2000, Data Science and Testing - Data Leaders Brief

Introducing Amazon MWAA larger environment sizes

AWS Big Data

APRIL 16, 2024

xlarge 8 vCPUs / 24 GB 4 vCPUs / 12 GB 40 tasks (default) Up to 2000 mw1.2xlarge 16 vCPUs / 48 GB 8 vCPUs / 24 GB 80 tasks (default) Up to 4000 With the introduction of these larger environments, your Amazon Aurora metadata database will now use larger, memory-optimized instances powered by AWS Graviton2.

Metadata

Metadata Metrics Testing Management

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

Data Science and Beyond

AUGUST 23, 2020

Previously, I encouraged readers to test different approaches to bootstrapped confidence interval (CI) estimation. The idea of using simulations to test bootstrapped CIs came from Tim Hesterberg’s What Teachers Should Know about the Bootstrap. The decision to use num_simulations =1,000 was informed by practical concerns (i.e.,

Testing

Testing IT Statistics Data Science

How to Use Apache Iceberg in CDP’s Open Lakehouse

Cloudera

AUGUST 8, 2022

Exploratory data science and visualization: Access Iceberg tables through auto-discovered CDW connection in CML projects. Our imported flights table now contains the same data as the existing external hive table and we can quickly check the row counts by year to confirm: year _c1. 9 2000 5683047. …. 1 2008 7009728.

Snapshot

Snapshot Data Warehouse Machine Learning Cost-Benefit

Webinars

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

MORE WEBINARS

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 Risk

Methods of Study Design – Experiments

Data Science 101

JANUARY 15, 2020

Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Suppose we want to test the effectiveness of a new drug against a particular disease. Suppose we want to compare the literate data of a country across decades. We randomly recruit subjects for that.

Experimentation

Experimentation Statistics Measurement Testing

Technical Analysis is Changing Quickly in the Era of Big Data

Smart Data Collective

JULY 16, 2023

This methodology is grounded in concrete, empirical evidence that has been tested and proven over time. For example, due to computerization and algorithmic trading, Goldman Sachs decreased the number of people trading stocks from 600 to 2, from 2000 to 2016. It is not based on unfounded claims or baseless assumptions.

Big Data

Big Data Marketing Strategy Insurance

Thread Dev Interview 6: @chris.mrbananas.greening

Data Science 101

AUGUST 30, 2023

Dot Com Boom: For those that don’t know, around 2000 the WWW became popular and many new web companies were formed. Another time we were doing “load testing” and everyone in the office was hitting the site and it seemed to be running really slowly. People were certainly using it for running and testing locally.

Consulting

Consulting Testing Data Processing IT

Moving Enterprise Data From Anywhere to Any System Made Easy

Cloudera

JUNE 2, 2022

This blog aims to answer two questions: What is a universal data distribution service? Why does every organization need it when using a modern data stack? In the modern data stack, there is a diverse set of destinations where data needs to be delivered. This presents a unique set of challenges.

Enterprise

Enterprise Data Lake Data Collection Data-driven

Moving Enterprise Data From Anywhere to Any System Made Easy

CIO Business Intelligence

JULY 13, 2022

This blog aims to answer two questions: What is a universal data distribution service? Why does every organization need it when using a modern data stack? In the modern data stack, there is a diverse set of destinations where data needs to be delivered. This presents a unique set of challenges.

Enterprise

Enterprise Data Lake Data Collection Data-driven

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Other techniques include simple re-sampling, where the minority class is continuously re-sampled until the number of obtained observations matches the size of the majority class, and focused under-sampling, where the discarded observations from the majority class are carefully selected to be away from the decision boundary (Japkowicz, 2000).

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Top Challenges and Opportunities for Chief Data Officers

Sisense

FEBRUARY 24, 2021

In fact, in a 2019 edition of Industrial Management & Data Systems, a research team led by Yu Nie noted that prior to the year 2000, there were only six chief data officers in the world. Clearly, data is becoming more important to organizations. The role of the chief data officer.

Data Warehouse

Data Warehouse Big Data Metrics Visualization

Density-Based Clustering

Domino Data Lab

DECEMBER 2, 2020

Due to its importance in both theory and applications, this algorithm is one of three algorithms awarded the Test of Time Award at the KDD conference in 2014. Rather it infers the number of clusters based on the data, and it can discover clusters of arbitrary shape (for comparison, k-means usually discovers spherical clusters).

Metrics

Metrics KDD Testing Machine Learning

Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

APRIL 23, 2024

Modeling live experiment data Data scientists at YouTube are rarely involved in the analysis of typical live traffic experiments. Multiparameter experiments, however, generate richer data than standard A/B tests, and automated t-tests alone are insufficient to analyze them well. Testing Statistical Hypotheses.

Experimentation

Experimentation Optimization Uncertainty Metrics

Misadventures in experiments for growth

The Unofficial Google Data Science Blog

APRIL 16, 2019

Such decisions involve an actual hypothesis test on specific metrics (e.g. This is not the classic case of hypothesis testing via experimentation, and thus the measured effects are subject to considerations that come with the territory of unintentional data. 0.86% - [Artist Title BPM] 2000 60 3.00±0.75%

Experimentation

Experimentation Sales Metrics Measurement

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. We wanted a realistic query and workload to test with but also wanted to focus on queries not accessing very many rows. In this case, reducing runtime from 930ms to 133ms for nearly a 7x improvement. We’ll illustrate with an example.

Optimization

Optimization Metadata Statistics Cost-Benefit

Unintentional data

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Thankfully not only have modern data analysis tools made data collection cheap and easy, they have made the process of exploratory data analysis cheaper and easier as well. We must correct for multiple hypothesis tests.

Experimentation

Experimentation Testing Statistics Metrics

The Benefits, Challenges and Risks of Predictive Analytics for Your Application

Jet Global

SEPTEMBER 1, 2023

Companies traditionally build their own predictive analytics solutions when they: Have significant IT resources to build, test, correct, and maintain an analytics platform. Enhanced Skill Development: Building your own software allows your application team to develop new skills in data science, machine learning, and analytics.

Predictive Analytics

Predictive Analytics Risk Analytics Software

Data Leaders Brief

Introducing Amazon MWAA larger environment sizes

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

Webinars

Trending Sources

How to Use Apache Iceberg in CDP’s Open Lakehouse

Webinars

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

Methods of Study Design – Experiments

Technical Analysis is Changing Quickly in the Era of Big Data

Thread Dev Interview 6: @chris.mrbananas.greening

Moving Enterprise Data From Anywhere to Any System Made Easy

Moving Enterprise Data From Anywhere to Any System Made Easy

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Top Challenges and Opportunities for Chief Data Officers

Density-Based Clustering

Towards optimal experimentation in online systems

Misadventures in experiments for growth

Keeping Small Queries Fast – Short query optimizations in Apache Impala

Unintentional data

The Benefits, Challenges and Risks of Predictive Analytics for Your Application

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