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 83

Forecasting Modeling Statistics Uncertainty 83

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 52

Experimentation Optimization Uncertainty Metrics 52

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 143

Optimization Metadata Statistics Cost-Benefit 143

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 68

Experimentation Sales Metrics Measurement 68

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 78

Data Warehouse OLAP Statistics Cost-Benefit 78

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 61

Experimentation Testing Statistics Metrics 61

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 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52