[Nuclear House] 2025 Big Data Analysis Article (Written)_Subject 3~4

Are you feeling overwhelmed by the practical exam because you’re new to Python? An instructor with over 10 years of experience teaching data analysis will take you from the basics—starting with a single variable—to Type 1 preprocessing, Type 2 modeling and result.csv submission, and Type 3 statistical tests across 81 lessons. Each video comes with a fill-in-the-blank notebook, and you’ll type along yourself in a practice environment identical to the exam setting, so what you learn stays with you instead of ending when the video does. Use the self-assessment to choose your track (complete beginner: 6 weeks · experienced learner: 3 weeks · two weeks before the exam) and take only the lessons you need.

We’ll guide non-majors and beginners to quickly earn the Big Data Analysis Engineer practical certification! Keep the theory light and the practical training solid. Even without complex background knowledge, you’ll focus on past exam questions and study only the key points that are sure to appear on the test.

The Big Data Analysis Engineer exam is a nationally recognized certification exam held twice a year. For those preparing for the exam, we provide detailed lectures covering essential topics from the basics onward. The exam becomes more difficult with each round. To help you pass, we cover a range of important topics starting from the fundamentals to improve your chances of success.

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.

There are patterns to the questions that frequently appear on the Big Data Analysis Engineer practical exam. There are also a few lines of code you should definitely memorize before going to the exam. If your goal is to pass the exam, be sure to take this course.

Aim for a quick pass of the first nationally certified AI qualification! Includes three sets of practical problem-solving exercises reconstructed by the instructor after taking the exam personally, along with code templates.

Have you ever clicked on an Etherscan link after seeing a post saying, "A whale has moved funds to an exchange"? You likely closed the window immediately after seeing a screen filled with nothing but incomprehensible strings of characters. This course covers how to read that screen. An instructor with 14 years of experience in the security field has packed 15 lessons on how to directly verify wallets, transactions, and tokens using only free tools, without a single line of code. Now, judge for yourself using original records instead of relying on what others say.

SQLD, where should I start? For those of you feeling completely overwhelmed, this is the lecture for you. You will meet a transformed version of yourself after completing the course. SQLD All-in-One Pass!

We explain 32 data visualization techniques very quickly and easily from a non-expert's perspective!

This course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is necessary to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence.

Finally, the Probability and Statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice. Reference book "Probability and Statistics for Everyone" https://wikidocs.net/book/18165

Understand the core of big data business and provide insights on how to utilize it in marketing strategies in the field.

It covers basic data concepts, useful R functions and packages, and data analysis exercises, to allow anyone to perform big data analysis using R programming.

"Big Data + AI Full Stack Development" - The process of building big data that breathes life into AI is carried out in the form of a pilot project. We identify requirements, configure the platform step by step, and perform smart car data exploration and analysis.

Learn how to process big data from a Silicon Valley software engineer & how to develop big data code with Apache Spark using Python. I am a 14-year software developer who handles everything from web applications to big data and SRE & DevOps with Python. Don't miss this opportunity to learn about Apache Spark, which is essential for big data professionals, in an easy and in-depth way using Python!

Revolutionize your stock investing with AI! In this course, you’ll build a fully functional AI automated trading system that targets the top 25 Nasdaq stocks. Gain experience and know-how through paper trading with the Korea Investment & Securities API, and take advantage of this special opportunity to conduct AI-powered automated stock trading with a real account. Even if you’re a complete beginner, there’s no problem. We’ll explain the basic concepts of stocks and why you should invest in U.S. stocks in an easy-to-understand way, and provide all source code along with remote support. From now on, investing is essential for the next 60 years of our lives. Build your own AI investment partner to accompany you!

Data analyst job prep: Lost on where to begin? Get hints at this September seminar!

- Practical Kaggle Competition Project: From Data Exploration to Submission - Beyond Tutorials: Follow a Real Kaggle Competition from Start to Finish Have you signed up for Kaggle and completed the Titanic tutorial, only to feel lost about where to start when facing a real competition? If you simply copy someone else’s notebook and submit it, you’ll eventually hit the wall of wondering, "Why isn’t my score improving?" In this course, we’ll work through an actual Kaggle competition from start to finish, covering data exploration, validation design, feature engineering, modeling, and submission. Instead of memorizing code, you’ll develop an intuitive understanding of the competition mindset: “What should I try and why, and how can I validate its effectiveness?” [One line about your competition participation, awards, or practical experience]

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

We explored the essential Python syntax necessary for big data analysis and, through various examples, presented methods such as big data file processing and visualization.