Data Analysis and IT Security Using Python

You will learn techniques that can be applied in various fields of IT security, starting from the introduction to Python programming.

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.

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.

It would be efficient to automate IT security tasks using Python, wouldn't it? This course follows hands-on practice, applying Python and web Flask development to automate tasks, idea by idea.

Leveraging Python's basic syntax and popular libraries Auto-generating Python syntax and library code using ChatGPT

We will introduce the types of big data analysis techniques and Python libraries for analysis, and the basics of statistical analysis techniques and machine learning analysis techniques.

Let's build a solid foundation in Python and solve data analysis problems!

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.

"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.

This course aims to acquire Python-based web crawling technology to collect data from web pages and improve data collection and analysis capabilities at work based on this.

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.

Build data-driven investment strategies with Python quant investing and put them into practice. You can implement various asset allocation strategies and ultimately create your own unique investment strategy.

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

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

Course Title: Big Data Analysis Engineer Practical Exam Pass Codebook This is a practical, certification-preparation course for candidates preparing for the Big Data Analysis Engineer practical exam. To reduce the burden of having to study extensive theory and content that is not directly necessary for the exam, this course focuses only on the key concepts and code needed to solve actual exam problems. The course consists of 23 lessons and covers everything step by step, from Python basics to Task Type 1 (data processing), Task Type 2 (machine learning), and Task Type 3 (statistical analysis). Rather than simply presenting completed code, the course explains the role of each section of code and why it is written, enabling students to follow along directly and develop their problem-solving skills. Main Course Content - Essential Python and pandas fundamentals required for the exam - Task Type 1 data processing and practical problem solving - Task Type 2 data preprocessing, machine learning modeling, and submission of prediction results - Task Type 3 statistical tests and regression analysis - Practical problem solving and code review for each session - How to use the exam practice environment and final review immediately before the exam Learning Materials Provided - An e-book summarizing key concepts and solution code - Python practice files for hands-on learning - Problems and explanations for each session - Final review materials for before the exam The primary goal of this course is to help students avoid wasting time studying unnecessary material and enable them to write code independently and solve problems at the exam site. The course is structured so that even candidates with limited Python fundamentals can learn in order, allowing them to prepare for everything from learning essential code to solving practical problems in one course.

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.

This course is designed for practitioners who are just starting infrastructure security audits or are consuming significant resources on manual inspections. It's a hands-on process where we build a fully automated audit system together that automates inspection items based on Electronic Financial Supervisory Regulations, performs batch inspections of dozens to hundreds of servers through Ansible, and visualizes results using Django with filtering and Excel download capabilities.

Based on the instructor’s experience of struggling when first learning machine learning, this course has been newly designed—unlike conventional courses—to help you understand machine learning easily and apply it to real-world problems.

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 system collapse in a falling market?" I, too, spent a great deal of time grappling with the same question. Most automated trading strategies on the market may appear to generate profits during bull markets, but the moment market conditions change, they lose their ability to adapt. To solve this problem, I combined the expertise I developed through two years of hands-on research with the latest AI technology. In this course, you will learn how to design a 'Transformer-based deep learning model' that simultaneously reads macroeconomic trends and the movements of individual assets, and how to automatically train and deploy it in a free GPU environment. In particular, by introducing sophisticated data normalization techniques on par with those used by hedge funds, you will build an 'intelligent comprehensive scoring system' that goes beyond simple indicators to incorporate news sentiment. On top of that, an ‘AI safety gate’ infused with the insight of a trader with 20 years of Wall Street experience adds a final layer of conviction from the perspective of a human trader just before a purchase. Going beyond simple buying and selling, you will implement the entire process of an institutional-grade algorithmic system yourself—including dynamic take-profit and stop-loss logic, eight self-recovery mechanisms, and real-time Slack reports.