2010 Big Data Statistics Strategy 
Smart Data Collective
JANUARY 27, 2022
Did you know that big data consumption increased 5,000% between 2010 and 2020 ? Big data technology is changing countless aspects of our lives. A growing number of careers are predicated on the use of data analytics, AI and similar technologies. This should come as no surprise. Genetic Engineer.
Smart Data Collective
APRIL 13, 2021
In the modern world of business, data is one of the most important resources for any organization trying to thrive. Business data is highly valuable for cybercriminals. They even go after meta data. Big data can reveal trade secrets, financial information, as well as passwords or access keys to crucial enterprise resources.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
The Unofficial Google Data Science Blog
JULY 22, 2020
by ALEXANDER WAKIM Ramp-up and multi-armed bandits (MAB) are common strategies in online controlled experiments (OCE). These strategies involve changing assignment weights during an experiment. The first is a strategy called ramp-up and is advised by many experts in the field [1].
O'Reilly on Data
MARCH 20, 2019
For example, an attacker could learn what characteristics your model associates with awarding large discounts, like comping a room at a casino for a big spender, and then falsify their information to receive the same discount. They could also share their strategy with others, potentially leading to large losses for your company.
O'Reilly on Data
JANUARY 25, 2021
AI, Machine Learning, and Data. Healthy growth in artificial intelligence has continued: machine learning is up 14%, while AI is up 64%; data science is up 16%, and statistics is up 47%. It’s often a chaotic grassroots process rather than a carefully planned strategy. We don’t think so, but we’re prepared to be wrong.
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.
The Unofficial Google Data Science Blog
MARCH 31, 2016
Far from hypothetical, we have encountered these issues in our experiences with "big data" prediction problems. 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. See [9], [10] for a discussion of this approach.
Expert insights. Personalized for you.
127 
Let's personalize your content