Big data analysis of technology trends using patent data

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.

This patent guide is designed to legally protect the development achievements of universities, research institutes, and companies, and to secure exclusive technological competitiveness. Additionally, we provide efficient and competitive research and development directions through understanding intellectual property-based R&D trends and predicting technology gaps.

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.

This course is designed for those aiming to obtain the Data Architecture Semi-Professional (DAsP) certification. It provides a simple and systematic overview of essential exam topics, ranging from basic data architecture concepts to data modeling, data quality, and governance. The curriculum is structured to minimize complex theories and focus on frequently tested concepts, making it accessible for non-majors and beginners to learn without pressure. Build your fundamental skills in understanding data structurally while preparing for the DAsP certification.

Leveraging Python's basic syntax and popular libraries Auto-generating Python syntax and library code using ChatGPT

Let's understand and try policy evaluation with R and R Studio!

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.

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

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.

Creating Your Own Data Analysis Portfolio with Free Gemini CLI Vibe Coding: Data Collection, Analysis, Reporting, and Dashboard Automation Even if you are new to data collection, you can collect, analyze, visualize, and create reports and dashboards like a professional data analyst once you master the principles, prompt engineering, and VSCode. From Python data collection to report writing with Gemini CLI and VSCode: Learn prompt engineering and troubleshooting methods for foolproof data collection and analysis.

You can learn the skills and know-how needed to improve work efficiency by utilizing Open API and Python.

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

A course to understand and practice machine learning for efficiently conducting complex analysis and predictions! Even machine learning, which may seem difficult and complex, can be easy and fun to master with Masocampus's <Python Machine Learning>.

Through this course, you can learn SQL and database fundamentals, SQL syntax, database design for practical applications, database utilization through programming, and data analysis use cases.

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.

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

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

In Part 3 of Creating a Python Trading Room for Quant Investment, you will learn about portfolio theory and concepts necessary for real investment, as well as how to implement them with code. In Parts 1 & 2, you mainly learned about code implementation methods for analyzing financial data, and in Part 3, you will learn about 'portfolios', which are an important part of quantitative investment. With the question of why you should create a portfolio, you will learn portfolio theory, implement it in Python, and practice the code necessary for real portfolio investment. I hope this lecture will be helpful for your own portfolio investment.

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.

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