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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/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/Mechanical_Engineering/Mechanics_of_Materials_(Roylance)/06%3A_Yield_and_Fracture/6.03%3A_Statistics_of_FractureThis page emphasizes the statistical analysis of fracture in high-strength materials, crucial for design related to human safety. It introduces key statistical measures, distribution functions (normal...This page emphasizes the statistical analysis of fracture in high-strength materials, crucial for design related to human safety. It introduces key statistical measures, distribution functions (normal and Weibull), and the importance of repeated measurements to reduce uncertainty in strength values. The t-distribution is highlighted for small samples, and connections between specimen volume and failure probability are discussed.
- https://eng.libretexts.org/Courses/Arkansas_Tech_University/Discrete-Time_Signal_Processing/01%3A_Introduction_to_Digital_Signal_ProcessingThis page discusses the differences between analog and digital signals, underscoring how digital signal processing (DSP) incorporates analog techniques while providing benefits such as faster computat...This page discusses the differences between analog and digital signals, underscoring how digital signal processing (DSP) incorporates analog techniques while providing benefits such as faster computations and programmability through computers. It highlights DSP's flexibility and applications in various fields, particularly in medicine and scientific analysis, with an example of analyzing sunspot data to identify cyclical patterns using power spectral density.
- 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.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/15%3A_Data_Science/15.02%3A_NumPyThis page details the NumPy library's learning objectives and features for numerical operations on multi-dimensional arrays in Python. It explains creating ndarray objects and covers mathematical func...This page details the NumPy library's learning objectives and features for numerical operations on multi-dimensional arrays in Python. It explains creating ndarray objects and covers mathematical functions, array manipulation, and linear algebra. Included are practice questions and references to the NumPy user guide, while encouraging practice through Google Colaboratory.
