DataScience VS cheatsheets

For all topics that explained briefly, I provided the links with more thorough documentation. In addition, I would highly recommend reading the Julia Data Science online book and learn the great set of machine learning examples in Julia Academy Data Science GitHub repository.

So, I just decided to try to learn Julia, and started by following the Julia for DataScience lectures on JuliaAcademy. In the first lecture, I get instructed to clone the DataScience repository on GitHub. According to instructions, I activated the environment with activate and check the status (status). I then ran instantiate to update any necessary packages, and get the following error message:

Matplotlib - A Python 2D plotting library.

Data visualization: utilizing Python's Matplotlib for visualizing order book information.

Name: matplotlib Version: 3.7.1 Summary: Python plotting package Home-page: https://matplotlib.org Author: John D. Hunter, Michael Droettboom Author-email: [email protected] License: PSFLocation: /home/huinker/.local/lib/python3.10/site-packages

Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses without relying on expensive or proprietary software.

programming language: basic python, pandas, matplotlib -- you'll probably do these in school, but if not https://cs50.harvard.edu/python/2022/ https://matplotlib.org/

Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.

Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.