If you just learn how to write code

You can never analyze data.

Edit Image Info

The most important thing in data analysis is not “coding,” but “thinking about analyzing data.”

You need to know how to design a process for how to look at data from a perspective and how to analyze that data. Statistics is the most essential concept in the process of “discovering insights,” where tens or millions of pieces of accumulated data are tied together with similar ones, found anomalies, and read trends.

Statistics inseparable from analysis,

Why should we learn statistics?

Edit Image Info

Are the above average values significant? When calculating statistics or visualizing data, it is impossible to find meaningful insights by randomly analyzing them without standards.

We 'do data analysis! ' Here are the first things you might think about: 1) How is the data distributed? 2) What is the relationship between the two data? 3) How can these be expressed by visualizing them? etc.

These are the processes of exploratory data analysis, and they are all topics covered in statistics. In other words, if you don't know the concept of statistics, you can only “lick” data analysis. Incorrect statistics, or analyses without statistics, are simply “guesses based on data.”

How to learn “statistics”

Should I start learning math again?

클래스 수업 내용_1

클래스 수업 내용_2

▶ Class lesson content_1, 2

This class is not intended to “memorize” statistical formulas and theories. You can learn basic statistical concepts necessary for data analysis from a practical perspective so that you can immediately use them in practice. Starting with the most basic and basic average, standard deviation, and variance, we will examine statistical techniques such as distribution/correlation analysis/hypothesis testing based on actual cases.

You don't just learn statistics, nor do you learn statistics very quickly and shallowly. Learn how to quickly and easily analyze data while building a basic statistical foundation by omitting all complicated and difficult theories and learning statistical analysis according to practical procedures.

It is said that companies must use it

4 steps of the statistical analysis procedure

DDA 묘사적 데이터 분석 /EDA 탐색적 데이터 분석 /CDA 확층적 데이터 분석 /PDA 예측정 데이터 분석

▶ DDA descriptive data analysis /EDA exploratory data analysis /CDA in-depth data analysis /PDA predictive data analysis

Learn by following data analysis procedures based on enterprise problem solving methodologies. We will help you learn more quickly by working on projects with practical data!

1단계 데이터를 각져오면 무엇을 해야 할까?

▶ What should I do if I have collected level 1 data?

[Step 1: Descriptive Data Analysis - DDA, Descriptive Data Analysis]

Identify business issues and check the data we have right now. You can establish business KPIs by setting the main indicators to look for in the data.

2단계 데이터를 파악할 수 있는 필수 요소, 시각화!

▶ Visualization, an essential element for understanding two-level data!

[Step 2: Exploratory Data Analysis - EDA, Exploratory Data Analysis]

Use data visualizations to identify trends for key metrics. By understanding the state of the data, we can fully understand the data we have.

3단계 객관적으로 인사이트를 찾자!

▶ Step 3 Let's find insights objectively!

[Step 3: Confirmatory Data Analysis - CDA, Confirmatory Data Analysis]

Test hypotheses based on statistics rather than “guesses.” We derive business insights based on objective facts.

4단계 다음 일어날 일을 예측하자!

▶ Step 4 Let's predict what will happen next!

[Step 4: Predictive Data Analysis - PDA, Predictive Data Analysis]

Let's use current data to predict what will happen in the future. You can predict future situations by learning the concepts of regression analysis, modeling, and systematization.

What is the completion of data analysis

It's about “objective persuasion.”

Edit Image Info

The 'core' of data analysis is to discover insights from data, but the 'completion' of data analysis lies in clearly communicating the discovered insights. Therefore, it is also very important for data analysts to express the analyzed results well.

In this class, in addition to looking at statistics, I will introduce “how to write a data analysis report” to more effectively convey the insights I have discovered.

As one class

Pack them all!

Learn statistical analysis procedures used in practice and practice using actual data.
You can learn easily without formulas by excluding difficult statistical terms and complex formulas.
You will learn statistical analysis methods according to practical procedures.
You can learn how to write a data analysis report in practice.

Ability to analyze data

It will be your sure-fire weapon.

수강생 후기

▶ Student reviews

Learn everything from 1) how to analyze data according to the enterprise problem solving procedure using actual enterprise data in one class, and 2) how to clearly organize the analysis results!

If you want to learn Python properly,

Let's finish it all in one package!

Edit Image Info

This class is for people who know basic Python syntax and have used Pandas libraries. If you buy [Basic Python] and [Practical Python] together, you can take three classes at once at a 41% discount compared to the regular price. Learn all in one all-in-one data analysis and statistics by building a solid Python foundation with the benefits of a cheaper package.

Python basic classes that are better to listen to

We've collected only basic Python knowledge essential for data analysts. This class is recommended for those who aren't confident in Python yet, or if they want to take this opportunity to test their skills.

Python project starting with working data

Learn intensively about the Pandas library, which is the foundation and core of Python. This is a class where you can learn practical data analysis the fastest by working on an analysis project using data that can be easily encountered in practice.

Python business data analysis and statistics for solving enterprise problems

Python business data analysis and statistics for solving enterprise problems