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- 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)/02%3A_Collecting_and_Preparing_Data/2.03%3A_Web_Scraping_and_Social_Media_Data_CollectionThis page provides an overview of web scraping and social media data collection, emphasizing Python techniques like web crawling, XPath, and APIs for data extraction. It introduces libraries such as P...This page provides an overview of web scraping and social media data collection, emphasizing Python techniques like web crawling, XPath, and APIs for data extraction. It introduces libraries such as Pandas, Beautiful Soup, and NLTK for data manipulation. The text also covers natural language processing with SpaCy and the use of regular expressions for text parsing.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/01%3A_What_Are_Data_and_Data_Science/1.09%3A_Critical_ThinkingThis page details tasks involving the Spotify and CancerDoc datasets, focusing on attributes, data type classification, entry identification, and data visualization using scatterplots and Python Panda...This page details tasks involving the Spotify and CancerDoc datasets, focusing on attributes, data type classification, entry identification, and data visualization using scatterplots and Python Pandas. It highlights the necessity of comprehending each dataset's characteristics and offers guidance for conducting analysis and visualization tasks.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/15%3A_Data_Science/15.03%3A_PandasThis page provides an overview of the Pandas library, a powerful Python tool for data cleaning and analysis, detailing its main data structures: Series and DataFrame. It highlights key functions like ...This page provides an overview of the Pandas library, a powerful Python tool for data cleaning and analysis, detailing its main data structures: Series and DataFrame. It highlights key functions like `info()`, `describe()`, `value_counts()`, and `unique()`, which facilitate data exploration and summary. Examples are given for creating DataFrames from various sources. The text also includes practice questions and recommends consulting the Pandas user guide for further learning.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/07%3A_Modules/7.05%3A_Finding_ModulesThis page outlines the Python Standard Library, highlighting built-in modules with over 200 standard functionalities, and third-party modules available on the Python Package Index (PyPI) for community...This page outlines the Python Standard Library, highlighting built-in modules with over 200 standard functionalities, and third-party modules available on the Python Package Index (PyPI) for community sharing. It provides examples like `math`, `datetime`, and `requests`, while encouraging hands-on activities, such as calculating age with the `datetime` module, to reinforce learning.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/01%3A_What_Are_Data_and_Data_Science/1.05%3A_Data_Science_with_PythonThis page covers the process of loading and analyzing data in Python using Jupyter Notebook and Google Colaboratory. It focuses on essential libraries like Pandas for data manipulation, including hand...This page covers the process of loading and analyzing data in Python using Jupyter Notebook and Google Colaboratory. It focuses on essential libraries like Pandas for data manipulation, including handling CSV files and using DataFrames and Series. The text explains filtering data and visualizing it with Matplotlib, providing examples with the Iris dataset and movie profits.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/09%3A_Visualizing_Data/9.04%3A_Geospatial_and_Heatmap_Data_Visualization_Using_PythonThis page discusses learning objectives involving geospatial data visualization in Python, detailing spatial heatmaps, GIS mapping features, and the use of Pandas and Geopandas for data manipulation. ...This page discusses learning objectives involving geospatial data visualization in Python, detailing spatial heatmaps, GIS mapping features, and the use of Pandas and Geopandas for data manipulation. It covers heatmap creation with Matplotlib and Seaborn, the collection and processing of geospatial data, and the significance of interactive mapping for data interpretation.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/16%3A_Answer_Key/16.14%3A_Chapter_15This page outlines key concepts in data science, emphasizing the four stages of the data science life cycle: data acquisition, exploration, analysis, and reporting. It highlights tools like Python and...This page outlines key concepts in data science, emphasizing the four stages of the data science life cycle: data acquisition, exploration, analysis, and reporting. It highlights tools like Python and libraries such as NumPy and Pandas, focusing on numerical analysis and data manipulation.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/15%3A_Data_ScienceThis page outlines a chapter on data science, detailing its structure and covering topics including an introduction to data science, essential libraries like NumPy and Pandas, methods for exploratory ...This page outlines a chapter on data science, detailing its structure and covering topics including an introduction to data science, essential libraries like NumPy and Pandas, methods for exploratory data analysis, data visualization, and concludes with a summary.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/01%3A_What_Are_Data_and_Data_Science/1.06%3A_Key_TermsThis page offers definitions for key data science terms, covering data types, data management concepts, tools, and analytical methods. It emphasizes the significance of data analysis for insights and ...This page offers definitions for key data science terms, covering data types, data management concepts, tools, and analytical methods. It emphasizes the significance of data analysis for insights and informed decision-making, mentioning essential programming languages like Python and R, and outlining the data science cycle.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/01%3A_What_Are_Data_and_Data_Science/1.10%3A_Quantitative_ProblemsThis page provides a guide on calculating the average beats per minute of 2023 songs using Python Pandas and spreadsheet programs like MS Excel or Google Sheets. It explains the AVERAGE() function in ...This page provides a guide on calculating the average beats per minute of 2023 songs using Python Pandas and spreadsheet programs like MS Excel or Google Sheets. It explains the AVERAGE() function in Excel with an example for clarity.
