[What's DS Doing - Part 2] Data Analysis with Informed Coding

: "From environment setup to practical business projects, a data science journey to find answers for e-commerce sales through statistics (38 lectures in total)" Go beyond simply learning how to use analysis tools and learn how to process log data from actual e-commerce fields using statistical processes. After building a solid foundation in core theories—from setting up the development environment to descriptive statistics, probability distributions, and hypothesis testing—you will move on to comparative analysis of actual user app dwell time and purchase conversion rates, and even quantify performance contribution through multiple linear regression analysis! Systematically master the core data scientist competencies of 'data-driven decision-making and analysis automation' through practical projects.

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.

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.

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.

Learn why and how to handle data exploration (EDA), data cleaning, scaling, outlier handling, log transformation, and categorical encoding in real-world applications. You will also learn how to merge tabular data and process (unstructured) time series data.

With the advent of the AI era, building data pipelines has become a core competency that determines a company's competitiveness. Learn how to build efficient data pipelines using Airflow, the most widely used tool, directly from a Silicon Valley expert (formerly Head of the Data Team at Udemy, currently a professor in the Data Masters program at San Jose State University) with practical experience and extensive lecturing experience.

Learn the core functions of SPSS in a short period to pass the Social Survey Analyst Level 2 practical exam. Master SPSS know-how along with problem-solving techniques—ranging from data entry to descriptive statistics, cross-tabulation, t-tests, ANOVA, and correlation/regression analysis—to perfectly prepare for the practical exam.

In the era of AI and Big Data, are you utilizing your organization's data effectively? This course builds essential capabilities for reading and interpreting data, covering everything from the foundations of statistical thinking to the core principles of machine learning and deep learning. Grow into a practical leader capable of discovering patterns amidst volatility and making data-driven decisions.

You will learn Apache Airflow, the most widely used Orchestrator for creating software data pipelines.

In an era where flexible data processing beyond the limits of SQL is required, learn everything about MongoDB. From NoSQL concepts to installation, CRUD practice, aggregation pipelines, text search, and indexing, you will learn step-by-step and complete your practical skills through Python crawling integration and container environment construction.

Learn NoSQL technology for handling big data, one of the fundamental technologies used in the full-stack and data science fields at modern startups. MongoDB is one of the easiest NoSQL technologies to learn and use quickly. In this course, you will learn the basics of MongoDB in a short time, as well as how to work with and utilize MongoDB using Python.

"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.

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.

This course covers everything from core recommendation system algorithms to practical implementation. - Content-based filtering - Collaborative filtering and deep learning-based recommendation model implementation - Two-step recommender systems implementation - Hands-on practice using PyTorch/RecBole - Industry know-how and recommendation result visualization

This course is not just about "how to implement" a Transformer, but about dissecting why this architecture was created, what role each module plays, and how the entire model works from the designer's perspective. We deeply analyze the internal computation principles of Self-Attention and Multi-Head Attention, and directly verify through formulas, papers, and implementation code what limitations Positional Encoding, Feed-Forward Networks, and Encoder·Decoder structures were introduced to solve. Starting from Attention, we assemble the entire Transformer structure ourselves, and actually perform training to experience firsthand how the model operates. This course is the most structured and practical roadmap for "anyone who wants to completely understand Transformers."

"Data analysis—don't stay trapped in complex Python syntax and Excel functions anymore." This course is a practical masterclass that automates the entire process of data processing, from Excel and Google Sheets to Python-linked analysis, by hiring the latest AI agents as your own dedicated data analysts. Instead of difficult coding knowledge, we introduce the latest 'Vibe Coding' method—commanding as if having a conversation in natural language—allowing even non-majors to enter the world of data analysis without any barriers. Through comprehensive hands-on practice using actual business data—covering everything from data preprocessing and visualization to report writing—experience a powerful AI workflow that you can apply to your work starting tomorrow. A lecture utilizing AI agents, Excel data analysis, chatGPT, Gemini, (Python), Genspark, Google Colab, chatbots, Google Antigravity, and notebookLM.

Creating your own data analysis portfolio with Google Antigravity CLI harness engineering and vibe coding: data collection, analysis, reporting, dashboard creation, and task automation. Even if you are new to data collection, once you master the principles, prompt engineering, and how to use Antigravity, you can perform data collection, analysis, visualization, reporting, and dashboard creation just like a data analyst. From Python data collection to report writing learned with Antigravity IDE and CLI. Prompt writing and error resolution methods for foolproof data collection and analysis.

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.

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.

This is a basic Python Excel programming course learned through an xlsxwriter tutorial. You can analyze data and automate tasks using Python and Excel.