2000, Data Science and Statistics

2000

Data Science

Statistics

IBM and Data Science are Helping Save the World through Call for Code

Business Over Broadway

SEPTEMBER 5, 2018

million people have been directly affected by natural disasters since 2000. Even though natural events such as floods, earthquakes or hurricanes are inevitable, I believe that their impact can be mitigated through the application of data and analytics. Data is the Fuel; Data Science is the Engine. Help from IBM.

Data Science

Data Science Statistics Advertising Deep Learning

Keeping Small Queries Fast – Short query optimizations in Apache Impala

Cloudera

NOVEMBER 13, 2020

Data science experiment result and performance analysis, for example, calculating model lift. Exhaustive cost-based query planning depends on having up to date and reliable statistics which are expensive to generate and even harder to maintain, making their existence unrealistic in real workloads.

Optimization

Optimization Metadata Statistics Cost-Benefit

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Webinars

Data Talks, CFOs Listen: Why Analytics Are Key To Better Spend Management

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

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Trending Sources

Methods of Study Design – Experiments

Data Science 101

JANUARY 15, 2020

Suppose we want to compare the literate data of a country across decades. Let the number of literate people increased by 5000 in 2010-2020 whereas 3500 in 2000-2010. Statistics Essential for Dummies by D. Rumsey Statistical Reasoning Course by Stanford Ligunita Introduction to the Practice of Statistics by D.

Experimentation

Experimentation Statistics Measurement Testing

Webinars

Data Talks, CFOs Listen: Why Analytics Are Key To Better Spend Management

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

MORE WEBINARS

Change The Way You Do ML With Applied ML Prototypes

Cloudera

FEBRUARY 25, 2021

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. With almost all of the Fortune 500 and a majority of the Global 2000 relying on Cloudera for their most important data assets, Cloudera’s Machine Learning product (CML) is the way enterprises do ML.

Deep Learning

Deep Learning Machine Learning Visualization Forecasting

Many is not enough: Counting simulations to bootstrap the right way

Data Science and Beyond

AUGUST 23, 2020

The output from running this function with the default arguments is plotted below. The output from running this function with the default arguments is plotted below. when asked to generate 95% CIs.

Testing

Testing IT Statistics Data Science

Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

APRIL 17, 2017

Due to multiple changes to the scale of the values depicted on the vertical axis, “Results Pages” values, which reflect search query volume, at the rightward end of the plot (corresponding to July 2004) are 2000 times larger than the values depicted at the leftward end (corresponding to November 1998). Forecasting data and methods". [2]

Forecasting

Forecasting Modeling Statistics Uncertainty

How to Build a Performant Data Warehouse in Redshift

Sisense

SEPTEMBER 3, 2019

Analyze is a process that you can run in Redshift that will scan all of your tables, or a specified table, and gathers statistics about that table. These statistics are used to guide the query planner in finding the best way to process the data. Conclusion.

Data Warehouse

Data Warehouse OLAP Statistics Cost-Benefit

Towards optimal experimentation in online systems

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. Biostatistics, 1(1):27-34, 03 2000. [2] Journal of Statistical Software, 56(1):1-56, 2014. [5] Testing Statistical Hypotheses.

Experimentation

Experimentation Optimization Uncertainty Metrics

Convergent Evolution

Peter James Thomas

AUGUST 18, 2018

That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. This is the essence of Convergent Evolution.

Data Lake

Data Lake Data Warehouse Data mining Statistics

Unintentional data

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Data was expensive to gather, and therefore decisions to collect data were generally well-considered. Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data.

Experimentation

Experimentation Testing Statistics Metrics

Misadventures in experiments for growth

The Unofficial Google Data Science Blog

APRIL 16, 2019

On the other hand, fledgling products often have neither the statistical power to identify the effects of small incremental changes, nor the luxury to contemplate small improvements. 0.71% Non-EDM users (2,000 impressions): Treatment Impressions Sales Conversion Rate Delta From Control [Artist Title] (control) 2000 80 4.00±0.86%

Experimentation

Experimentation Sales Metrics Measurement

BI Bake-Off Goes Virtual!

Rita Sallam

SEPTEMBER 11, 2020

We try use the Bake-Offs as a platform for data for good. Rather than just using some solely fun data like football/ soccer statistics – go Mo Salah! – this year, we used population health data. Last year we did loneliness and happiness data. The United States grew the least at only 2% from 2000 to 2016.

Statistics

Statistics Machine Learning Sales Data Science

Gartner D&A Summit Bake-Offs Explored Flooding Impact And Reasons for Optimism!

Rita Sallam

APRIL 2, 2023

We explored these questions and more at our Bake-Offs and Show Floor Showdowns at our Data and Analytics Summit in Orlando with 4,000 of our closest D&A friends and family. The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms.

Optimization

Optimization Machine Learning Insurance Data Science

What is a Data Pipeline?

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. There are many types of data pipelines, and all of them include extract, transform, load (ETL) to some extent.

Data Lake

Data Lake Data Warehouse Business Intelligence Machine Learning

Data Leaders Brief

IBM and Data Science are Helping Save the World through Call for Code

Keeping Small Queries Fast – Short query optimizations in Apache Impala

Webinars

Trending Sources

Methods of Study Design – Experiments

Webinars

Change The Way You Do ML With Applied ML Prototypes

Many is not enough: Counting simulations to bootstrap the right way

Our quest for robust time series forecasting at scale

How to Build a Performant Data Warehouse in Redshift

Towards optimal experimentation in online systems

Convergent Evolution

Unintentional data

Misadventures in experiments for growth

BI Bake-Off Goes Virtual!

Gartner D&A Summit Bake-Offs Explored Flooding Impact And Reasons for Optimism!

What is a Data Pipeline?

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