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Bastian Greshake Tzovaras

I'll give a little bit of the history to provide context. My own involvement in this started around 2008 after we had shipped our key-value store. My next project was to try to get a working Hadoop setup going, and move some of our recommendation processes there. Having little experience in this area, we naturally budgeted a few weeks for getting data in and out, and the rest of our time for implementing fancy prediction algorithms. So began a long slog. We originally planned to just scrape the data out of our existing Oracle data warehouse. The first discovery was that getting data out of Oracle quickly is something of a dark art. Worse, the data warehouse processing was not appropriate for the production batch processing we planned for Hadoop—much of the processing was non-reversable and specific to the reporting being done. We ended up avoiding the data warehouse and going directly to source databases and log files. Finally, we implemented another pipeline to load data into our key-value store for serving results. This mundane data copying ended up being one of the dominate items for the original development. Worse, any time there was a problem in any of the pipelines, the Hadoop system was largely useless—running fancy algorithms on bad data just produces more bad data. Although we had built things in a fairly generic way, each new data source required custom configuration to set up. It also proved to be the source of a huge number of errors and failures. The site features we had implemented on Hadoop became popular and we found ourselves with a long list of interested engineers. Each user had a list of systems they wanted integration with and a long list of new data feeds they wanted. ETL in Ancient Greece. Not much has changed.

Effective use of data follows a kind of Maslow's hierarchy of needs. The base of the pyramid involves capturing all the relevant data, being able to put it together in an applicable processing environment (be that a fancy real-time query system or just text files and python scripts). This data needs to be modeled in a uniform way to make it easy to read and process. Once these basic needs of capturing data in a uniform way are taken care of it is reasonable to work on infrastructure to process this data in various ways—MapReduce, real-time query systems, etc. It's worth noting the obvious: without a reliable and complete data flow, a Hadoop cluster is little more than a very expensive and difficult to assemble space heater. Once data and processing are available, one can move concern on to more refined problems of good data models and consistent well understood semantics. Finally, concentration can shift to more sophisticated processing—better visualization, reporting, and algorithmic processing and prediction. In my experience, most organizations have huge holes in the base of this pyramid—they lack reliable complete data flow—but want to jump directly to advanced data modeling techniques. This is completely backwards. So the question is, how can we build reliable data flow throughout all the data systems in an organization?

Data integration is making all the data an organization has available in all its services and systems.

1. Introduction to eXtensible Time Series, using xts and zoo for time series Introducing xts and

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Second, uBlock Origin does not have a dedicated server, it can't "phone home" with your browsing data, there is only GitHub, and GitHub is completely unrelated to uBlock Origin.

Is there a home server?

TrackMeNot is user-installed and user-managed, residing wholly on users' system and functions without the need for 3rd-party servers or services. Placing users in full control is an essential feature of TrackMeNot, whose purpose is to protect against the unilateral policies set by search companies in their handling of our personal information.

What’s the Difference Between AI, Machine Learning and Data Science?

landscape vs. portrait.

how to nest grids inside grids using a Master / Detail configuration

Google has confirmed that it partnered with health heavyweight Ascension, a Catholic health care system based in St. Louis that operates across 21 states and the District of Columbia.

Speaking with MIT Technology Review, Rohit Prasad, Alexa’s head scientist, has now revealed further details about where Alexa is headed next. The crux of the plan is for the voice assistant to move from passive to proactive interactions. Rather than wait for and respond to requests, Alexa will anticipate what the user might want. The idea is to turn Alexa into an omnipresent companion that actively shapes and orchestrates your life. This will require Alexa to get to know you better than ever before.

SEL measurement is being done in myriad ways, involving multiple different conceptualizations of SEL, different political positions, and different sectoral interests.

This booklet itells you how to use the R statistical software to carry out some simple analyses that are common in analysing time series data.

"While we hope that Google will lift these unwarranted sanctions for AdNauseam, it highlights a much more serious problem for Chrome users," the AdNauseam team adds. "It is frightening to think that at any moment Google can quietly make your extensions and data disappear, without so much as a warning."

"Element" SelectorsEach component has a data-reach-* attribute on the underlying DOM element that you can think of as the "element" for the component.

Data Scientist

I frequently talk with people who are not that concerned about surveillance, or who feel that the positives outweigh the risks. Here, I want to share some important truths about surveillance: Surveillance can facilitate human rights abuses and even genocide Data is often used for different purposes than why it was collected Data often contains errors Surveillance typically operates with no accountability Surveillance changes our behavior Surveillance disproportionately impacts the marginalized Data privacy is a public good We don’t have to accept invasive surveillance

Terminar los proyectos que empezamos en 2019, con prioridad en Documentatón, ya que no es un cover, sino que es nuestro propio libro.

Selección comunitaria de temas para Data Weeks o Data Rodas. Apoyo de proyectos de los participantes de la comunidad. Reuniones periódicas de la Comunidad, algo así como Data Roda el primer viernes de cada mes, así sea para saludarnos síncronamente y ver en qué andamos y hacer un encera - brilla de bacanes.

Si puedo aportar una herramienta más a Grafoscopio, quiero que sea la querendura.

if n is very small (for example n = 3), rather than showing error bars and statistics, it is better to simply plot the individual data points.

Keep the ergonomics of stable reference and directly mutable objects. In other words; be able to have a variable pointing to an object, and make subsequent reads or writes to it. Without needing to fear that you’re working with old data. While, in the background,..State is stored in an immutable, structurally shared tree.

With MobX you don't need to normalize your data.

the absence of a social contract

“But then again,” a person who used information in this way might say, “it’s not like I would be deliberately discriminating against anyone. It’s just an unfortunate proxy variable for lack of privilege and proximity to state violence.

More conspicuously, since Trump’s election, the RNC — at his campaign’s direction — has excluded critical “voter scores” on the president from the analytics it routinely provides to GOP candidates and committees nationwide, with the aim of electing down-ballot Republicans. Republican consultants say the Trump information is being withheld for two reasons: to discourage candidates from distancing themselves from the president, and to avoid embarrassing him with poor results that might leak. But they say its concealment harms other Republicans, forcing them to campaign without it or pay to get the information elsewhere.

Methodology To determine the link between heat and income in U.S. cities, NPR used NASA satellite imagery and U.S. Census American Community Survey data. An open-source computer program developed by NPR downloaded median household income data for census tracts in the 100 most populated American cities, as well as geographic boundaries for census tracts. NPR combined these data with TIGER/Line shapefiles of the cities.

On the other hand, a resource may be generic in that as a concept it is well specified but not so specifically specified that it can only be represented by a single bit stream. In this case, other URIs may exist which identify a resource more specifically. These other URIs identify resources too, and there is a relationship of genericity between the generic and the relatively specific resource.

Data Roda 34: Cambiar Juntos

Suspense

Material Design Material System Introduction Material studies About our Material studies Basil Crane Fortnightly Owl Rally Reply Shrine Material Foundation Foundation overview Environment Surfaces Elevation Light and shadows Layout Understanding layout Pixel density Responsive layout grid Spacing methods Component behavior Applying density Navigation Understanding navigation Navigation transitions Search Color The color system Applying color to UI Color usage Text legibility Dark theme Typography The type system Understanding typography Language support Sound About sound Applying sound to UI Sound attributes Sound choreography Sound resources Iconography Product icons System icons Animated icons Shape About shape Shape and hierarchy Shape as expression Shape and motion Applying shape to UI Motion Understanding motion Speed Choreography Customization Interaction Gestures Selection States Material Guidelines Communication Confirmation & acknowledgement Data formats Data visualization Principles Types Selecting charts Style Behavior Dashboards Empty states Help & feedback Imagery Launch screen Onboarding Offline states Writing Guidelines overview Material Theming Overview Implementing your theme Components App bars: bottom App bars: top Backdrop Banners Bottom navigation Buttons Buttons: floating action button Cards Chips Data tables Dialogs Dividers Image lists Lists Menus Navigation drawer Pickers Progress indicators Selection controls Sheets: bottom Sheets: side Sliders Snackbars Tabs Text fields Tooltips Usability Accessibility Bidirectionality Platform guidance Android bars Android fingerprint Android haptics Android icons Android navigating between apps Android notifications Android permissions Android settings Android slices Android split-screen Android swipe to refresh Android text selection toolbar Android widget Cross-platform adaptation Data visualization Data visualization depicts information in graphical form. Contents Principles Types Selecting charts Style Behavior Dashboards Principles Data visualization is a form of communication that portrays dense and complex information in graphical form. The resulting visuals are designed to make it easy to compare data and use it to tell a story – both of which can help users in decision making. Data visualization can express data of varying types and sizes: from a few data points to large multivariate datasets. AccuratePrioritize data accuracy, clarity, and integrity, presenting information in a way that doesn’t distort it. HelpfulHelp users navigate data with context and affordances that emphasize exploration and comparison. ScalableAdapt visualizations for different device sizes, while anticipating user needs on data depth, complexity, and modality. Types Data visualization can be expressed in different forms. Charts are a common way of expressing data, as they depict different data varieties and allow data comparison.The type of chart you use depends primarily on two things: the data you want to communicate, and what you want to convey about that data. These guidelines provide descriptions of various different types of charts and their use cases.Types of chartsChange over time charts show data over a period of time, such as trends or comparisons across multiple categories. Common use cases include: Category comparison...Read MoreChange over timeChange over time charts show data over a period of time, such as trends or comparisons across multiple categories.Common use cases include: Stock price performanceHealth statisticsChronologies Change over time charts include:1. Line charts 2. Bar charts 3. Stacked bar charts 4. Candlestick charts 5. Area charts 6. Timelines 7. Horizon charts 8. Waterfall charts Category comparisonCategory comparison charts compare data between multiple distinct categories. Use cases include: Income across different countriesPopular venue timesTeam allocations Category comparison charts include: 1. Bar charts 2. Grouped bar charts 3. Bubble charts 4. Multi-line charts 5. Parallel coordinate charts 6. Bullet charts RankingRanking charts show an item’s position in an ordered list.Use cases include: Election resultsPerformance statistics Ranking charts include: 1. Ordered bar charts 2. Ordered column charts 3. Parallel coordinate charts Part-to-wholePart-to-whole charts show how partial elements add up to a total.Use cases include: Consolidated revenue of product categoriesBudgets Part-to-whole charts include: 1. Stacked bar charts 2. Pie charts 3. Donut charts 4. Stacked area charts 5. Treemap charts 6. Sunburst charts CorrelationCorrelation charts show correlation between two or more variables.Use cases include: Income and life expectancy Correlation charts include: 1. Scatterplot charts 2. Bubble charts 3. Column and line charts 4. Heatmap charts DistributionDistribution charts show how often each values occur in a dataset. Use cases include: Population distributionIncome distribution Distribution charts include: 1. Histogram charts 2. Box plot charts 3. Violin charts 4. Density charts FlowFlow charts show movement of data between multiple states.Use cases include: Fund transfersVote counts and election results Flow charts include: 1. Sankey charts 2. Gantt charts 3. Chord charts 4. Network charts RelationshipRelationship charts show how multiple items relate to one other.Use cases includeSocial networksWord charts Relationship charts include: 1. Network charts 2. Venn diagrams 3. Chord charts 4. Sunburst charts Selecting charts Multiple types of charts can be suitable for depicting data. The guidelines below provide insight into how to choose one chart over another. Showing change over timeChange over time can be expressed using a time series chart, which is a chart that represents data points in chronological order. Charts that express...Read MoreChange over time can be expressed using a time series chart, which is a chart that represents data points in chronological order. Charts that express change over time include: line charts, bar charts, and area charts.Type of chartUsageBaseline value * Quantity of time seriesData typeLine chartTo express minor variations in dataAny valueAny time series (works well for charts with 8 or more time series)ContinuousBar chartTo express larger variations in data, how individual data points relate to a whole, comparisons, and rankingZero4 or fewerDiscrete or categoricalArea chartTo summarize relationships between datasets, how individual data points relate to a wholeZero (when there’s more than one series)8 or fewerContinuous* The baseline value is the starting value on the y-axis.Bar and pie chartsBoth bar charts and pie charts can be used to show proportion, which expresses a partial value in comparison to a total value. Bar charts,...Read MoreBoth bar charts and pie charts can be used to show proportion, which expresses a partial value in comparison to a total value. Bar charts express quantities through a bar’s length, using a common baselinePie charts express portions of a whole, using arcs or angles within a circleBar charts, line charts, and stacked area charts are more effective at showing change over time than pie charts. Because all three of these charts share the same baseline of possible values, it’s easier to compare value differences based on bar length. Do.Use bar charts to show changes over time or differences between categories. Don’t.Don’t use multiple pie charts to show changes over time. It’s difficult to compare the difference in size across each slice of the pie. Area chartsArea charts come in several varieties, including stacked area charts and overlapped area charts: Overlapping area charts are not recommended with more than two time...Read MoreArea charts come in several varieties, including stacked area charts and overlapped area charts:Stacked area charts show multiple time series (over the same time period) stacked on top of one another Overlapped area charts show multiple time series (over the same time period) overlapping one anotherOverlapping area charts are not recommended with more than two time series, as doing so can obscure the data. Instead, use a stacked area chart to compare multiple values over a time interval (with time represented on the horizontal axis). Do.Use a stacked area chart to represent multiple time series and maintain a good level of legibility. Don’t.Don’t use overlapped area charts as it obscures data values and reduces readability. Style Data visualizations use custom styles and shapes to make data easier to understand at a glance, in ways that suit the user’s needs and context.Charts can benefit from customizing the following: Graphical elementsTypographyIconographyAxes and labelsLegends and annotationsStyling different types of dataVisual encoding is the process of translating data into visual form. Unique graphical attributes can be applied to both quantitative data (such as temperature, price,...Read MoreVisual encoding is the process of translating data into visual form. Unique graphical attributes can be applied to both quantitative data (such as temperature, price, or speed) and qualitative data (such as categories, flavors, or expressions). These attributes include:ShapeColorSizeAreaVolumeLengthAnglePosition DirectionDensityExpressing multiple attributesMultiple visual treatments can be applied to more than one aspect of a data point. For example, a bar color can represent a category, while a bar’s length can express a value (like population size). Shape can be used to represent qualitative data. In this chart, each category is represented by a specific shape (circles, squares, and triangles), which makes it easy to compare data both within a specific range or against other categories. ShapeCharts can use shapes to display data in a range of ways. A shape can be styled as playful and curvilinear, or precise and high-fidelity,...Read MoreCharts can use shapes to display data in a range of ways. A shape can be styled as playful and curvilinear, or precise and high-fidelity, among other ways in between. Level of shape detailCharts can represent data at varying levels of precision. Data intended for close exploration should be represented by shapes that are suitable for interaction (in terms of touch target size and related

Security Issues, Dangers And Implications Of Smart Systems

Indigenous Data Sovereignty

Every time your child opens the email, that person knows generally where they are (or specifically, if they have other info to triangulate against).

In contrast to such pseudonymous social networking, Facebook is notable for its longstanding emphasis on real identities and social connections.

our sum of squares is 41.187941.187941.1879

it acts as PCA quantitative variables and as MCA for qualitative variables.

in clustering analyses, standardization may be especially crucial in order to compare similarities between features based on certain distance measures. Another prominent example is the Principal Component Analysis, where we usually prefer standardization over Min-Max scaling, since we are interested in the components that maximize the variance

As a rule of thumb I’d say: When in doubt, just standardize the data, it shouldn’t hurt.

driven by data—where schools use data to identify a problem, select a strategy to address the problem, set a target for improvement, and iterate to make the approach more effective and improve student achievement.

Balloon plot

how the features are all on the same relative scale. The relative spaces between each feature’s values have been maintained.

many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the l1 and l2 regularizers of linear models) assume that all features are centered around zero and have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.

4ExportardatosdesdeTwitter

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missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random.

Success ina data science project comes not from access to any one exotic tool, but from having quantifiablegoals, good methodology, crossdiscipline interactions, and a repeatable workflow.

Academicsarealsoatfaulthere:arecentanalysisof29millionpapersinover15,000peer-reviewedtitlespublishedaroundthetimeoftheZikaandEbolaepidemicsfoundthatlessthan1%exploredthegenderedimpactoftheoutbreaks

However, this doesn’t mean that Min-Max scaling is not useful at all! A popular application is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.

The result of standardization (or Z-score normalization) is that the features will be rescaled so that they’ll have the properties of a standard normal distribution with μ=0μ=0\mu = 0 and σ=1σ=1\sigma = 1 where μμ\mu is the mean (average) and σσ\sigma is the standard deviation from the mean

Threshold values of 0.8-0.9 are recommended for well separated clusters; to allow for overlapping clusters, we chose a threshold of 0.6.

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Virtually all BPMs have utilities for creating simple, data-gathering forms. And in many types of workflows, these simple forms may be adequate. However, in any workflow that includes complex document assembly (such as loan origination workflows), BPM forms are not likely to get the job done. Automating the assembly of complex documents requires ultra-sophisticated data-gathering forms, which can only be designed and created after the documents themselves have been automated. Put another way, you won't know which questions need to be asked to generate the document(s) until you've merged variables and business logic into the documents themselves. The variables you merge into the document serve as question fields in the data gathering forms. And here's the key point - since you have to use the document assembly platform to create interviews that are sophisticated enough to gather data for your complex documents, you might as well use the document assembly platform to generate all data-gathering forms in all of your workflows.

El ritmo de las actividades de diseño e instalación de redes comunitarias en veredas del municipio de Fusagasugá se ve acrecentado por las convocatorias internas de investigación de la Universidad de Cundinamarca que a lo largo del tiempo de vida de Red FusaLibrehan sido un músculo financiero que les permite acelerar los proc

Chile has developed from 1990 to date, a considerable decrease in poverty rates, which fell from 40.5% in 1990 to 8.5% in 2015.