Coding-free AI data analysis using Orange - Lv.5 Clustering and dimension reduction

From understanding and utilizing time series data and correlation analysis, Orange's AI analysis tool makes it quick and easy!

Orange Data Analysis Course, which contains only the essential elements that can be applied immediately in real-world situations.

Don't be fooled into thinking that data analysis is difficult.
Easy-to-understand essential concepts and related techniques of unsupervised learning!

– How to easily utilize various data with Orange without coding
– Solid theory and practice on clustering and dimensionality reduction techniques
– Anyone can understand the visualization-based data analysis process.

What if you don't know how to view or handle your data right now?!
With this course, take the first step toward realizing data-driven decision-making.

Course Experience Group Reviews
Corporate lecture specialist, lectures created with know-how

Easy to use even for data analysis beginners

I'm a job seeker who tries to study data analysis little by little whenever I have time!

I was someone who didn't know how to code and was far from data analysis.
I also learned about a tool called Orange and I think I realized how to handle data.
This is my first time using it... I'll do my best.

-Review of the experience group (co******)

See the new possibilities of Orange

Although I have a background in computer science, I was hesitant to study data analysis because coding had already left my mind . However, I liked that the difficult concepts were explained in an easy-to-understand way, and most importantly, there was a wealth of practical materials.
I thought Orange could only analyze simple data, but I found out that it is a tool that can analyze and visualize complex and large amounts of data .

-Review of the Experience Group (Hye**)

Effective lectures applicable to practical work

I took this course as the number of situations requiring data analysis increased at my company.

First of all, I was amazed that Orange could implement complex and difficult functions like DBSCAN!
I was worried that it would be difficult to understand since it was an IT lecture, but my worries were for naught because the lecture was well organized and I thought I would be able to use it effectively when working on future projects .

-Review from the experience group (Choi**)

AI Data Analysis Without Coding - Level 5 Clustering and Dimensionality Reduction Curriculum Features

This is a core lecture that presents how to utilize clustering and dimensionality reduction, which are major methods of unsupervised learning, through Orange .

1. Understanding the basic concepts of unsupervised learning

First, understand the difference between supervised and unsupervised learning, and learn through the basic theory of unsupervised learning and various examples.

2. Improve your data classification skills by practicing various clustering techniques.

Understand the basics and applications of K-means clustering and DBSCAN techniques, and learn how to group data and find patterns in data.

3. Acquire the ability to analyze similarity between data using distance and similarity.

Understand various geometric distance calculation methods, such as Euclidean and Manhattan distances, and understand how to use cosine similarity and Pearson correlation coefficient.

4. Extracting core information from data using dimensionality reduction techniques.

Easily reduce high-dimensional data with principal component analysis (PCA), and even utilize nonlinear dimensionality reduction techniques such as t-SNE and LLE, and practice data visualization.

5. Acquire analytical and visualization techniques applicable to actual business data.

Analyze and visualize various real-world business data and share practical application methods.

We'll feed you everything. Just prepare your mind to take the class!

1. Take a class

2. Follow the instructor's instructions and use Orange.

3. Demonstrate competitive analytical capabilities based on data.

The 3 Steps to Success: Now is the Time

Learn this!

Practice clustering and dimensionality reduction using DBSCAN and PCA techniques.

Distance matrix map analysis using distance and similarity measurements between data

Data Analysis Practice with K-Means Clustering

Dimensionality reduction for advanced data with manifold learning

Start analyzing data without complex programming.

Understand the fundamentals of machine learning and analyze data without the burden of coding.

Gain a new perspective on data

Quantify the similarities and differences between data
Gain the ability to visualize high-dimensional data in an easy-to-understand way.

Applying data analysis skills to practice

Through practice of various clustering/dimensional reduction techniques

Unlock data-driven insights across your business

The massive amount of data that increases over time,
Utilizing this is no longer an option but a necessity.

It's not too late. Turn your data from enemy to ally.

Introducing the knowledge sharer

I confidently recommend this to these people.

– Those who want to strengthen their practical data analysis skills
– Those who want to improve their data analysis skills without difficult coding
– Data analysts who seek to derive business insights by applying AI and data to practice.
– Those who want to analyze data using unsupervised learning and clustering & dimensionality reduction techniques.
– Those who feel the limitations of Excel and want a simpler advanced analysis tool
– Job seekers who want to highlight their uniqueness in the job market
– Office workers considering a career change to the IT field

Do machine learning and data analysis feel far from your reach?
Learn unsupervised learning, a core technology for data analysis, without complex programming with Orange.
If you want to drive data-driven decision-making in your business, take this course today.

Lecture Features

STEP 1. From the basic concepts of machine learning and unsupervised learning, step by step.

Clearly understand the concepts of machine learning and unsupervised learning and the process of data analysis.

STEP 2. Learn clustering techniques to discover hidden patterns in data.

Practice using divisive/hierarchical clustering and representative clustering algorithms.

STEP 3. Data visualization and information compression using dimensionality reduction techniques.

Learn how to reduce the dimensionality of complex data while preserving key information using linear and nonlinear dimensionality reduction techniques.

STEP 4. Practical Data Analysis Practice

Acquire practical data analysis skills through hands-on practice using real data alongside theory.

Are you ready to be a leader in the AI era?

Don't think data analysis is difficult anymore.

Now is the perfect time to leap forward!
Be the first to experience enhanced efficiency and creativity.

Expected Questions Q&A

Q. Do I need prior knowledge of artificial intelligence, coding, or design to take this course?
A. This course does not require any prior knowledge of AI, coding, or Excel. We will cover the basics so anyone can easily follow along. However, you can gain a more comprehensive understanding of the course content by taking the previous Orange Course Level 1 or Level 2.

Q. Are there any requirements or prerequisites for taking the course?
A. If you have never used Orange before, we recommend that you learn the basic usage and installation methods in advance to make the course more smooth.

Q. Orange? Do I need to purchase separate software?
A. Orange is free software that allows anyone to easily build an AI data analysis environment. The portable version allows use without an external internet connection, making it ideal for use in high-security work environments.

Please check before taking the class!