datapine

MAY 14, 2019

In 2013, less than 0.5% We gave you a curated list of our top 15 data analytics books , top 18 data visualization books , top 16 SQL books – and, as promised, we’re going to tell you all about the world’s best books on data science. Why You Need To Read Data Science Books. of all available data was analyzed, used, and understood.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Figure 4: Visualization of a central composite design. Journal of Statistical Software, 56(1):1-56, 2014. [5] Hedayat, N.J.A.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

O'Reilly on Data

DECEMBER 12, 2019

Partial dependence, accumulated local effect (ALE), and individual conditional expectation (ICE) plots : this involves systematically visualizing the effects of changing one or more variables in your model. 1] “All models are wrong, but some are useful.” — George Box, Statistician (1919 – 2013). [2] If so, have fun debugging! [1]

Machine Learning 196

Machine Learning Modeling Testing Risk Management 196

Sisense

DECEMBER 11, 2019

And he explained that the leap to visual-based data discovery put analytics in the hands of data experts working in different lines of business, rather than in IT, where bottlenecks could previously occur, and reporting could take much longer. Kongregate has been using Periscope Data since 2013.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

Domino Data Lab

JANUARY 6, 2019

I’ve been teaching data science since 2008 privately for employers – exec staff, investors, IT teams, and the data teams I’ve led – and since 2013, for industry professionals in general. See the 2013 article in Nature , “ MOOCs taken by educated few, ” and also some extended discussion at an ACM meetup on the topic.

Data Science 56

Data Science Machine Learning Visualization Reporting 56

Domino Data Lab

JULY 9, 2019

.” And this is one of his papers about “you’re doing it wrong” where he talked about the algorithmic culture that he was observing in the machine learning community versus the generative model community that was more traditional in statistics. When I showed up in 2013…there was pain. And it works.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. Human brains are not well suited to visualizing anything in greater than three dimensions. Visualizing data using t-SNE.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Insight

MARCH 12, 2020

In 2013, Robert Galbraith?—?an The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. an aspiring author?—?finished finished his first novel, Cuckoo’s Calling. often without even looking at it. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

Ontotext

MARCH 24, 2021

For these reasons, we have applied semantic data integration and produced a coherent knowledge graph covering all Bulgarian elections from 2013 to the present day. One can explore that data in GraphDB Workbench using its search, graph traversal and visualization facilities. Easily accessible linked open elections data. The road ahead.

Statistics 142

Statistics Data Processing Publishing Metrics 142

Domino Data Lab

MARCH 27, 2019

Special thanks to Addison-Wesley Professional for permission to excerpt the following “Manipulating data with dplyr” chapter from the book, Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R. While this grouping is not visually apparent (i.e., Introduction.

Statistics 85

Statistics Data Science Visualization IT 85

Jet Global

MAY 1, 2023

Plus, there is an expectation that tools be visually appealing to boot. In the past, data visualizations were a powerful way to differentiate a software application. Companies like Tableau (which raised over $250 million when it had its IPO in 2013) demonstrated an unmet need in the market. It’s all about context.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52