Finding Signals and Noise through Python Stock Data Collection and Analysis

This is not an investment lecture. It covers data collection, analysis, and visualization using stock market data. You will work with various data formats and learn various text preprocessing techniques. It covers visualization techniques for time-series data, methods for representing scales, and several techniques for interpreting stock price data. This course is about learning how to gain insights through data analysis. It is structured so that the concepts learned through stock price data can be applied to collecting, analyzing, and visualizing other time-series data such as demand, inventory, sales, and traffic volume.

(4.8) 수강평 109개

강의소개.상단개요.수강생.short

난이도 초급

수강기한 무제한

Python
Python
Plotly
Plotly
Pandas
Pandas
Numpy
Numpy
Seaborn
Seaborn
Python
Python
Plotly
Plotly
Pandas
Pandas
Numpy
Numpy
Seaborn
Seaborn

먼저 경험한 수강생들의 후기

먼저 경험한 수강생들의 후기

4.8

5.0

yonghankim7

93% 수강 후 작성

I think this is the best lecture ever!!! I've taken many lectures, but I think this is the first time I've learned as much and been helped as much by Professor Park Jo-eun's lecture! There are times when I get confused by grammar rules and think, "Hmm... why is that like that?", but after the teacher explains the concept, she would say on the video, "There are many people who ask, "Why isn't this grammar rule valid?" and she would answer my questions exactly as they were. I remember being so surprised every time I took the class. ㅋㅋㅋㅋ It was a really helpful class, and the class on analyzing stock data was so fun that I finished all the lectures in less than a month! All of you who are thinking about it, you won't regret your choice! If you are thinking about it, sign up for Professor Park Jo-eun's lecture right away!!!! 5 stars!

5.0

hakjuknu

94% 수강 후 작성

Great!

5.0

moonchoh

100% 수강 후 작성

I took the class because I was interested in stocks. It's a little past the beginning of the lecture, but I'm looking forward to the end ^^

강의상세_배울수있는것_타이틀

  • Web scraping, not crawling

  • Collecting stock price information in a single line using FinanceDataReader

  • Collecting data with just one or two lines of Pandas code

  • How to collect data without complex coding based on an understanding of the browser's network tab

  • Handling JSON File Formats

  • Handling time series data and exploring various operations using diff and shift

  • Calculating daily returns and cumulative returns of stock prices

  • Differences and usage of seaborn, plotly, pandas plot, and matplotlib **1. Matplotlib** * **Difference:** The most fundamental and oldest library in Python. It offers the highest degree of freedom but requires a lot of code to create complex charts. * **Usage:** Used for low-level customization or when you need to control every detail of a plot. **2. Seaborn** * **Difference:** Built on top of Matplotlib. It provides beautiful default styles and makes it easy to visualize complex statistical relationships with simple code. * **Usage:** Best for statistical data analysis and creating aesthetically pleasing static graphs. **3. Pandas Plot** * **Difference:** A plotting function built directly into Pandas DataFrames. It uses Matplotlib as a backend. * **Usage:** Useful for quickly checking the distribution or trends of data immediately after data processing. **4. Plotly** * **Difference:** A library for creating interactive web-based visualizations. It allows users to zoom, pan, and see data values on hover. * **Usage:** Ideal for creating dashboards, web applications, or when interactive data exploration is required.

  • Interactive visualization techniques using Plotly and Cufflinks

  • Pandas filter, merge, concat, and text preprocessing methods

  • How to collect and analyze all ETF, sector, and theme stocks

  • Implementation and understanding the principles of auxiliary indicators such as Bollinger Bands, MACD, and RSI

  • Understanding terms for stock market beginners: PER, EPS, BPS, PBR, ETF, Inverse, Leverage, Currency Hedge, etc.

Data A to Z learned through stock data,
master everything from collection and analysis to visualization!

Find signals and noise within data,

and gain more insights! 📈

Everyone, are you familiar with the book ?
We aim to predict an uncertain future and find insights through data analysis and visualization.
That is why it is the data analyst's job to identify signals and noise within vast amounts of data.

This lecture starts from exactly that perspective.
It involves receiving and analyzing stock price data in real-time, which continues to rise and fall even at this very moment.

증권 데이터 수집과 분석으로 신호와 소음 찾기

In this course, through the three stages of data collection/preprocessing - analysis - visualization,

  • Instead of using data collected by someone else, you will learn how to collect and preprocess the data yourself.
  • The goal is to learn and apply data analysis methods for use in work or research.

Data Analysis,
why should you learn it
using stock market data?
📊

  • What if you have to copy and paste content from dozens or hundreds of web pages into Excel?
  • What if the collected data is so messy that you don't even know where to start?
  • If you're not sure how to apply the statistical terms you learned in middle school?

👉 If you related to this, now is the time to build your foundational data strength!

Did you know that Pandas, the Python data analysis library, was developed by a quant working on Wall Street? Stock market data is the perfect type of data for applying various analytical methods, formulas, and statistics.

What the differences are between categorical and numerical data,
and what the appropriate visualization methods are to distinguish signals from noise in data...
By analyzing securities data, you can learn how to handle data in various formats.

Building fundamental data skills
through stock data analysis.

보조 지표 이해하고 차트 구현하기

  • ✅ You will also directly implement technical analysis indicators such as Moving Averages, Bollinger Bands, MACD, and RSI.
  • ✅ You will also practice drawing charts with just a line or two of code using pre-implemented libraries.
  • ✅ Understand the principles of technical analysis and implement charts just as you see them on HTS or MTS.

Who is this for? 🔍

Those who want to learn
data analysis
using live data

Those who want to get started with
data analysis
using Python

Those who want to learn how to
collect and preprocess
data

Those who want to
implement/analyze auxiliary indicators such as
Bollinger Bands, MACD, and RSI

📢 Please check the required prerequisite knowledge!

  • This course is at a beginner level or higher and requires the following prerequisite knowledge.
    • How to read a table in Excel format (understanding rows and columns)
    • Basic arithmetic operations and middle school-level mathematical knowledge
    • Understanding of mean, median, percentage, variance, and standard deviation
    • Understanding of Python strings, numbers, lists, and variables

Here are the
key features of this course. ⚡️

One, collect data with just one or two lines of code 🧹

We will learn how to collect data from web pages—which previously seemed to require heavy tools like Selenium—using just one or two lines of code by leveraging the browser's network tab. You will be able to directly collect and analyze the information necessary for your work or research.

Second, dynamic visualization tools are also OK. 📈

In this course, you will learn how to use not only static visualization tools but also dynamic ones. Through dynamic data visualization, you will be able to effectively convey and implement more extensive and complex information than when representing data with static tools alone.

Three, we help you develop the ability to see the big picture 🧰

It is difficult to learn many tools all at once. You only need to understand the core functions. If you know how to read and understand documentation even when tools change, you won't be afraid when new libraries emerge.

Four, libraries for convenient use! 💡

The functions we feel we need have already been created by someone as abstracted libraries. We will learn how to install and familiarize ourselves with new tools. These can be used conveniently when actually analyzing data.

Fifth, we provide comprehensive practice materials. 💻

We provide two types of practice materials: an "input" file with no code and an "output" file with the code already entered. You can follow along with the lecture by typing the code yourself into the empty cells provided with descriptions, or you can practice by running the file that already contains the code. Alternatively, you can use the empty cells to review and test your knowledge after finishing the lecture.

Six, from technical analysis to auxiliary indicators! 📖

This course includes a process where you can understand the principles of various auxiliary indicators (Moving Average, Bollinger Bands, RSI, MACD, etc.) found in securities firms' HTS and MTS by directly implementing and displaying them on visualized charts. It is helpful for those who want to learn how to directly implement and analyze various technical indicators.

코드가 입력되지 않은 파일(input)과 입력된 파일(output) 2가지 실습자료를 제공 Two types of practice materials are provided: files without code (input) and files with code (output).

HTS, MTS 에서 볼 수 있는 보조지표(이동평균, 볼린저밴드, RSI, MACD) 직접 구현하고 원리 이해하기 Understand the principles and directly implement auxiliary indicators (Moving Average, Bollinger Bands, RSI, MACD) found in HTS and MTS


With just one lecture, 
you can learn 
this many skills. 📌

A one-line introduction to the skills you'll learn in this course!

  • 🐼 Pandas: Python's representative data analysis tool, originally created for financial data analysis.
  • 🧮 Numpy : A numerical computing tool for Python.
  • 📊 matplotlib : This is Python's representative data visualization tool.
  • 📊 seaborn: A high-level visualization tool that abstracts matplotlib for ease of use and provides basic statistical operations.
  • 📊 plotly : Provides high-level and low-level visualization features and enables interactive visualization.
  • 📊 cufflinks : A productive tool that powerfully connects plotly and pandas.
  • 📈 FinanceDataReader: A tool that allows you to collect financial data with just a line or two of code.
  • 🌏 Requests: A tool that allows you to retrieve web page source code via HTTP communication.
  • 🔍 BeautifulSoup4: A tool that allows you to extract the information you want from a web page's source code.
  • ⏰ tqdm: This tool allows you to view the progress status of time-consuming tasks in data collection or preprocessing.
데이터 시각화를 통한 신호와 소음 찾기
데이터 시각화를 통한 신호와 소음 찾기

Check out the answers to
your questions first! 🙋‍♀️

Q. Can non-majors take this course?

Data analysis is useful in many areas, regardless of whether you are a major or non-major. By learning data analysis techniques using Python instead of Excel, you can apply them in various ways to your work and research. I have already conducted corporate training for non-development roles using this offline curriculum. I have supplemented the curriculum by conducting various interviews regarding the difficulties encountered in the field. Mastering the core functions for analysis and visualization will help improve your work efficiency.

Q. Why should I learn data analysis and collection skills using Python?

Excel is one of the essential skills for professionals, regardless of the type of work they do. However, Excel has limitations regarding the size and types of data it can import; by learning Python, you will be able to handle various formats and large-scale data.

Q. What are the benefits of learning data analysis and collection techniques?

Often, you may find yourself performing repetitive tasks like flipping through pages and using drag-and-drop or copy-paste to collect necessary data. Now, you can leave these tasks to Python⏰ and spend your time on more productive work or take a break🧘‍♀️.

Q. Is there anything I need to prepare before taking the course?

It is helpful to have an understanding of Python concepts such as variables, numbers, strings, and lists. Additionally, middle school-level mathematical knowledge, including mean, median, variance, standard deviation, and percentiles, is required.

Q. To what level of depth does the course cover?

We collect, preprocess, analyze, and visualize stock market data. The course covers Python skills from beginner to intermediate levels. The difficulty increases significantly starting from the collection of industry theme information. The goal is to enable various roles—such as planning, marketing, sales, and operations—to directly utilize data analysis in their work. If you are new to programming, you may find the course challenging from the midpoint onwards. In such cases, it is recommended to run the completed files (those with "output" at the end of the filename) provided by the instructor, create a new code cell directly below, and practice by following along exactly.

Q. What level of computer performance is required to take this course?

Any PC or laptop with at least 4GB of RAM and about 20GB of free storage space will be fine. If your computer's performance is low, you can practice using Google Colaboratory.

Q. Can I summarize and post the course content on my personal blog or GitHub?

There is a copyright notice on the lecture's GitHub. Please include the source attribution when organizing and publishing the content.


Please check before taking the course! ⚠️

Those who want predictions through time series models like ARIMA, machine learning, or deep learning:
This course mainly covers data collection, preprocessing, analysis, and visualization. It does not include the process of predicting future data.

Those who want automated trading:
We do not use securities firm APIs related to automated trading.

Those who expect to gain significant profits in the stock market by mastering data analysis:
This is a data analysis course, not a stock investment course. Unfortunately, you may be disappointed if you are expecting investment-related skills. Furthermore, even if you invest using the analysis techniques learned in this course, the responsibility for any investment losses lies with the investor.

Please watch some of the lessons made available through the Inflearn preview or the YouTube channel first before deciding whether to take the course.

You can preview some of the lessons before enrolling. Please check if the course aligns with your learning goals. If you have any questions, feel free to ask through the pre-enrollment inquiry.


Curious about the knowledge sharer
who created this course? 👩‍💻

Interview with Knowledge Sharer Joeun Park X Inflearn

강의소개.콘텐츠.추천문구

학습 대상은 누구일까요?

  • Those who want to learn analysis and visualization using stock data rather than for investment purposes.

  • Those who want to learn data analysis through live data

  • Those who wish to start learning Python data analysis

  • Those who want to learn data collection methods

  • Those who want to learn how to preprocess collected data

  • How to directly implement and analyze technical indicators such as Bollinger Bands, MACD, and RSI

선수 지식, 필요할까요?

  • How to read an Excel-style table (Understanding rows and columns)

  • Basic arithmetic operations and middle school-level mathematical knowledge

  • Understanding Mean, Median, Percentage, Variance, and Standard Deviation

  • Understanding Python strings, numbers, lists, and variables

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109개의 수강평

  • hakjuknu님의 프로필 이미지
    hakjuknu

    수강평 155

    평균 평점 5.0

    5

    94% 수강 후 작성

    Great!

    • cypooh1036님의 프로필 이미지
      cypooh1036

      수강평 3

      평균 평점 5.0

      5

      100% 수강 후 작성

      This is the best course that really helps with data analysis and visualization in Python.

      • roykim7님의 프로필 이미지
        roykim7

        수강평 4

        평균 평점 5.0

        5

        93% 수강 후 작성

        I think this is the best lecture ever!!! I've taken many lectures, but I think this is the first time I've learned as much and been helped as much by Professor Park Jo-eun's lecture! There are times when I get confused by grammar rules and think, "Hmm... why is that like that?", but after the teacher explains the concept, she would say on the video, "There are many people who ask, "Why isn't this grammar rule valid?" and she would answer my questions exactly as they were. I remember being so surprised every time I took the class. ㅋㅋㅋㅋ It was a really helpful class, and the class on analyzing stock data was so fun that I finished all the lectures in less than a month! All of you who are thinking about it, you won't regret your choice! If you are thinking about it, sign up for Professor Park Jo-eun's lecture right away!!!! 5 stars!

        • sunyeol12035400님의 프로필 이미지
          sunyeol12035400

          수강평 2

          평균 평점 5.0

          5

          7% 수강 후 작성

          I am a liberal arts student who has been living a life of being indifferent to programming, but I wanted to get started with data analysis, so I took the public data lecture and took the class. I am a liberal arts student, but I think I am starting to get the hang of it after listening to it repeatedly! I thought I would be able to apply Python in various places in the future by learning how to use libraries and how to view documents and help. I think it will be very helpful for future visualizations because it covers various Python data visualization tools. Thanks to this, I also gained some knowledge about securities.

          • moonchoh님의 프로필 이미지
            moonchoh

            수강평 5

            평균 평점 5.0

            5

            100% 수강 후 작성

            I took the class because I was interested in stocks. It's a little past the beginning of the lecture, but I'm looking forward to the end ^^

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