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- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/01%3A_What_Are_Data_and_Data_Science/1.07%3A_Group_ProjectThis page outlines three projects aimed at enhancing data science skills for students and professionals. Project A focuses on finding and cleaning secondary data while analyzing datasets relevant to s...This page outlines three projects aimed at enhancing data science skills for students and professionals. Project A focuses on finding and cleaning secondary data while analyzing datasets relevant to specific policies. Project B involves downloading a dataset, formulating questions, and visualizing results using Python.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/05%3A_Time_Series_and_Forecasting/5.03%3A_Time_Series_Forecasting_MethodsThis page covers time series analysis techniques, including decomposition into trends, seasonal and cyclic variations, and noise. It elaborates on methods like Simple Moving Averages (SMA), Exponentia...This page covers time series analysis techniques, including decomposition into trends, seasonal and cyclic variations, and noise. It elaborates on methods like Simple Moving Averages (SMA), Exponential Moving Averages (EMA), and ARIMA models for forecasting and illustrates their applications using Python and Excel, particularly on datasets like the S&P 500 Index and U.S. coal consumption.
- 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)/09%3A_Visualizing_Data/9.00%3A_IntroductionThis page discusses various data visualization techniques for univariate data, including boxplots, histograms, and Pareto charts. It highlights the importance of using Python and Matplotlib for creati...This page discusses various data visualization techniques for univariate data, including boxplots, histograms, and Pareto charts. It highlights the importance of using Python and Matplotlib for creating these visualizations, providing examples like employee salaries and baseball player ages. The section also covers methods for analyzing distributions, such as identifying skewness and bimodal patterns.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/01%3A_What_Are_Data_and_Data_Science/1.01%3A_What_Is_Data_ScienceThis page outlines data science objectives and processes, emphasizing the data science cycle, which includes problem definition, data collection, preparation, analysis, and reporting. It highlights th...This page outlines data science objectives and processes, emphasizing the data science cycle, which includes problem definition, data collection, preparation, analysis, and reporting. It highlights the importance of efficient data management and communication, particularly with cloud systems and data visualization, to convey insights effectively.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/05%3A_Time_Series_and_Forecasting/5.07%3A_Critical_ThinkingThis page discusses time series data characteristics with examples like monthly expenses and weather measurements. It encourages trend analysis of a graph and the identification of a moving average tr...This page discusses time series data characteristics with examples like monthly expenses and weather measurements. It encourages trend analysis of a graph and the identification of a moving average trendline from provided data. Additionally, it introduces error measures such as MAE, RMSE, and MAPE, urging the identification of the most and least accurate models based on these metrics, while also exploring the reasons for their accuracy.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/15%3A_Data_Science/15.05%3A_Data_VisualizationThis page highlights the significance of data visualization in data science, discussing various visualization types like bar plots and scatter plots, each suited for specific analysis. It underscores ...This page highlights the significance of data visualization in data science, discussing various visualization types like bar plots and scatter plots, each suited for specific analysis. It underscores visualization's role in data exploration, trend identification, and reporting within the data science life cycle.
- 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)/05%3A_Time_Series_and_Forecasting/5.01%3A_Introduction_to_Time_Series_AnalysisThis page provides an overview of time series data, highlighting its significance in forecasting across fields like business and healthcare. It covers the analysis process, related forecasting methods...This page provides an overview of time series data, highlighting its significance in forecasting across fields like business and healthcare. It covers the analysis process, related forecasting methods from naïve to linear regression, and the importance of validating model accuracy. It also details the visualization of S&P 500 time series data in Python using Matplotlib, emphasizing clarity with appropriate formatting and grid lines while referencing Excel for additional context.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/06%3A_Decision-Making_Using_Machine_Learning_Basics/6.03%3A_Machine_Learning_in_Regression_AnalysisThis page covers multiple regression techniques, focusing on both multiple linear and logistic regression. It outlines the assumptions necessary for multiple linear regression and demonstrates the ana...This page covers multiple regression techniques, focusing on both multiple linear and logistic regression. It outlines the assumptions necessary for multiple linear regression and demonstrates the analysis process, including parameter estimation and significance evaluation, using Python implementations with NCAA basketball statistics. Additionally, it discusses bootstrapping to assess the variability of regression estimates. The text illustrates model building, achieving an R-squared score of 0.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/08%3A_Ethics_Throughout_the_Data_Science_Cycle/8.05%3A_Group_ProjectThis page discusses various projects emphasizing ethical practices in data handling and representation. The WWF analyzed 2020 temperature data for trends, focusing on ethical data presentation. School...This page discusses various projects emphasizing ethical practices in data handling and representation. The WWF analyzed 2020 temperature data for trends, focusing on ethical data presentation. School projects assessed cafeteria food quality while considering privacy and bias. Additionally, research on increasing ransomware attacks led to discussions on data predictability and protective measures for organizations.
