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It examines the design and implementation of learning experiences based on constructivist pedagogy and their effectiveness in producing conceptual understanding of the measurement of slope as a ratio. Here are links to interactive interfaces of the TSSEs hosted (and publicly available) at shinyapps.This study investigates secondary mathematics students‚ÄövÑv¥ learning of slope concepts through a social constructivist teaching approach. One TSSE is based on Apple Mobility Trends data, the other on The New York Times COVID-19 data.

RANGE MATHEMATICA SERIES

In this article we proclaim the preparation and availability of interactive interfaces to two Time Series Search Engines (TSSEs) over COVID-19 data. Get the data of all crypto-currencies in : In this section we get all crypto-currencies symbols and related metadata. Yahoo! Finance Getting cryptocurrencies symbols and summaries

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This means the code has to be changed if the underlying data organization in is changed. Remark: The code below is made with certain ad-hoc inductive reasoning that brought meaningful results. Another easy to work with Bitcoin-only data source is. There are multiple crypto-currencies data sources, but a small proportion of them give a convenient way of extracting crypto-currencies data automatically. We assume the described data acquisition workflow is useful for doing more detailed (exploratory) analysis.

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In this notebook we show how to obtain crypto-currencies data from several data sources and make some basic time series visualizations. Anton Antonov, QuantileRegression, (2019). Anton Antonov, IndependentComponentAnalysis, (2019). Anton Antonov, ParetoPrinciplePlot, (2019). Anton Antonov, CryptocurrencyData, (2021). Anton Antonov, Heatmap plot Mathematica package, (2018), MathematicaForPrediciton at GitHub.

Anton Antonov, Data reshaping Mathematica package, (2018), MathematicaForPrediciton at GitHub. Anton Antonov, “Apple mobility trends data visualization”, (2020), SystemModeling at GitHub. Anton Antonov, “NY Times COVID-19 data visualization”, (2020), SystemModeling at GitHub. Anton Antonov, “Crypto-currencies data acquisition with visualization”, (2021), MathematicaForPrediction at WordPress. LsEventData =, PlotRange -> All, AspectRatio -> 1 / 4, ImageSize -> Large ], grGrid ] ] CryptocurrencyPlot := $Failed References Articles Here are all cryptocurrencies we have data for: There is a dedicated resource object CrypocurrencyData that provides the cryptocurrency data and related documentation.

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The procedure of obtaining the cryptocurrencies data is explained in detail in. The cryptocurrencies data used in this notebook is obtained from found in Yahoo Finance. We only discuss financial time series data. Remark: Note that in this notebook we do not discuss philosophical, macro-economic, and environmental issues with cryptocurrencies.

Those two articles deal with COVID-19 time series.

The points 6 and 7 follow similar explorations (and code) described in.

Links are provided in the corresponding dataset.

The cryptocurrency events data, point 3, is taken from different news sites.

There is a dedicated resource object CrypocurrencyData that provides cryptocurrency data and related documentation.

The procedure of obtaining the cryptocurrencies data, point 1, is explained in detail in.

Here are some details for the steps above:

Find clusters of cryptocurrencies based on time series correlations.

Find “global” correlations between the different cryptocurrencies.

Make observations and conjectures over the plots.

Plot together cryptocurrency prices and trading volume time series together with the events.

Do basic data analysis over suitable date ranges.

Here is the work plan followed in this notebook: The “landscape” we consider consists of price action and trading volume time series for cryptocurrencies found in Yahoo Finance. The main goal of this notebook is to provide some basic views and insights into the landscape of cryptocurrencies.