[Big Data Analysis Engineer] Learning Association Rule Mining with R: The Apriori Algorithm and Practical Market Basket Analysis

Fully prepare for the Goorm environment—the biggest challenge of the Big Data Analysis Engineer practical exam—on Algorithun’s practice platform. Repeatedly practice past Type 1, 2, and 3 problems in the exact same way as the actual exam environment, developing real exam-day familiarity 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.

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.

From evaluating analysis models to interpreting and utilizing results, Big Data Analysis that anyone can use smartly, perfectly, cleanly, and with style!

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

This is a lecture titled "Data Analysis Based on Practical Examples" prepared for data analysts.

Course Title: Big Data Analysis Engineer Practical Exam Pass Codebook This is a practical, certification-preparation course for candidates preparing for the Big Data Analysis Engineer practical exam. To reduce the burden of having to study extensive theory and content that is not directly necessary for the exam, this course focuses only on the key concepts and code needed to solve actual exam problems. The course consists of 23 lessons and covers everything step by step, from Python basics to Task Type 1 (data processing), Task Type 2 (machine learning), and Task Type 3 (statistical analysis). Rather than simply presenting completed code, the course explains the role of each section of code and why it is written, enabling students to follow along directly and develop their problem-solving skills. Main Course Content - Essential Python and pandas fundamentals required for the exam - Task Type 1 data processing and practical problem solving - Task Type 2 data preprocessing, machine learning modeling, and submission of prediction results - Task Type 3 statistical tests and regression analysis - Practical problem solving and code review for each session - How to use the exam practice environment and final review immediately before the exam Learning Materials Provided - An e-book summarizing key concepts and solution code - Python practice files for hands-on learning - Problems and explanations for each session - Final review materials for before the exam The primary goal of this course is to help students avoid wasting time studying unnecessary material and enable them to write code independently and solve problems at the exam site. The course is structured so that even candidates with limited Python fundamentals can learn in order, allowing them to prepare for everything from learning essential code to solving practical problems in one course.

The easiest explanation of R-based data visualization📊. (Almost) everything you need to know😎. At the end, we will plot COVID-19 💉vaccine vaccination data on a map🗺️!

🔥Don’t have time to complete the 32-hour all-in-one course? We’ve condensed the four subjects of the Big Data Analysis Engineer written exam into around 4 hours. Connect concepts directly to actual exam questions through modified past exam questions, and receive 150 pages of lecture materials plus 13 pages of final review notes for the exam room.

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.

This is an advanced course where you will directly implement and interpret practical statistical techniques such as sampling distributions, hypothesis testing, regression analysis, and logistic regression using R. By utilizing AI tutors like ChatGPT and Gemini, you will learn everything from code generation to interpreting analysis results and writing reports, allowing you to take your data analysis capabilities to the next level.

We cover frequently used functions for R programming, how to write code efficiently, and intermediate techniques.

Design and create your own student-customized AI Tutors and AI Avatars using generative AI such as ChatGPT and Gemini along with Google Colab. From prompt engineering and data analysis to building an AI Career Exploration Platform for career and academic counseling, you will complete practical projects that can be immediately applied in the classroom.

Understand the core principles of the DBSCAN algorithm for discovering clusters of complex shapes and learn how to apply it to real-world data using R. Develop your density-based clustering skills through hands-on practice covering the concepts of core points, border points, and noise points, as well as parameter tuning, visualization, and result interpretation.

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.

We are going on a long journey into the unknown world of deep learning and artificial intelligence. Let's leave complex formulas and coding behind for a moment and set off with the wings of R and Keras.

Understand the essence of data visualization as a balance between scientific accuracy and aesthetic expression, and systematically learn everything from color and symbology principles to graph design. By utilizing visual attributes based on cognitive science principles, you will complete your data communication skills to achieve reliability and persuasiveness in professional practice.

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!