Sisense

JANUARY 21, 2020

To better understand when the data should be grouped, you should be familiar with causal inference. If you don’t have the time to read “The Book of Why,’” you can refer to Towards Data Science. In 2009, researchers suggested that Simpson’s paradox may occur more often than commonly thought.

Testing 104

Testing Risk Data-driven Statistics 104

bridgei2i

APRIL 16, 2021

He has over a decade of experience in the field of AI, which he started with his paper on machine learning in 2009. So, I remember when I was studying, and there were a lot of buzzwords like big data, Hadoop making the rounds and then came data science. His journey to the field of AI is an interesting one.

Deep Learning 98

Deep Learning Machine Learning Digital Transformation Software 98

Peter James Thomas

JUNE 20, 2019

The above chart compares monthly searches for Business Process Reengineering (including its arguable rebranding as Business Transformation ) and monthly searches for Data Science between 2004 and 2019. Here we come back to the upward trend in searches for Data Science. – McKinsey 2009. . [6].

Cost-Benefit 55

Cost-Benefit Data Warehouse Consulting Data Science 55

Jen Stirrup

APRIL 2, 2019

So, we made efforts to have real chops and apply our research and findings to our own organization, and to really analyze the data, and to apply marketing theory and practice to improving , informed by the academic literature. Data can help us to understand the research to explore and elicit reasons for avoidance of brands (Lee et al.,

Marketing 60

Marketing Advertising Data-driven Strategy 60

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Data science. Session Summary.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

MAY 20, 2021

The problem with this approach is that in highly imbalanced sets it can easily lead to a situation where most of the data has to be discarded, and it has been firmly established that when it comes to machine learning data should not be easily thrown out (Banko and Brill, 2001; Halevy et al., The unreasonable effectiveness of data.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. back to the structure of the dataset. Let’s look through some antidotes. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

SEPTEMBER 18, 2019

Models are at the heart of data science. Data exploration is vital to model development and is particularly important at the start of any data science project. the mouse is hovering over December 2009, and it shows a point near the bottom of the last housing crash, with the median housing price in Palo Alto at $1.2

Visualization 99

Visualization Interactive Machine Learning Data Science 99

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. Introduction Time series data appear in a surprising number of applications, ranging from business, to the physical and social sciences, to health, medicine, and engineering. Petrone, S.,

Forecasting 64

Forecasting Statistics Modeling Software 64

Domino Data Lab

APRIL 21, 2021

The complete dataset and code used in this blog post is available at try.dominodatalab.com, and all results shown here are fully reproducible, thanks to the Domino reproducibility engine, which is part of the Domino Data Science platform. Learning from Imbalanced Data. References. [1] Pavía, Ernesto J. 2] Nitesh V. 3] He, H. &

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Peter James Thomas

AUGUST 12, 2018

Especially for all Business Analytics professionals out there (2009). [7]. See in particular my trilogy: Using historical data to justify BI investments – Part I (2011). Using historical data to justify BI investments – Part II (2011). Using historical data to justify BI investments – Part III (2011). [8].

Metrics 49

Metrics Statistics Data Quality Measurement 49

The Unofficial Google Data Science Blog

MARCH 13, 2017

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. 2009, " Measuring invariances in deep networks ". Technical Report 1341, University of Montreal, 2009.

IT 68

IT Visualization Modeling Uncertainty 68

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

GAMs are popular among data science and machine learning applications for their simplicity and interpretability. For example, one could apply logarithms or add polynomial terms for certain features. A more general approach is to learn a Generalized Additive Model (GAM). References Wightman, L. Law School Admission Council, 1998.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

AUGUST 1, 2021

PDPs for the bicycle count prediction model (Molnar, 2009). Instead, you should focus on how techniques like PDPs and LIME can be used to gain insights into the model’s inner workings and how you can add those to your data science toolbox. Conference on Knowledge Discovery and Data Mining, pp. References.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

datapine

JANUARY 31, 2022

Although the oil company has been producing massive amounts of data for a long time, with the rise of new cloud-based technologies and data becoming more and more relevant in business contexts, they needed a way to manage their information at an enterprise level and keep up with the new skills in the data industry.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Peter James Thomas

APRIL 9, 2019

This increase was driven in part by the launch of my new Maths & Science section , articles from which claimed no fewer than 6 slots in the 2018 top 10 articles, when measured by hits [1]. Given the advent of the Maths & Science section, there are now seven categories into which I have split articles. Data Visualisation.

Data-driven 49

Data-driven Statistics Big Data Data Science 49