
(Free) Introduction to Stock Data Analysis with Python (Finance/Quant)
HappyAI
This is a course for beginners in stock data analysis. Get started with stock data analysis using Python!
Beginner
Quant, Investment, Python
This is a lecture on learning text mining analysis techniques, big data analysis techniques, word frequency analysis, word cloud visualization, morphological analysis, and topic modeling analysis techniques utilizing Python and ChatGPT, covering how to utilize text mining data analysis techniques necessary for writing papers and basic application methods for research papers.

Reviews from Early Learners
5.0
뚱이 언니
Hello, I am leaving a lecture review after listening to half of the class. I have a PhD in language and have absolutely no knowledge of Python, but I am listening to the lecture with satisfaction because the teacher writes the code well and explains it well. Recently, I kept getting errors related to using LDAvis, so the teacher helped me remotely at 10 PM, but I couldn't do it even after 12:30 PM. It was late, and the teacher was really trying to help me, but I was really sorry that it didn't work, but the teacher corrected the error and sent me the code by email the next day. That's how sincere and passionate the teacher is in teaching. (When I came in, I saw that the teacher had updated the code in the lecture list.) Even if you have absolutely no knowledge of Python like me, you can listen to the lecture, and if you ask the teacher for help, he will help you, so I hope you listen to the lecture. After I finish this course, I plan to listen to the teacher's other (paid) lectures. I sincerely recommend the lecture content and the teacher.
5.0
밝은 비버
This is a lecture that I found like a ray of light when I was frustrated while studying text mining on my own :-) Why is it so easy to understand when it explains all the hot methodologies that are often covered in theses these days..!! (Honestly, if I were to take it at school, it would be a 1-semester lecture..How come you explain it so compactly?>.<) More than anything, it was a practical lecture, not a theory lecture, so it was very helpful in practice, and it was a lecture that completely condensed knowledge, so I didn't regret the tuition at all. In fact, it felt cheap..ㅠㅠ One more thing that is the most...perfect thing about this lecture..!! When I leave a question, the teacher gives feedback almost in real time, and tries to solve it together until the end, so I dare to give a thumbs up!!^^bb Thank you, teacher~~
5.0
우아한 북극곰
This lecture is truly a blessing in disguise for graduate students or researchers preparing a text mining research paper. There are many books on Python text mining on the market, but there is no one that provides guidance for beginners in coding to actually apply coding to research papers. This lecture is very helpful because it shows the entire process of how Python coding is actually done from the data collection stage to the analysis method so that it can be applied directly to research papers. I especially liked the fact that they gave me quick and friendly feedback on parts I was curious about. This lecture is not a lecture that explains the principles of coding grammar in detail. However, since it is a lecture that allows you to immediately apply the necessary parts of your research to real-life situations even if you don't know how to code, it is a lecture that is perfect for those who want to do text mining research but have difficulty applying coding!
Summary of Key Tips for Text Mining using Python
Word frequency analysis
Word Cloud Visualization
Morphological analysis
TF-IDF for identifying importance in text
Text Topic Classification using the LDA Method
Data Interpretation Methods for Big Data/Text Mining Thesis Writing
Explanation of the core theory of Text Mining
Sentiment Analysis using the KNU Sentiment Dictionary (Sentiment Word Extraction, Sentiment Document Ratio)
Researchers, data scientists, and machine learning engineers working in the data field
Researchers and graduate students who want to write papers on text mining and big data
Anyone interested in big data analysis and text mining analysis technology
Provides information on data extraction and analysis required for paper writing
Practical data analysis using real Python
Easy explanation that even Python beginners can follow
Learn how to extract meaningful information from big data and text data with Python.
The right ratio of theory and practice! Practice in which actual data analysis is performed based on theory.
Who is this course right for?
Graduate students, researchers aiming to write Text Mining/Big Data papers
Those who want to learn text mining techniques
Those interested in text mining using Python
Conducting Text Mining Projects and Research Tasks
Those who want SNS data analysis
Those who want to understand customer needs for marketing by analyzing customer feedback and reviews
Those who want to try sentiment analysis
Need to know before starting?
You need some knowledge of Python basics.
Need to know how to use Google Colab
5,073
Learners
285
Reviews
52
Answers
4.6
Rating
11
Courses
AI·LLM·Big Data Analysis Expert / CEO of Happy AI
👉You can check the detailed profile at the link below.
https://bit.ly/jinkyu-profile
Hello.
I am Lee JinKyu (Ph.D. in Engineering, Artificial Intelligence), CEO of Happy AI, who has consistently handled AI and big data analysis in R&D, education, and project sites.
I have analyzed various types of unstructured data, such as
surveys, documents, reviews, media, policies, and academic data,
based on Natural Language Processing (NLP) and text mining.
Recently, I have been delivering practical AI application methods tailored to organizations and work environments
using Generative AI and Large Language Models (LLM).
We have collaborated with numerous public institutions, corporations, and educational organizations such as Samsung Electronics, Seoul National University, the Office of Education, Gyeonggi Research Institute, the Korea Forest Service,
the Korea National Park Service, and the Seoul Metropolitan Government,
and have conducted more than 200 research and analysis projects across various domains including healthcare, commerce, ecology, law, economics, and culture.
📧 Email : leejinkyu0612@naver.com
🌐 Homepage : https://happyaidata.kr
📝 Blog : https://blog.naver.com/leejinkyu0612
📺 YouTube : https://www.youtube.com/@HappyAI_0612
💻 GitHub : https://github.com/leejin-kyu
📞 Mobile : 010-9973-2113
💬 KakaoTalk : jinkyu0612
※ Kmong Prime Expert (Top 2%)
2024.07 ~ Present
CEO of Happy AI, a company specializing in Generative AI and Big Data analysis
Ph.D. in Engineering (Artificial Intelligence)
Dongguk University Graduate School of AI
Detailed Major: Large Language Models (LLM)
(2022.03 ~ 2026.02)
2023 ~ 2025
Public News AI Columnist
(Generative AI Bias, RAG, LLM Application Issues)
2021 ~ 2023
AI & Big Data specialized company Stellavision Developer
2018 ~ 2021
Government-funded Research Institute Natural Language Processing & Big Data Analysis Researcher
Generative AI and LLM Utilization
Private LLM, RAG, Agent
Basics of LoRA and QLoRA Fine-tuning
AI-based Big Data Analysis
Survey, review, media, policy, and academic data
Natural Language Processing (NLP) · Text Mining
Topic analysis, sentiment analysis, keyword network
Public and Corporate AI Task Automation
Document summarization, classification, and analysis
LLM/sLLM Application Development
(Fine-tuning, RAG, Agent-based) – KT
LangChain·RAG-based LLM Programming – Samsung SDS
LLM Theory and RAG Chatbot Development Practice – Seoul Digital Foundation
Introduction to ChatGPT-based Big Data Analysis – LetUin Edu
AI Fundamentals & Prompt Engineering Techniques – Korea Vocational Development Institute
LDA & Sentiment Analysis with ChatGPT – Inflearn
Python-based Text Analysis – Seoul National University of Science and Technology
Building LLM Chatbots Using LangChain – Inflearn
Python Basics using ChatGPT – Kyonggi University
Big Data Expert Course Special Lecture – Dankook University
Fundamentals of Big Data Analysis – LetUin Edu
Building a Private LLM-based RAG Chatbot (Korea Electric Power Corporation)
LLM-based Forest Restoration Big Data Analysis (National Institute of Forest Science)
Internal Network Private LLM Text Mining Solution (Government Agency)
LLM Model Development based on Instruction Tuning and RLHF
Healthcare, Law, Policy, and Education Data Analysis
AI Analysis of Survey, Review, and Media Data
→ Performed over 200 cases, including public institutions, corporations, and research institutes
Improving Commonsense Bias Classification by Mitigating the Influence of Demographic Terms (2024)
Improving Generation of Sentiment Commonsense by Bias Mitigation
– International Conference on Big Data and Smart Computing (2023)
Analysis of Perceptions of LLM Technology Based on News Article Big Data (2024)
Numerous NLP-based text mining studies
(Forestry, Environment, Society, and Healthcare sectors)
Python-based data analysis and visualization
Data analysis using LLM
Improving work productivity using ChatGPT, LangChain, and Agents
All
48 lectures ∙ (5hr 38min)
Course Materials:
All
17 reviews
4.5
17 reviews
Reviews 1
∙
Average Rating 5.0
5
Hello, I am leaving a lecture review after listening to half of the class. I have a PhD in language and have absolutely no knowledge of Python, but I am listening to the lecture with satisfaction because the teacher writes the code well and explains it well. Recently, I kept getting errors related to using LDAvis, so the teacher helped me remotely at 10 PM, but I couldn't do it even after 12:30 PM. It was late, and the teacher was really trying to help me, but I was really sorry that it didn't work, but the teacher corrected the error and sent me the code by email the next day. That's how sincere and passionate the teacher is in teaching. (When I came in, I saw that the teacher had updated the code in the lecture list.) Even if you have absolutely no knowledge of Python like me, you can listen to the lecture, and if you ask the teacher for help, he will help you, so I hope you listen to the lecture. After I finish this course, I plan to listen to the teacher's other (paid) lectures. I sincerely recommend the lecture content and the teacher.
Reviews 1
∙
Average Rating 5.0
5
This lecture is truly a blessing in disguise for graduate students or researchers preparing a text mining research paper. There are many books on Python text mining on the market, but there is no one that provides guidance for beginners in coding to actually apply coding to research papers. This lecture is very helpful because it shows the entire process of how Python coding is actually done from the data collection stage to the analysis method so that it can be applied directly to research papers. I especially liked the fact that they gave me quick and friendly feedback on parts I was curious about. This lecture is not a lecture that explains the principles of coding grammar in detail. However, since it is a lecture that allows you to immediately apply the necessary parts of your research to real-life situations even if you don't know how to code, it is a lecture that is perfect for those who want to do text mining research but have difficulty applying coding!
Reviews 1
∙
Average Rating 5.0
5
After hearing the course reviews, I also rushed through the lectures Although the progress rate is high, I honestly don't know what it means. I'm too much of a beginner. I feel like I should have taken the Textom lecture. From Python ~~~~~~~~ ah~ it's difficult. I need to submit a journal paper in May.. Actually, I saw a colleague write and upload a paper with Textom in just 1.5 days, so I thought I could do it too. After taking the lecture, I'm even more lost. Seems I need to go for the Textom lecture... So sad.
Hello 😊 If you are new to big data analysis, the concepts might feel a bit difficult. This is especially true if you are new to Python or text mining. As you mentioned, taking a free course like "Basic Text Mining: App Review Analysis with Python" or "TEXTOM Basic Course: SNS Perception Analysis for Writing Big Data Papers" before this course will be much more helpful in understanding the overall flow. Try taking a basic course first, and then coming back to this main course will make it easier to follow along. I also wish you all the best in completing your academic paper! Thank you 😊
Reviews 1
∙
Average Rating 5.0
5
This is a lecture that I found like a ray of light when I was frustrated while studying text mining on my own :-) Why is it so easy to understand when it explains all the hot methodologies that are often covered in theses these days..!! (Honestly, if I were to take it at school, it would be a 1-semester lecture..How come you explain it so compactly?>.<) More than anything, it was a practical lecture, not a theory lecture, so it was very helpful in practice, and it was a lecture that completely condensed knowledge, so I didn't regret the tuition at all. In fact, it felt cheap..ㅠㅠ One more thing that is the most...perfect thing about this lecture..!! When I leave a question, the teacher gives feedback almost in real time, and tries to solve it together until the end, so I dare to give a thumbs up!!^^bb Thank you, teacher~~
Reviews 2
∙
Average Rating 5.0
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