[2025 Revised Edition] Big Data Analysis Engineer Practical Exam (with Python)

Given the need for data analysis experts, this course is designed to obtain the nationally certified 'Data Analysis Associate Professional' qualification.

The Big Data Analyst Engineer exam is a national certification held twice yearly. For those preparing, we extensively covered core topics from the basics. With each session, exam difficulty rises. To this end, we provided essential content from basics to boost passing rates.

This is a course on expected questions & latest reconstructed past exam problems for those wishing to obtain the National Certified Data Analysis Associate Professional Certificate (ADsP).

This is a course aimed at obtaining the nationally recognized SQLD certification, and SQLD refers to developers who can write accurate SQL for manipulating and extracting data while developing application software based on database and data modeling knowledge.

If you have learned the Python language, this course will help you improve your Python skills and obtain a certificate through solving Cos Pro Level 2 problems, which will allow you to acquire simple problem-solving skills, analyze output values for input, and analyze requirements.

We have included only the essential content so that even non-majors can learn Python easily and enjoyably. We will proceed with detailed lectures and challenge exercises so that beginners can properly learn the ‘essential content.’

This course is for those preparing for the CosPro Level 1 exam, covering the COS Pro Level 1 sample problems released by the exam organizer, YBM Net Co., Ltd.

The Big Data Analyst Engineer exam is a national certification held twice yearly. For those preparing, we extensively covered core topics from the basics. With each session, exam difficulty rises. To this end, we provided essential content from basics to boost passing rates.

SQLD Yellow Book 176 Problem Solving Lecture for You Who Studied but Cannot Solve Problems. After Completing the Course, You Will Become an Expert Beyond Passing. SQLD All-in-One Pass!

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

A complete roadmap for obtaining the Big Data Analysis Engineer certification! We provide a systematic learning process for passing the exam, from data analysis beginners to experts.

Many applicants want to solve past questions. Why is that? Because the question setter refers to past questions. Solving the latest past questions is the shortcut to success.

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

Repetitive pipeline management in Data Warehouses (DW), now free yourself from that painful cycle with dbt! Building on the efficiency provided by dbt, become a data analytics engineer who creates higher value through business-centric data modeling, effective data lifecycle management, and more.

This lecture aims to systematically learn the essential core concepts and functions absolutely necessary for the Business Information Visualization Practical Exam (Tableau), based on the exam guidelines of the organizing institution. It is structured around practical exercises so that you can freely use the functions frequently tested in the actual exam, and through solving mock exam problems of types A and B, you can improve your understanding of the exam types and your problem-solving skills together. Lecture plan and materials can be checked at https://github.com/hansikyung/Tableau_OneTime.

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

This course is a course that practices the basics of machine learning mathematics and statistics using Python. Machine learning and deep learning require programming skills, but they also require a mathematical background to understand the principles. Through this course, you can gradually learn the basic mathematics required for artificial intelligence.

"Big Data + AI Full Stack Development" - The process of building big data that breathes life into AI is carried out in the form of a pilot project. We identify requirements, configure the platform step by step, and perform smart car data exploration and analysis.

Revolutionize your stock investments with AI! In this lecture, you will directly build a real, working AI automated trading system targeting the top 25 NASDAQ stocks. We provide a special opportunity to gain experience and know-how through simulated investments with the 한국투자증권 API, and to perform AI stock automated trading with a real account. Even stock beginners will have no problem. We will easily explain basic stock concepts and why you should invest in US stocks, and provide all source code and remote support. From now on, investment is essential for the next 60 years of our lives. Create your own AI investment partner that will be with you!

SSIS, SSAS, MachineLearning, BI, ETL. You can learn important technologies that cannot be found in domestic books, YouTube, lectures, blogs, and academies. I also recommend it to those who are interested in employment at domestic large companies, American large companies, and American state funding agencies.

Students who want to learn Hadoop and Big Data will celebrate the amazing advancements in experiencing the world of Big Data through this course!

It covers basic data concepts, useful R functions and packages, and data analysis exercises, to allow anyone to perform big data analysis using R programming.

Understand the core of big data business and provide insights on how to utilize it in marketing strategies in the field.

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.

Do you want to get a job as a data analyst? Based on my experience, let's take a look at the tasks for each data role in real companies.

This is a practical project lecture that collects data and analyzes big data using Arduino and wind sensors.

This course covers how to leverage AI in the food and pharmaceutical manufacturing industry to enhance productivity and quality. Through real-world case studies, you'll learn how AI is applied in key stages such as raw material management, production processes, quality inspection, and regulatory compliance, while understanding defect rate reduction and efficient operational strategies. Through this course, learners will master ▲characteristics of the food and pharmaceutical industry and the necessity of AI adoption ▲data-driven quality management ▲predictive maintenance and equipment optimization ▲regulatory compliance monitoring and risk management ▲analysis of domestic and international implementation cases, gaining strategies and insights that can be immediately applied in the field.