AI platform Kaggle ✨

Lecture Features ✨

1) I will organize all the test materials that were hard to find and send them to you!

Big Data Analysis Engineer, which was first implemented in 21 years and has very little information about the exam! The most important thing in the practical exam is practice. Therefore, this lecture, which can solve both theory and practice at once , will help you pass the practical exam as quickly as possible.

The best curriculum for obtaining qualifications 🚊

  • ✔️ Selected key concepts to help you pass in one go
  • ✔️ Simple practice environment
  • ✔️ How to respond to error messages that will 100% confuse you when you encounter them on the exam
  • ✔️ Prepare for the exam by solving past and expected questions

2) Let me explain it to you in an easy way!

Many people start with Python for data analysis and give up on programming. However, the Big Data Analysis Engineer practical exam is not a coding test! In other words, you should focus more on understanding and 'utilizing' data analysis/machine learning concepts rather than programming skills.

3) You can start learning right away without any installation or setup process.

This is a practical course designed to help you obtain the 'Big Data Analyst' license. There is no installation or setup process as it is a cloud-based environment.

  • ✔️ Practice on Colab (Google Colab) or Kaggle (Kaggle, global AI competition platform). We will explain in detail how to conduct the practice in class!
  • ✔️ After completing all the learning, we will help you get used to the testing environment (IDE).

Check before taking the class 👀


Introducing the knowledge sharer ✒️

Hello! This is After-Off-Off-Off-Off . I started out of curiosity and became interested in machine learning/deep learning. I am currently working as an AI Educator, growing and sharing every day.

I am also a non-major, so I know the difficulties better than anyone else. I created this lecture to help beginners who are wondering, "How should I prepare?", "How much should I prepare?", "In what environment should I practice?" I am sharing machine learning/deep learning content on YouTube 'After Work, Doing Other Things', and I am growing every day by running Kaggle Study online and offline. I am also writing a deep learning book with experts and giving lectures/mentoring activities.

These days, "data-related certifications" are very 'hot' and popular in companies, so a lot of training is being conducted in-house. Let's start learning AI and machine learning together and have fun :)

I have grown with experts by running online and offline meetings for 5 years (more than 200 times) in machine learning/deep learning study. I did it even though I am not a major. So you can grow too. I know the parts that I had difficulty studying when I was a beginner, so I will explain them in a way that beginners can easily understand. Data analysis/AI does not matter your major. I will help you grow quickly!

When an expert teaches a beginner, there is something called an Expert Blind Spot . It means that there are parts that an expert in that field cannot properly explain to a beginner in that field. When you first learn something, you forget the parts that were difficult and they become natural! I also think that a person who has just studied can explain it better than an expert. From this perspective, I will guide you to the path of passing the exam at the level of a beginner!


Recommended for
these people!

Who is this course right for?

  • Those preparing for the Big Data Analysis Engineer practical exam

  • Those who want to obtain a short-term qualification

  • For those who are unsure of how to study Python, Pandas, machine learning, and statistics

  • For those who want to save time

Hello
This is roadmap

2,740

Students

281

Reviews

2,273

Answers

4.9

Rating

1

Course

  • 저서:

    • 2025 시나공 빅데이터분석기사 실기 (길벗)

    • 파이썬 딥러닝 텐서플로 (정보문화사)

  • 유튜브: https://www.youtube.com/@ai-study

     

More

Curriculum

All

81 lectures ∙ (20hr 7min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

281 reviews

4.9

281 reviews

Similar courses

Explore other courses in the same field!

$77.00