CIO Business Intelligence

APRIL 24, 2023

In the 2023 State of the CIO report , IT leaders said they were most concerned about finding qualified experts in advanced areas such as cybersecurity, blockchain, and data science and analytics. Internal talent is gold, and we’re making sure our current employees find places to grow and modernize their skill sets.”

IT 106

IT Strategy Software Insurance 106

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.

Forecasting 64

Forecasting Statistics Modeling Software 64

Jen Stirrup

JUNE 20, 2020

My analysis is based on the Financial statements put forward by PASS using some basic metrics; until you do that piece, you can’t move forward to compare and contrast it with other data since you have not done your ‘descriptive statistical analysis’ first to ensure that the comparison is valid. Current Ratio.

Metrics 104

Metrics Business Analytics Data-driven Statistics 104

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. Eighty percent of this problem is collecting the data and then transforming the data. The other 20 percent is ML- and data science–related tasks like finding the right model, doing EDA, and feature engineering.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

SEPTEMBER 25, 2019

Conducting exploratory analysis and extracting meaningful insights from data are core components of research and data science work. Time series data is commonly encountered. We see it when working with log data, financial data, transactional data, and when measuring anything in a real engineering system.

Forecasting 87

Forecasting Modeling Statistics Optimization 87

Occam's Razor

MARCH 30, 2017

While the phrase Artificial Intelligence has been around since the first human wondered if she could go further if she had access to entities with inorganic intelligence, it truly jumped the shark in 2016. trillion pictures in 2016. One key thing that stymied my efforts, and likely your ML efforts, in 2016 was Identity.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Domino Data Lab

JANUARY 6, 2019

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev. Learning Data Science.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. GAMs are popular among data science and machine learning applications for their simplicity and interpretability.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Rita Sallam

JULY 22, 2016

Gartner revamped the BI and Analytics Magic Quadrant in 2016 to reflect the mainstreaming of this market disruption. Research VP, Business Analytics and Data Science. A modern BI platform supports IT-enabled analytic content development. Enjoy your summer!! Thanks for reading and stay tuned. Regards, Rita Sallam.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

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. Data scientists spend countless hours wrangling data.

Statistics 85

Statistics Data Science Visualization IT 85

The Unofficial Google Data Science Blog

MARCH 31, 2016

We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. Journal of the American Statistical Association 68.341 (1973): 117-130. [5] Journal of the American Statistical Association, Vol. 5] Anoop Korattikara, et al.

Modeling 40

Modeling Statistics Advertising Testing 40

Andrew White

JANUARY 9, 2022

This research does not tell you where to do the work; it is meant to provide the questions to ask in order to work out where to target the work, spanning reporting/analytics (classic), advanced analytics and data science (lab), data management and infrastructure, and D&A governance. We write about data and analytics.

Analytics 64

Analytics Measurement Modeling Data-driven 64

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. CoRR, 2016. [3] CoRR, 2014. [2] 2] Shrikumar, Avanti, Greenside, Peyton, Shcherbina, Anna, and Kundaje, Anshul.

IT 68

IT Visualization Modeling Uncertainty 68

Domino Data Lab

AUGUST 22, 2019

Data scientists and researchers require an extensive array of techniques, packages, and tools to accelerate core work flow tasks including prepping, processing, and analyzing data. Utilizing NLP helps researchers and data scientists complete core tasks faster. Note: Google Translate has incorporated NMT since 2016.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111