Big Data-Based Policy Evaluation Theory

Big data is being developed by integrating with various business areas. In particular, big data is useful for identifying the latest business trends. Therefore, after studying big data application cases in marketing, finance, and healthcare, we aim to learn how to extract data patterns through a technical understanding of big data platform construction and analysis techniques, and apply them to business. **Business environment analysis and new business creation through big data!** This is the beginning of the concerns for all professionals seeking new business environments in the era of the 4th Industrial Revolution.

A current professional data analysis instructor at a large company who passed the Big Data Analysis Writer and Practical exams in one go will help you prepare quickly by summarizing the key points.

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.

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!

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

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

We explain 32 data visualization techniques very quickly and easily from a non-expert's perspective!

Feeling overwhelmed by where to start with complex data analysis? I will clearly guide you through how to utilize R and AI Tutors using the practical know-how I've built up.

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

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.

We’ll guide non-majors and beginners to quickly earn the Big Data Analysis Engineer practical certification! Keep the theory light and the practical training solid. Even without complex background knowledge, you’ll focus on past exam questions and study only the key points that are sure to appear on the test.

It improves logical thinking skills, fosters the ability to understand how to use programming tools, and develops the capacity to design algorithms for problem-solving.

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

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 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.

- Practical Kaggle Competition Project: From Data Exploration to Submission - Beyond Tutorials: Follow a Real Kaggle Competition from Start to Finish Have you signed up for Kaggle and completed the Titanic tutorial, only to feel lost about where to start when facing a real competition? If you simply copy someone else’s notebook and submit it, you’ll eventually hit the wall of wondering, "Why isn’t my score improving?" In this course, we’ll work through an actual Kaggle competition from start to finish, covering data exploration, validation design, feature engineering, modeling, and submission. Instead of memorizing code, you’ll develop an intuitive understanding of the competition mindset: “What should I try and why, and how can I validate its effectiveness?” [One line about your competition participation, awards, or practical experience]

"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.

Let's use the R language, which processes data easily and conveniently, to help even non-computer science majors develop their skills in utilizing computer programs related to their own fields!

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

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