This is not an investment lecture. It covers data collection, analysis, and visualization through securities data. It covers various data formats and teaches various text preprocessing techniques. It covers visualization techniques for time series data and methods for expressing scale. It covers several techniques for interpreting stock price data. This lecture is a course that teaches you how to gain insights through data analysis. The content learned through stock price data is organized so that you can apply it to collecting, analyzing, and visualizing data such as demand, inventory, sales, and traffic volume, where time series is used.
Web scraping, not crawling
Collecting stock price information in one line using FinanceDataReader
Collecting data with one or two lines of Pandas code
How to collect data without complex coding based on understanding the network tab of the browser
Working with JSON File Formats
Learn how to handle time series data and various operations using diff and shift
Calculating Daily and Cumulative Return on Stock Price
Differences and usage of seaborn, plotly, pandas plot, matplotlib
Interactive visualization techniques using plotly, cufflinks
Pandas filter, merge, concat, text preprocessing methods
How to Collect and Analyze All ETF Industry/Theme Stocks
Implementation and understanding of auxiliary indicators such as Bollinger Bands, MACD, and RSI
Understanding terms such as PER, EPS, BPS, PBR, ETF, inverse, leverage, and currency hedging for beginners
Data A to Z, Learning from Securities Data
Learn how to collect, analyze, and visualize!
Hey guys, do you know the book "Signal and Noise"?
We use data analysis and visualization to predict uncertain futures and find insights.
That's why it's the job of a data analyst to find the signal and noise in a huge amount of data .
This lecture starts from precisely that perspective.
This is receiving and analyzing real-time stock price data that is rising and falling right now.
In this lecture, we will go through the three steps of data collection/preprocessing - analysis - visualization .
👉 If you agree, now is the time to build your data fundamentals !
Did you know that Pandas, a Python data analysis library, was developed by quants working in the securities industry? Securities data is data that can be applied with various analysis methods, formulas, statistics, etc.
What is the difference between categorical data and numerical data?
What are the appropriate visualization methods to find signal and noise in data?...
Analyzing securities data will teach you how to work with data in a variety of formats.
With living data
Data analysis
For those who want to learn
Using Python
In data analysis
For those who want to get started
Collect data and
How to preprocess
For those who want to learn
Bollinger Bands, MACD,
Auxiliary indicators such as RSI
Anyone who wants to implement/analyze
📢 Check your player knowledge!
Collect data in one or two lines of code 🧹
Learn how to collect data from web pages that seemed like they could only be collected using heavy tools like Selenium, using just one or two lines of code, using the network tab of your browser. You can directly collect and analyze the information you need for your work or research.
2. Dynamic visualization tools are also OK. 📈
In this course, you will learn how to use dynamic visualization tools as well as static visualization tools. Dynamic data visualization will allow you to effectively convey and implement a wider range of complex information than when you only use static visualization tools to express data.
Third, we will develop the ability to see the forest 🧰
It's hard to learn many tools at once. Just understand the core functions. If you know how to read and understand the documentation even when the tool changes, you won't be afraid when a new library appears.
Four, a library that you can use conveniently! 💡
The features we feel we need have been abstracted by someone into libraries. Learn how to install and learn new tools. They can be useful when actually analyzing data.
Five, we provide rich practical materials. 💻
We provide two types of practice materials: a file without code (input) and a file with code (output). You can follow the lecture by directly entering the code in the blank cells with descriptions, or you can practice by running the file with the code, or you can review by filling in the blank cells after listening to the lecture.
Six, Technical Analysis + Auxiliary Indicators! 📖
This course includes a process to understand the principles by implementing and displaying various auxiliary indicators (moving average, Bollinger bands, RSI, MACD, etc.) that can be seen in securities companies' HTS and MTS on visualized charts. It will be helpful for those who want to learn how to implement and analyze various auxiliary indicators themselves.
Q. Can non-majors also take the course?
Regardless of your major or non-major, data analysis can be used in many ways if you learn it. If you learn data analysis techniques using Python instead of Excel, you can use them in various ways for work and research. I have already conducted corporate lectures for non-development positions through offline curriculums on this content. I conducted various interviews about the areas where people find it difficult and supplemented the curriculum. Learning the core functions for analysis and visualization will help you increase work efficiency.
Q. Why should I learn data analysis and collection techniques with Python?
Excel is one of the essential skills for office workers, regardless of the job. However, Excel has limitations in terms of the size and type of data that can be imported, but if you learn it through Python, you will be able to handle various formats and large amounts of data.
Q. What are the benefits of learning data analysis and collection techniques?
There are often repetitive tasks that require you to go through each page, drag and drop, and copy and paste to collect the data you need. Now, you can leave this work to Python⏰ and invest your time in more productive work or take a break🧘♀️.
Q. Is there anything I need to prepare before attending the lecture?
It would be helpful to understand the concepts of variables, numbers, characters, lists, etc. in Python. Also, a middle school level knowledge of mathematics such as mean, median, variance, standard deviation, and percentile is required.
Q. To what extent does the class cover the content?
Collect, preprocess, analyze, and visualize securities data. Covers basic to intermediate Python skills. The difficulty level increases significantly from collecting industry theme information. The goal is to be able to directly utilize data analysis in various fields such as planning, marketing, sales, and operations. If you are new to programming, you may feel difficult from the middle of the lecture. In this case, I recommend that you look at the completed file with the name output at the end of the file name among the materials provided by the instructor and create a code cell right below it and follow along.
Q. What level of computer performance is required to take the course?
Any PC or laptop with at least 4GB of memory and about 20GB of remaining storage will do. If your computer's performance is low, you can try practicing through Google Colaboratory.
Q. Can I organize the class content and publish it on my personal blog or GitHub?
The copyright notice is on the GitHub page of this lecture. Please indicate the source when organizing and publishing it.
For those who want to make predictions using time series models such as ARIMA, machine learning, or deep learning:
It mainly deals with data collection, preprocessing, analysis, and visualization. There is no process of predicting future data.
For those who want to do automatic trading:
We do not use securities APIs related to automated trading.
For those who expect that learning data analysis will help them make big profits in the stock market:
This lecture is not a securities investment lecture, but a data analysis lecture. Unfortunately, if you expect investment-related skills, you may be disappointed. Also, even if you invest using the analysis techniques learned in the lecture , the investor is responsible for any investment losses.
Please listen to some of the classes released through Inflearn Preview or YouTube Channel first and then decide whether to take the class.
You can preview some of the classes before taking them. Check if they are the direction you want to study. Also, if you have any questions, please ask them through the inquiry before taking the class.
Knowledge Sharer Park Jo-eun X Inflearn Interview
Who is this course right for?
Those who want to learn analysis and visualization using securities data rather than for investment purposes
Those who want to learn data analysis through live data
Anyone who wants to get started with Python data analysis
Those who want to learn how to collect data
Those who want to learn how to preprocess collected data
How to implement and analyze auxiliary indicators such as Bollinger Bands, MACD, and RSI
Need to know before starting?
How to read a table in Excel format (understanding rows and columns)
Basic arithmetic and middle school level math knowledge
Understanding Mean, Median, Percentiles, Variance, and Standard Deviation
Understanding Python's Strings, Numbers, Lists, and Variables
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정말 최고의 강의라고 생각합니다!!! 많은 강의를 들어왔었지만 박조은 선생님의 강의처럼 많이 배우고 도움이 된 강의는 처음인거 같아요! 중간중간에 스스로 헷갈리는 문법이 있어서 "음... 저건 왜 그러지?" 라고 생각할 때가 있는데, 선생님께서 개념 설명을 해주신 다음에, 영상에서 바로 "이러이러한 문법은 왜 안되나요? 라는 질문을 주시는 분들이 많은데" 라고 제가 궁금했던 것들을 그대로 짚어주셔가지고 매번 깜짝깜짝 놀라서 수업을 들었던 기억이 나네요 ㅋㅋㅋㅋ 정말 도움이 많이 되었던 수업이었고 증권 데이터를 가지고 분석해보는 수업에 너무 재미있어서 1달도 안돼서 강의 모두 완료 했습니다! 고민하고 있는 여러분들 모두 후회하지 않을 선택하실꺼에요! 고민하고 있으시면 바로 박조은 선생님 강의 신청하세요!!!! 별 5개!
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