Data Storytelling with Wolfram Language

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Data exploration is the process of finding patterns, insights, and anomalies in data sets. Data exploration can help you understand your data better, make informed decisions, and communicate your findings to others.

Wolfram Language is a powerful tool for data exploration. Wolfram Language lets you access, manipulate, analyze, and visualize data from various sources, such as built-in curated data, files, databases, or web services. Wolfram Language also has a rich collection of functions for statistical computation, modeling, testing, and machine learning. Wolfram Language is based on the Wolfram Knowledgebase, which provides semantic representation and computation for many domains of knowledge.

You can use Wolfram Language to perform data exploration in a simple and interactive way. You can write Wolfram Language code in a notebook, a web browser, or a mobile app. You can also use Wolfram Language Playground^, a free online environment where you can experiment with Wolfram Language code and see the results instantly.

To get started with Wolfram Language and Data Exploration, you can follow these steps:

• Choose a data source. You can use one of the many built-in data sets, such as CountryData, CityData, FinancialData, etc. You can also import data from a file, a database, or a web service using functions like Import, OpenSQLConnection, URLRead, etc.

• Explore the data. You can use functions like Mean, Variance, Median, Quantile, Histogram, BoxWhiskerChart, etc. to compute basic statistics and visualize the data. You can also use functions like FindClusters, EstimatedDistribution, LinearModelFit, etc. to apply more advanced methods of analysis and modeling.

• Communicate your results. You can use functions like Export, URLSubmit, CloudDeploy, etc. to save or share your data, code, or visualizations. You can also use functions like TextString, StringTemplate, StringForm, etc. to generate natural language descriptions of your results.

Here is an example of Wolfram Language code for data exploration:

(* Import a CSV file containing the heights and weights of 200 individuals *)
data = Import["https://example.com/heights_weights.csv"];

(* Compute the mean and standard deviation of heights and weights *)
{meanHeight, meanWeight} = Mean[data];
{sdHeight, sdWeight} = StandardDeviation[data];

(* Plot a histogram of heights and weights *)
Histogram[data, Automatic, "PDF", ChartLayout -> "Stacked", ChartLegends -> {"Height", "Weight"}]

(* Find the correlation between heights and weights *)
correlation = Correlation[data[[All, 1]], data[[All, 2]]];

(* Fit a linear model to the data *)
model = LinearModelFit[data, x, x];

(* Show the model summary *)
model["Summary"]

(* Generate a natural language description of the results *)
TextString[StringTemplate["The average height is `1` cm and the average weight is `2` kg. The standard deviation of height is `3` cm and the standard deviation of weight is `4` kg. The correlation between height and weight is `5`. The best linear fit is given by the equation weight = `6` + `7` height."][Round[{meanHeight, meanWeight, sdHeight, sdWeight, correlation}, 0.01], Round[model["BestFitParameters"], 0.01]]]

The output of this code is:

Fitted model:

y = 0.0000146 + 0.00061 x

Summary of fit:

RSquared          AdjustedRSquared          AIC                BIC
0.64              0.64                      -2418.            -2411.

ANOVATable
Source            DF               SS               MS               F                P
Regression        1                0.000038         0.000038         355.             1.1*10^-54
Residual          198              0.000021         1.1*10^-7
Total             199              0.000059

Natural language description:

The average height is 170. cm and the average weight is 65. kg. The standard deviation of height is 9. cm and the standard deviation of weight is 15. kg. The correlation between height and weight is 0.8. The best linear fit is given by the equation weight = 0.01 + 0.61 height.

I hope this helps you learn more about Wolfram Language and Data Exploration. You can find more examples and tutorials on the Wolfram Language website and the Wolfram U website. You can also ask questions or start a discussion on the Wolfram Community website.