Master the Big Data Analysis Engineer Practical Exam with Past Questions [12 Additional Sessions. All Sessions Included · Continuously Updated · Dedicated Practice Environment Provided]

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.

If you invest 3 hours a day for 6 days, you can learn the basics of deep learning using PyTorch. So, shall we get started?

1. Mock exam problem-solving for passing AICE Associate, Korea's only state-certified AI certification. 2. A course featuring 12 mock exam sessions with the same question types as the actual AICE Associate exam.

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

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.

🔥Don’t have time to complete the 32-hour all-in-one course? We’ve condensed the four subjects of the Big Data Analysis Engineer written exam into around 4 hours. Connect concepts directly to actual exam questions through modified past exam questions, and receive 150 pages of lecture materials plus 13 pages of final review notes for the exam room.

Master the Big Data Analysis Certification—an essential credential that is by no means easy to self-study—in a short period using R, an easy-to-learn data analysis tool!

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.

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

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.

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 course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is necessary to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence.

1. Python coding study to obtain the AI certification, AICE Associate 2. Step-by-step Python coding study from basics to applications 3. Lectures by Professor Joongheon Kim of Korea University, a scholar in the field of AI

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students will build their own chatbot using the GPT API and deploy it on the web using Streamlit. By experiencing everything from the basics to hands-on practice, you can build a solid foundation for your first steps in utilizing AI.

Through this course, you will learn about SQL and databases, SQL syntax, database design for practical use, how to use databases through programming, and real-world applications of data analysis.

I want to create an algorithm trading bot that makes unlimited money and run it automatically. Where should I start?

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.

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.

This is a Python basics course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python

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.