Learn Data Science with Kaggle Practice

Hello. Thank you to everyone who took the course.

💻 Finding Signal and Noise by Collecting and Analyzing Securities Data 👉 http://bit.ly/inflearn-finace-data

We have opened a new course. We are offering a 30% discount during the early bird period.

📈 Finding Signal and Noise in Securities Data

We currently receive and analyze stock price data that is rising and falling in real time.
Learn how to collect data yourself without using data collected by someone else.
Learn how to preprocess.
The purpose is to learn and apply data analysis methods for use in work or research.

📊 Why it's good to learn data analysis with securities data

Did you know that Pandas was developed by quants working in the financial world?!
Securities data is data that can be applied with various analysis methods, formulas, statistics, etc.

What if you need to copy and paste content from dozens or hundreds of pages of websites into Excel?!
What if your collected data is so messy that you don’t know where to start?!

What is the difference between categorical data and numerical data?
What is the right visualization method to find signal and noise in data?!
You will learn how to handle data in various formats.

I also tried implementing technical analysis such as moving averages, Bollinger Bands, MACD, and RSI.
You can even draw it with a line or two of code using an already implemented library.
Understand the principles of technical analysis
Let's implement a chart like you see in HTS or MTS.

⚡️ Course Features  

🧹 Data from web pages that seemed like they could only be collected using heavy tools like Selenium
Learn how to collect this in a line or two of code using the network tab in your browser.
You can directly collect and analyze the information you need for work or research.

📈 Learn how to use dynamic visualization tools as well as static ones.

🛠 It's difficult to learn many tools at once.
Even if you use various tools, if you understand only the core functions,
Knowing how to view and understand documentation even when tools change
You won't be afraid when new libraries appear.

💡Someone has created an abstract library for any feature we feel we need.
Learn how to install and get familiar with new tools.

🛠 Introduction to learning skills

🐼 Pandas : A representative data analysis tool in Python, created for financial data analysis.
🧮 Numpy : Python's numerical calculation tool.
📊 matplotlib : Python's representative data visualization tool.
📊 seaborn : A high-level visualization tool that abstracts matplotlib for easy use and provides basic statistical operations.
📊 plotly : Provides high-level and low-level visualization features and allows interactive visualization.
📊 cufflinks : A productive tool that powerfully connects plotly and pandas.
📈 FinanceDataReader : A tool that allows you to collect financial data with just one or two lines of code.
🌏 Requests : This is a tool that can receive the source code of a web page via HTTP communication.
🔍 BeautifulSoup4 : A tool that can retrieve desired information from the source code of a web page.
⏰ tqdm : You can view the progress of long-running tasks such as data collection or preprocessing.

📊 How to use and the differences between various visualization libraries

Image source: https://pyviz.org/overviews/index.html

💻  Provides two types of practice materials: a file without code (input) and a file with code (output).

You can follow the lesson line by line by directly entering the code into the blank cells with the description.
You can also practice by running the file containing the code .
You can review by listening to the lecture and filling in the blank cells .

📈 Implement auxiliary indicators (moving average, Bollinger bands, RSI, MACD) that can be seen in HTS and MTS and understand the principles

🙋‍♀️ Expected Questions Q&A

• 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.

• 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.

• 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 to Python ⏰ and invest your time in more productive work or just take a break 🧘‍♀️.

• What can I do after taking the course?
You will be able to directly collect, analyze, and visualize data generated from work and research, and apply it to production volume, inventory volume, sales volume, traffic volume, etc. It can also be used to analyze the industry, theme, or ETF of the stock you are investing in, but investment opinions will not be provided in the lecture.

• 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.

• To what extent does the course content cover?
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.

• What level of computer performance is required to take the course?
Any PC or laptop with more than 4G of memory and about 20G of remaining storage space will do. If your computer's performance is low, you can try practicing through Google Colaboratory .

• 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.

⚠️ Please check before taking the class.

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 lectures that are available through Infraon Preview or the knowledge sharer's YouTube channel before deciding whether to take the course .
You can preview some lectures before taking the course. Check if it is the direction you want to study. Also, if you have any questions, please ask them through the inquiry before taking the course.

📈 Finding Signal and Noise by Collecting and Analyzing Securities Data 👉 http://bit.ly/inflearn-finace-data

There will be a 30% discount during the early bird period!

thank you