ADsP Essential

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

I'll explore ADsP certification passing strategies and learn summarized memorization subjects. Reference book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

We'll learn about the Tableau visualization tool needed for the Business Information Visualization Skills practical exam. Don't hesitate any longer and take on the challenge!

ADsP Summary + Past Exam Solutions. Reflects August 2025 46th Exam! (Continuously Updated!)

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

You will learn how to develop data/machine learning web applications through 5 practical projects. You can easily and quickly build and deploy web applications with Python and Streamlit. (Updated in June 2025)

ADsP Summary + Past Exam Solutions. Reflects August 2025 46th Exam! (Continuously Updated!)

Do you want to become a data analytics expert of the future? Grab your chance now! This is a unique opportunity to explore infinite possibilities and learn how to derive powerful insights through cutting-edge artificial intelligence technology and big data.

This is a practice exam and latest past exam restoration problem-solving course for those who wish to obtain the Advanced Data Analytics Semi-Professional (ADsP) national certification.

This course focuses on practical training in the core foundations of data analysis and data preprocessing using NumPy and Pandas. You will learn step-by-step, starting from Python's basic data structures to NumPy arrays and Pandas DataFrames, and master various processing techniques for analyzing real-world data. By practicing the most frequently used functions in data analysis—such as data cleaning, transformation, merging, and grouping—you can naturally improve your data preprocessing skills for machine learning. The course provides both conceptual explanations and hands-on exercises so that even beginners can easily follow along, allowing you to build a solid foundation for data analysis and machine learning.

An easy-to-understand introduction to data lecture by Korea's first data humanist, Shin Do-yong! Let's learn data-based thinking to properly read and utilize massive amounts of data!

Based on the instructor's own failed experiences when first learning machine learning, this is a newly designed course that differs from existing lectures, making it easy to understand machine learning and apply it to real-world problems

I'll explore ADsP certification passing strategies and learn summarized memorization subjects. Reference book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

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.

The easiest ADsP introductory course that even non-majors can understand right away! From basic concepts to expected questions, everything you need to pass!

"The deep learning code runs, but why do the formulas in papers look so unfamiliar?" As you study AI, you eventually hit a wall of mathematical formulas. If you trace the source of that wall, it usually leads back to not having a solid foundation in the most basic mathematical language. Starting with the language of sets, this course covers core concepts across 18 lectures, including equations, functions, sequences, exponents/logarithms, and trigonometric functions. We don't make you memorize formulas. While we briefly touch upon how these connect to the context of AI, the primary goal of this course is one thing — to build an unshakable foundation for when you later learn linear algebra, calculus, and probability/statistics. As the first part of the Suminjeongeum series, this course will provide you with the common language needed to properly digest the remaining three parts.

Through this lecture, students are expected to be able to check actual materials such as big data contests and self-introduction essays and determine the direction of their studies and job preparation.

1. AI practice using an AutoML platform that performs data analysis and AI modeling without coding 2. Carrying out the entire process from data exploration, visualization, and preprocessing for data analysis to AI modeling 3. This course is optimized for passing AICE Basic, an AI certification organized by KT

Data Science(DS) & Artificial Intelligence(AI) job role analysis, plus a learning & employment roadmap for building a career in these roles.

Students who want to learn Hadoop and Big Data will celebrate the amazing advancements in experiencing the world of Big Data through this course!

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!

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

If you ask an AI to "build a model with this data," the first few lines might look plausible, but as soon as things get slightly complex, it starts using the wrong columns, introduces data leakage, and results in a model that performs worse despite a higher score. This course does not teach you better prompts. Instead, it covers how to directly design a loop (harness) where an agent understands data, plans, writes code, validates its own results, and backtracks when wrong. After manually walking through the five-piece loop of DS-STAR, you will assemble components one by one, from data profile cards, hooks, and skill hierarchies to independent validators and ablation.

As the popularity and interest in AB Testing explodes, we will introduce Korea's first AB Testing lecture right now!

"Smart Factory—it sounds cool, but can't quite picture it in your head?" The news talks about the 4th Industrial Revolution and AI factories every day, but when you actually look for information, it's full of "alien languages" like PLC, MES, ERP, and Edge Computing. What non-majors need isn't a complex circuit diagram, but a 'map' that shows how the whole system works. This course strips away difficult engineering jargon and explains everything about smart factories through intuitive illustrations and analogies that anyone can understand.

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.