[Nuclear House] 2025 Big Data Analysis Article (Practical)_Type 1, 2, and 3

Passed the Big Data Analyst Practical Exam,
Anyone can challenge! 💯

Big Data Analysis Engineer Practical Test ,
So that you can prepare properly! 🔑

The written test is to gauge your knowledge, and the practical test is to check whether you can actually analyze big data .
So, it is important to know the know-how of a data scientist that is learned through hard work and typing in the field.

The examiner's eyes!
Former/current data experts
Practical lectures ✅

Test-taking strategies for types 1 and 2

Learning is centered around simple and easy models, but most importantly , writing actual code !

• Learning considering exams that do not provide code auto-completion
  ▶ Code practice using notepads such as Notepad
  ▶ Check how to use help
• Task 1: Focus on technical statistics and preprocessing
• Task 2: Learning in the order of classification → regression → clustering

Task 3 Exam Strategy

Statistical hypothesis testing theory + practical example solutions to conquer new types of questions that require scientific and technological calculation skills!

• Understanding the core theory through analysis of example problems of the Korea Data Industry Promotion Agency
  ▶ Hypothesis testing / statistical testing method
  ▶ T-test + essential example learning
  ▶ Analysis of Variance + Essential Example Learning
  ▶ Chi-square test + essential example learning
• After understanding the core theory, solve actual problems at the level of the test questions.

Efficiency Up! High-efficiency learning techniques

📜 Highest accuracy question restoration and in-depth analysis of past questions!

By analyzing past exam questions that were directly examined and restored by experts, we can clearly determine whether they can be included in future exams.
Bold selection and focus by excluding theories and fields with low probability of being tested

📜 Minimize the scope of the exam by determining the suitability of the test environment!

A test environment where problems that require access to the external environment cannot be tested, and the coolness of not even mentioning things that will not be tested!

📜 The know-how of experts at the examination committee level who can see through the examiner's eyes!

Big data analysis is not an easy task, but it provides practical theories and practical applications that have been acquired through many years of practical experience.
Presenting effective test-taking strategies that only experts in the field would know, including the level and limitations of the questions

📜 A surefire passing running mate that helps you improve your coding skills in the short term for the 1-2-3 type of tasks !

We propose learning focus through analysis of key frequently occurring coding types and strategies for avoiding negative scores for each of Type 1 and Type 2 tasks .
Clearly present the expected problem types of the new task -type 3 questions .
Coding simulation that shows the types of coding errors that learners who code alone frequently make and provides solution tips
Provided as a lecture

📜 Essential statistical theories that will be of great help if you know them to pass the practical exam!

Determining sample size and hypothesis testing, hypothesis testing for key parameters, inference about the difference between two populations
Basic physical strength required for practical tests that evaluate data analysis skills = Providing lectures on core statistical theories

Nuclear power plant big branch real-time, that's why it's more special !

[Nuclear House] Big Data Analysis Engineer (Practical) lecture textbook is the most complete practical textbook written by current data analysts .
* New book scheduled for publication in January 2025

Created this course
Introducing the knowledge sharer ✨

This course was developed through joint work between the representative instructors of [Nuclear House], the brand representing RMP's certification, and [Vermind], a content expert.

Lee Kyung-sook

• Work Type 1,2 Big Data Analysis Practice and Training
• Task 3 Statistical Hypothesis Testing and Practice
• Industrial Engineering Major
• Current) PricewaterhouseCoopers Consulting AI/ML consultant
• Former LG CNS Data Scientist
• Conducting analysis projects in various fields such as sales/marketing

Are you curious about the Big Data Analysis Engineer written lecture ?