Big Data Analysis Engineer Practical Exam Pass Codebook

We will help those preparing for the Big Data Analysis Engineer Practical Exam with R to prepare for the exam. We will select and inform you of only the essential parts based on the questions that were on the Big Data Analysis Engineer Practical Exam. If you complete the course, you will be one step closer to passing the Big Data Analysis Engineer Practical Exam. We have reviewed the recent exam questions and uploaded related scripts and data. Also check out the latest exam information.

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.

From data preprocessing to understanding statistical techniques, start your certification prep right away even without professional knowledge!

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.

From understanding big data to actual analysis planning, start preparing for your certification right away with systematic training!

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.

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

This is a lecture on the National Technical Qualification Big Data Analysis Technician Practical with Python. We hope you all pass!

This course is designed to prepare you for the Information Processing Engineer practical exam solely through solving previous exam questions. Instead of unnecessary theories or vast amounts of content, it focuses on past exam questions that are repeatedly featured, allowing you to connect your learning directly to passing the exam. You can quickly grasp core points and question patterns by reflecting on the latest trends, and even learn tips for writing answers through the problem-solving process. This lecture is optimized for examinees aiming to pass efficiently in a short period of time.

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.

1. AI practice using an AutoML platform that performs data analysis and AI modeling without coding 2. Carrying out the entire process from data exploration, visualization, and preprocessing for data analysis to AI modeling 3. This course is optimized for passing AICE Basic, an AI certification organized by KT

This course was created based on my personal experience passing the very first revised Information Processing Engineer practical exam, which had a pass rate of only 5.34%. The Information Processing Engineer practical exam can no longer be prepared for through simple memorization alone. In particular, the ability to read the execution flow of C, Java, Python, and SQL code to track results, as well as the ability to accurately distinguish short-answer concepts and write them as answers, has become crucial. This course is structured to help even beginners who are new to programming follow along step-by-step, covering everything from creating variable tables to tracking C, Java, Python, and SQL code, core practical terminology, and written exam subjects 1 through 5. Rather than being a video-centered course, this is a text-based course designed for fast reading and repeated review. It makes it easy to look up necessary parts again and focuses on practicing how to derive answers at the testing center by following the code and explanations directly. The 1st update covers the practical programming sections: C, Java, Python, and SQL. The 2nd update will organize core terms and concepts required for the short-answer practical questions and provide a "Stepper Simulator" to practice code tracking directly. The 3rd update will sequentially expand to cover written exam subjects 1 through 5. The Stepper Simulator is designed so that you don't just stop at reading explanations; you can understand how variable values change as the code executes line by line by following along yourself. If you have any questions while taking the course, please leave them in the Q&A. I will respond as quickly as possible to ensure your learning doesn't get stuck.

* 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

Let's collect and refine data through public data, web scraping, and more, and perform exploratory data analysis!

Pass the Information Processing Engineer practical exam in one go! We pinpoint only the core code and theory for you. Master past exam questions perfectly! Open the door to passing with our systematic lectures. 🏆 2024 Inflearn Rookie Award 🏆 2025 Students' Pick Award

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

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.

Learn about the latest technology trends and practical insights through presentations by leaders of the Python ecosystem, including NumPy creator Travis Oliphant, Polars creator Ritchie Vink, Python Software Foundation leaders, and Django core contributors. We will also explore the future of Python in the AI era and the role of the community.

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

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