Python library for data analysis and EDA

This is a lecture on how to use NumPy and practice its application in real-world scenarios.

Using pandas, we will learn data processing patterns that frequently appear in real-world situations and analyze the processed data.

This course is for those who have learned the basic grammar of Python and will learn the core skills used in text data analysis. I hope that those who are interested in text analysis will gain more ideas through this course and use it for a long time.

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.

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.

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.

A course to understand and practice machine learning for efficiently conducting complex analysis and predictions! Even machine learning, which may seem difficult and complex, can be easy and fun to master with Masocampus's <Python Machine Learning>.

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.

Prepare perfectly for the Goorm environment—the biggest challenge of the Big Data Analysis Engineer practical exam—on Algorun’s practice platform. Repeatedly practice past Type 1, 2, and 3 problems in the same way as the actual exam environment, developing real exam-day proficiency rather than memorizing code. This is a hands-on, practice-focused course designed to train you to work instinctively even in an environment different from Colab or Jupyter.

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.

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.

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.

Let's learn how to collect data from the web and create a supervised learning model for machine learning!

The Intermediate Python course is designed to deepen your understanding of Python by covering advanced topics such as functional programming, metaprogramming, exception handling, and object-oriented programming.

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.