Convergence of AI and Big Data: A New Paradigm for Data Analysis

This course uses Python, a programming language well suited for big data analysis, to directly collect, process, and analyze social big data from the web, social media, and other sources. Python programming exercises are conducted in a step-by-step, follow-along format, making it easy for humanities and social science researchers with no programming experience to learn. By completing this course, you will be able to master Python, collect data, and analyze big data.

Even without coding experience, you can create a real-world work automation program yourself with Cursor AI. This course is a hands-on project in which you use Python and Cursor AI to automatically collect news comments (Crawling), save them as a CSV file, and analyze public opinion and sentiment, creating a complete project from start to finish. This is not a course where you simply type along with the code. It is designed to let you experience the entire real-world development process, including how to ask Cursor AI effective questions, setting up the development environment, writing automation programs, and troubleshooting errors. By following the course, you will learn step by step—from installing Python and configuring Cursor AI to the basics of work automation, scraping news comments, saving data as a CSV file, performing sentiment analysis, and fixing errors that arise in actual projects. Even if you are new to programming, you will naturally learn how to develop alongside AI, automate repetitive tasks, and build practical skills for using data.

This is a lecture for non-majors or high school students who are studying data science and artificial intelligence for the first time. We will teach you the concepts of artificial intelligence and machine learning and what efforts you need to make to get a related job.

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

From evaluating analysis models to interpreting and utilizing results, Big Data Analysis that anyone can use smartly, perfectly, cleanly, and with style!

From the design of analysis models to advanced analysis techniques, a big data analysis certification that even non-majors can start right away!

* 100% Practical Curriculum: Focused on hands-on data handling rather than grammar-based learning * Essential Machine Learning Prep: A complete summary of data preprocessing techniques required before AI training * Data Analysis Tool Mastery: Conquer core functions of Numpy (numerical operations) and Pandas (data manipulation) * Deriving Visualization Insights: Transferring data visualization skills using Matplotlib and Seaborn * Building Statistical Foundations: Learning basic statistics and group analysis techniques to identify data characteristics

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

"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.

🧩 Rather than focusing on complex formulas or theory-centric explanations, the goal is to learn the core concepts of recommendation systems by directly implementing programs. 🛠️ Through a total of 12 diverse and practical examples, we have progressively designed recommendation systems that can be utilized in real-world environments, including content-based recommendation, collaborative filtering, and deep learning recommendation.

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.

Let's analyze and visualize data using Orange3 and explore machine learning concepts!

This lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.

"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.

On your way to work tomorrow morning, your phone will have “Today’s stocks selected by the system and the reasons why” waiting for you. An AI-powered Korean stock auto-trading system that reads foreign and institutional buying trends and handles everything from purchasing to taking profits and cutting losses automatically—you build it yourself, making it truly yours, rather than relying on a program made by someone else.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.

Revolutionize your stock investing with AI! In this course, you will directly build a functional AI automated trading system targeting the top 25 Nasdaq stocks. We provide a special opportunity to gain experience and know-how through mock trading using the Korea Investment & Securities API, and then transition to AI automated trading with a real account. Even stock market beginners will have no problem. We explain basic stock concepts and the reasons why you should invest in US stocks in an easy-to-understand way, while providing all source codes and even remote support. From now on, investing is essential for the next 60 years of our lives. Create your own AI investment partner to join you on this journey!

Learn a systematic approach to S&P 500 pair trading with Python. Lay the foundation for an investment strategy that excludes emotions through professional data analysis.