Data Analysis (Power Query + Tableau + Dashboard Planning) Core 4 Hours

This course is designed for all learners, from those just starting with data analysis to those looking to apply it in practical work, to systematically learn the entire process of data processing and visualization using R. First, in the basic setup stage, we start with installing the development environment and learn how to create DataFrames and Series, building a solid foundation in the fundamentals needed for R programming. Next, we move on to learning core functions, focusing on hands-on practice with the most frequently used features in data analysis such as indexing, slicing, sorting, filtering, and handling missing values. After building the foundation, we expand to intermediate functions, intensively covering features essential for actual data analysis work such as date and time data processing, groupby operations, data merging (merge, concat), and function application (apply, lambda). Through this, you can develop the ability to handle complex data structures beyond simple data manipulation. Finally, in the visualization section, we cover various visualization techniques including line graphs, histograms, box plots, and time series graphs to learn how to express data more intuitively. Through visualization, you can also acquire the ability to effectively communicate analysis results. In particular, this course is structured not to stop at theoretical learning, but to allow immediate application of learned content through various examples and step-by-step practice. Therefore, students can naturally develop practical skills just by following the course and further secure data analysis capabilities that can be applied to actual projects.

From understanding to the practical application of time series data analysis and association analysis! Complete it easily and quickly with Orange, the no-code AI analysis tool!

"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.

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.

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

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.

This course covers practical strategies for leveraging AI in the machinery and metal processing industry to enhance productivity and quality. It analyzes key process data from CNC, welding, cutting, and other operations, explaining how AI technology is applied to reduce defect rates, enable predictive maintenance, and manage worker safety through real-world case studies. Learners will understand AI-based processing optimization methods that can be immediately implemented in the field, from small and medium enterprises to large corporations, and gain insights to realize manufacturing innovation in global competition.

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!

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

We are now in an era where data speaks louder than intuition. Learn data analysis easily with ChatGPT!

The chemical and fine chemical manufacturing industry is a sector that requires complex processes and high safety standards. This course covers strategies for leveraging AI to increase productivity, reduce costs, and enhance safety. You can systematically learn practical skills centered on AI application cases that can be immediately applied in actual field settings, including process data analysis, quality prediction, equipment predictive maintenance, and environmental regulation compliance.

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.

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

This is a special lecture that can lay the theoretical foundation to simultaneously prepare for the Microsoft AZ-900 certification, and as the latest content reflecting the scope of questions as of May 2025, by providing content related to core data concepts, Azure's relational data, Azure's non-relational data, and Azure's analytical workloads in a form combining theory and practice, it can be utilized as a meaningful educational opportunity to not only obtain the certification but also take the first step towards becoming a data professional.

"Smart Factory—it sounds cool, but can't quite picture it in your head?" The news talks about the 4th Industrial Revolution and AI factories every day, but when you actually look for information, it's full of "alien languages" like PLC, MES, ERP, and Edge Computing. What non-majors need isn't a complex circuit diagram, but a 'map' that shows how the whole system works. This course strips away difficult engineering jargon and explains everything about smart factories through intuitive illustrations and analogies that anyone can understand.

We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

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.

Here’s an example of the synergy that can be created when combining Tableau Desktop and Server/Online. Let’s build a pipeline together to see how data can lead to concrete actions!

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.

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.