[Non-majors are very welcome] Python data analysis that even zero-base users can easily learn - Kaggle Introduction

This introductory course, designed to be the easiest and most practical for Python beginners, covers intuitive dashboards using Streamlit, Google Cloud Platform for deployment, and more. Additionally, you can also prepare for the 빅데이터 분석기사 practical exam.

"Why does my automated trading collapse in a bear market?" I have also deeply pondered this same question. Most automated trading logics on the market seem to generate profits during a bull market, but they lose their ability to respond the moment the market sentiment changes. To solve this problem, I have combined the know-how I researched myself over the past two years with the latest AI technology. In this course, you will learn how to directly design a 'Transformer-based deep learning model' that simultaneously reads macroeconomic trends and individual stock flows, and how to automatically train and deploy it in a free GPU environment. In particular, we will introduce sophisticated data normalization techniques at the hedge fund level to build an 'Intelligent Comprehensive Scoring System' that goes beyond simple indicators to incorporate news sentiment. To this, we add an 'AI Safety Gate' that implants the insight of a trader with 20 years of experience on Wall Street, adding a final layer of confidence from a human trader's perspective right before a purchase. Beyond simple buying and selling functions, you will directly implement the entire process of an institutional-grade algorithmic system by adding dynamic take-profit and stop-loss logic, eight self-recovery mechanisms, and real-time Slack reports.

"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.

Doing it carelessly is worse than not doing it at all. You'll just waste time! From now on, let's solve it thoroughly and perfectly together!

Prepare perfectly for the Goorm environment—the biggest challenge of the Big Data Analysis Certification practical exam—on the practice platform provided by Algorun. Practice past exam questions for Task Types 1, 2, and 3 repeatedly in a setting identical to the actual exam environment to gain an intuitive feel for the test rather than just memorizing code. This is a practice-oriented lecture designed to train your hands to move automatically in an environment that differs from Colab or Jupyter.

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.

SQLD Yellow Book 176 Problem-Solving Course for those who studied but can't solve problems. After completion, you'll go beyond passing to become an expert. SQLD All-in-One Pass!

We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

"From environment setup to practical business projects, a data science journey to find revenue answers through data (39 lectures in total)." Go beyond simply learning how to use libraries and learn how to cook real-world e-commerce data through the data science process. Starting from development environment setup and Numpy/Pandas basics to data preprocessing, statistical analysis, visualization, and business scenario-based RFM segmentation and hypothesis testing! Systematically master the core competencies of a data scientist through practical projects.

Creating Your Own Data Analysis Portfolio with Free Gemini CLI Vibe Coding: Data Collection, Analysis, Reporting, and Dashboard Automation Even if you are new to data collection, you can collect, analyze, visualize, and create reports and dashboards like a professional data analyst once you master the principles, prompt engineering, and VSCode. From Python data collection to report writing with Gemini CLI and VSCode: Learn prompt engineering and troubleshooting methods for foolproof data collection and analysis.

"SQLD is an exam where past questions are not officially released." In preparation for the upcoming 61st exam (May 31, 2026)— Practical materials featuring 50 questions per subject and chapter, vividly reconstructed by directly recalling the latest 60th exam!

Build your own powerful stock price analysis model that analyzes over 40 types of economic indicators and stock price data using AI to predict not only the S&P 500 and QQQ ETFs but also individual stocks!

This course is for both beginners who have never been exposed to programming before and those who have some theoretical knowledge about Python. The main purpose of the course is to create interest and enjoyment in Python based on real projects. I hope that through this course, you will gain more ideas and have more fun with Python.

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!

Through detailed lectures and hands-on practice on the core elements of SQL, we will provide you with a solid foundation to grow into a SQL analysis expert.

Learn how to design and handle large-scale data architectures that process hundreds of billions of records using only pure SQL and core MySQL features, alongside a Toss developer who handles hundreds of billions of financial traffic transactions and a developer who started as a non-major and is now developing platforms in Pangyo.

Learn the fundamentals of SQL and databases essential for practical work. Not only developers, but also planners and data analysts can learn SQL in depth.

Solve the challenges you inevitably face in real-world design—such as common codes, category hierarchies, history tracking, soft deletes, statistics, dynamic design, and JSON utilization—with 9 core patterns. This is not just simple theory; it contains practical know-how and selection criteria for various scenarios, proven across numerous projects. Now, become a true design expert who can confidently handle any requirement that comes your way.

You will systematically learn database design through three stages—conceptual, logical, and physical—and strengthen your skills through a practical shopping mall project. Gain real-world know-how through modern design techniques and the latest trends.

In this class, you will learn about the principles and methods of analyzing and processing various types of financial data using Python's Pandas library, apply them to situations you may encounter in the real world, and ultimately learn how to implement backtesting based on financial statement data (based on Kang Hwan-guk's book, "You Can Do Quant Investment"). As a result, you can break away from being a "passive investor" who simply follows what others say without verifying or basing the investment logic, and become a "self-directed and active investor" who can freely extract various elements necessary for strategy implementation from data and quantitatively analyze them using Python and Pandas.

"From the basics to 7 domain projects, completing the A to Z of deep learning (41 lectures in total)." Go beyond simply calling models and learn how to build clean, efficient deep learning pipelines using PyTorch Lightning, the current industry trend. Grow into a confident AI engineer capable of handling any data by directly implementing 7 projects, ranging from stock price prediction to generative AI, medical imaging, and sound analysis.