15 - Day 6 Data Visualization with Matplotlib and Seaborn 27:02 16 - Day 7 Exploratory Data Analysis EDA Project 23:09 17 - Introduction to Week 3 Mathematics for Machine Learning 00:43 18 - Day 1 Linear Alg
After processing your data, exploratory data analysis is the next step. For example, we can calculate the mean of a vector with mean() and the standard deviation with sd(); however, without specifying additional arguments, they will not work with data containing missing values (NA). By addin...
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When working with real-world data,incorrect data typesare a frequent hurdle. Dates might be stored as strings, categories as integers, or booleans as text. These data types can complicate analysis and visualization. Strategies: Convertcolumns to appropriate types as early as possible. For example,...
Another useful type of categorical data is ordinal data in which the categories are ordered; an example of this is a numerical rating (1, 2, 3, 4, or 5). Why do we bother with a taxonomy of data types? It turns out that for the purposes of data analysis and predictive modeling, th...
normality of a data set, as many statistical tests have the assumption that the exposure variables are approximately normally distributed. These plots are also used to examine residuals in models that rely on the assumption of normality of the residuals (ANOVA or regression analysis for example). ...
For example, split the line to work with a street segment about 250 meters long. Click Line of Sight from the Exploratory 3D Analysis drop-down list of tools in the Workflows group on the Analysis tab. The tool is now active and the Exploratory Analysis pane appears. On the Create tab,...
Aygaz Data Analyst Bootcamp Project 📋 Project Description This project is a study developed as part of the Aygaz Data Analyst Bootcamp. It aims to derive meaningful insights through data analysis and visualization. The steps of data preprocessing, exploratory data analysis (EDA), and visualization...
Data Product:Any computer software that uses data as inputs, produces outputs, and provides feedback based on the output to control the environment is referred to as a data product. A data product is generally based on a model developed during data analysis, for example, a recommendation model...
Example Percentile statisticdef globalUniqueCustomersCounterStats(transactions: RDD[Transaction], nPercentiles: Int = 1001) = Some("Global distinct Customers") .map(n => PrettyPercentileStats( name = n, levels = SequenceStats.distinct[Transaction, Long, Long](data = AppLogger.logStage(transactions,...