Introductory Notebooks for Data Analytics using Python/Pandas/Other libraries. There are nine sessions after studying you can understand/use python-based data analytics. Please note that there are some korean - my mother tongue - descriptions included.
To open the notebook, you need to setup your own Jupyter environment. The easiest and suggested way is installing Anacona pacakge. Please refer the anaconda web page (https://docs.anaconda.com/anaconda/install/) and install the package and start a jupyter environment - jupyter notebook or jupyter lab.
After then, you can download (or clone) this Github into your local machine. If you know how to use Git, then please clone this project to your Jupyter ntoebook directory. Otherwise, download this project as a zip file, and deflate the file into your Jupyter notebook directory. (Note that you need to understand the meaning of Jupyter notebook directory.)
Below are the topics for 10 sessions. (NOTE that 8th session on which no materias are included here)
- Jupyter basic + Python Basic
- Pandas Basic (+ tip: Folium)
- Advanced Pandas (+ tip: pandas_profiling)
- Python Modules : frequently used modules
- Natual Language analysis : POS, Similarity, Sentiment Analysis, Vectorization, Document Clustering NOTE: The content is mainly for Korean language.
- Time-Series analysis (Stock Trend) - Basic Korea Stock Data Acquisiation, Regression, Stock Trend Prediction
- Time-Series analysis (Stock Trend) - Advanced ARIMA prediction, (Stock) Technical Analysis Library, Linear Regression
- ( Machine Learning : no materials here)
- Machien Learning Example : Vehicle License Plate Recognition show an example ML project 10.Wraup : summary on what we have discussed, and relevant websites for further study
Any questions or comment, please send it to [email protected]