Search
- Filter Results
- Location
- Classification
- Include attachments
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/15%3A_Data_Science/15.06%3A_Chapter_SummaryThis page provides an overview of data science fundamentals, highlighting its multidisciplinary nature and lifecycle, which involves data acquisition, exploration, analysis, and reporting. It introduc...This page provides an overview of data science fundamentals, highlighting its multidisciplinary nature and lifecycle, which involves data acquisition, exploration, analysis, and reporting. It introduces key Python libraries, including NumPy for numerical tasks and Pandas for data management. The importance of Exploratory Data Analysis (EDA) and data visualization techniques is emphasized, along with functions for data structure manipulation in Python.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/11%3A_Appendix/11.01%3A_Appendix_B-_Review_of_R_Studio_for_Data_ScienceThis page discusses R, an open-source statistical tool favored in data science for data exploration and visualization. It covers basic commands for creating visualizations like scatter plots and perfo...This page discusses R, an open-source statistical tool favored in data science for data exploration and visualization. It covers basic commands for creating visualizations like scatter plots and performing statistical analyses such as correlation and regression, using examples related to S&P 500 stock returns and other datasets.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/15%3A_Data_Science/15.01%3A_Introduction_to_Data_ScienceThis page provides an overview of data science, detailing its definition, lifecycle stages (data acquisition, exploration, analysis, reporting), and essential tools (Python, R, Jupyter Notebook, Googl...This page provides an overview of data science, detailing its definition, lifecycle stages (data acquisition, exploration, analysis, reporting), and essential tools (Python, R, Jupyter Notebook, Google Colaboratory, Kaggle Kernels, Microsoft Excel). It also includes practical exercises for using Google Colaboratory, highlighting the significance of these tools in data analysis and visualization.
