Smarten

APRIL 26, 2022

Now, consider the relevance of the knowledge gained from a degree achieved in 2000 in an IT related discipline. You hear it every day of your life. “Experience counts.” ” With experience comes valuable knowledge and, presumably, a history of what works, what has been tried, and how to navigate life and business.

Technology 67

Technology Software Enterprise Machine Learning 67

Smart Data Collective

JULY 16, 2023

Big data technology has undoubtedly changed the financial industry in extraordinary ways. We usually talk about the benefits of big data from the perspective of financial institutions such as hedge fund managers, insurance companies and banks. It will play an even more important role in the future.

Big Data 76

Big Data Marketing Strategy Insurance 76

Rita Sallam

JUNE 2, 2023

We explored these questions and more at our Bake-Offs and Show Floor Showdowns at our Data and Analytics Summit in London with 3,700 of our closest D&A friends and family. We did two Bake-Offs at the Gartner Data and Analytics Summit in London last week. In 2000, the Netherlands had 8.5

Optimization 52

Optimization Insurance Machine Learning Risk 52

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 85

Deep Learning Machine Learning Forecasting Visualization 85

Cloudera

JUNE 2, 2022

allowing developers to connect to any data source anywhere with any structure, process it, and deliver to any destination. 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? What is the modern data stack?

Enterprise 107

Enterprise Data Lake Data Collection Data-driven 107

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. Dot Com Boom: For those that don’t know, around 2000 the WWW became popular and many new web companies were formed. “Definitely – now it [programmer] seems like an obvious career choice.

Consulting 111

Consulting Testing Data Processing IT 111

Cloudera

APRIL 4, 2019

When you put your customer at the core of your business and combine it with your CRM system you have a wealth of data and the beginnings of a full 360 view of the customer. This data can now be used to enhance your customer experience by: Understanding buyer behavior, hobbies, interests and engagement.

Insurance 55

Insurance IT Interactive Cost-Benefit 55

Data Science 101

JANUARY 15, 2020

Bias ( syatematic unfairness in data collection ) can be a potential problem in experiments and we need to take it into account while designing experiments. Quality of data is another factor we should keep an eye on. Bad data can result in poor results. Suppose we want to compare the literate data of a country across decades.

Experimentation 96

Experimentation Statistics Measurement Testing 96

IBM Big Data Hub

JANUARY 10, 2023

Grasping these opportunities at IBM, we’re increasingly building our specialism in process mining and data analysis tools and techniques we believe to be true ‘game changers’ when it comes to building cultures of continuous change and innovation. Starting from the top: Focusing on the right change and making it feel right.

Management 52

Management Data-driven Data mining Business Objectives 52

Peter James Thomas

AUGUST 18, 2018

No this article has not escaped from my Maths & Science section , it is actually about data matters. But first of all, channeling Jennifer Aniston [1] , “here comes the Science bit – concentrate” Shared Shapes. That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

CIO Business Intelligence

JULY 13, 2022

allowing developers to connect to any data source anywhere with any structure, process it, and deliver to any destination. 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? What is the modern data stack?

Enterprise 64

Enterprise Data Lake Data Collection Data-driven 64

CIO Business Intelligence

JULY 24, 2023

“Growth, profitability, productivity, and engagement are all now part of the CIO role that 20 years ago was all about internal operations,” says Herlihy, who has been an IT executive since landing his first CIO position in 2000. Much of that work started in response to the pandemic.

IT 92

IT Sales Business Objectives Marketing 92

Rita Sallam

SEPTEMBER 11, 2020

The United States grew the least at only 2% from 2000 to 2016. Another interesting finding is that when mashing up population health data with freely available weather data, there is a clear correlation between higher level of precipitation and higher birthrates. In 2016, life expectancy in the United States (78.5)

Statistics 96

Statistics Machine Learning Sales Data Science 96

Insight

MARCH 27, 2019

DeepAugment is an AutoML tool focusing on data augmentation. It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. Introduction Data is the most critical piece of AI applications. Collecting additional data is not always possible and can be expensive. 2018 , blog ).

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

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 83

Forecasting Modeling Statistics Uncertainty 83

Domino Data Lab

MAY 20, 2021

Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. If, however, the dataset is imbalanced with a class ratio of 100:1, this means that it contains only 100 examples of the minority class.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

DataRobot

MAY 6, 2021

In 2000, Netflix offered Blockbuster a partnership, but the home movie provider turned it down. If it has a predictable outcome, and you have suitable data to reach that outcome, then automate that workflow. As I pulled books from my bookcase, packing them into boxes to go into storage, I found my Blockbuster Video membership card.

Forecasting 145

Forecasting Measurement Reporting Data-driven 145

Sisense

FEBRUARY 24, 2021

Whatever a company does, how it uses data is a key differentiator in its success or failure. Whether that data is generated internally or gathered from an external application used by customers, organizations now use on-demand cloud computing resources to make sense of the data, discover trends, and make intelligent forecasts.

Data Warehouse 69

Data Warehouse Big Data Metrics Visualization 69

Cloudera

JUNE 25, 2019

On June 18th, Cloudera provided an exclusive preview of these capabilities, and more, with the introduction of Cloudera Data Platform (CDP), the industry’s first enterprise data cloud. Over 2000 customers and partners joined us in this live webinar featuring a first-look at our upcoming cloud-native CDP services.

Enterprise 89

Enterprise Machine Learning Recreation/Entertainment IoT 89

Domino Data Lab

DECEMBER 2, 2020

Compared to centroid-based clustering like k-means, density-based clustering works by identifying “dense” clusters of points, allowing it to learn clusters of arbitrary shape and identify outliers in the data. neighborhoods is given a data point, we want to be able to reason about the data points in the space around it.

Metrics 116

Metrics KDD Testing Machine Learning 116

The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. by CHRIS HAULK It is sometimes useful to think of a large-scale online system ( LSOS ) as an abstract system with parameters $X$ affecting responses $Y$.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

APRIL 16, 2019

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. Unlike, say, picking a data center, randomization produces metrics that are immune to sampling bias and can thus detect regressions in fewer treated units.

Experimentation 68

Experimentation Sales Metrics Measurement 68

Cloudera

NOVEMBER 13, 2020

The reality is that data warehousing contains a large variety of queries both small and large; there are many circumstances where Impala queries small amounts of data; when end users are iterating on a use case, filtering down to a specific time window, working with dimension tables, or pre-aggregated data. More on this below.

Optimization 144

Optimization Metadata Statistics Cost-Benefit 144

Domino Data Lab

AUGUST 29, 2019

With a self-driving car, there are effectively an infinite number of possible environmental states: As it moves along a road, its myriad sensors—cameras, radar, lidar, accelerometers, microphones, and so on—stream in broad swaths of state information from the world around the vehicle, on the order of a gigabyte of data per second.

Optimization 111

Optimization Modeling Deep Learning Metrics 111

Sisense

SEPTEMBER 3, 2019

roll-ups of many rows of data). roll-ups of many rows of data). On the other hand, OLTP databases are great for cases where your data is written to the database as often as it is being read from it. As the name suggests, a common use case for this is any transactional data. Redshift is a type of OLAP database.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

Peter James Thomas

AUGUST 30, 2018

Based on Kaggle’s State of Data Science Survey 2017 (Sample size: 10,153). The text in the above exhibit is not that clear [2] , so here are the 20 top challenges [3] faced by those running Data Science teams in human-readable form: #. Dirty Data. Lack of Data Science talent in the organization.

Data Science 75

Data Science Data Quality Machine Learning Business Intelligence 75

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Yet when we use these tools to explore data and look for anomalies or interesting features, we are implicitly formulating and testing hypotheses after we have observed the outcomes. We ought not dredge our data. For better and for worse, technology has led to a democratization of data within organizations. Is it actionable?

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. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. That’s excellent for supporting really interesting workflow integrations in data science work. Usually it’s human-generated text, but not always. the the DET True? PUNCT False.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

Jet Global

SEPTEMBER 1, 2023

At its heart, predictive analytics answers the question, “What is most likely to happen based on my current data, and what can I do to change that outcome?” Enhanced Skill Development: Building your own software allows your application team to develop new skills in data science, machine learning, and analytics.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52

Jet Global

MARCH 14, 2024

The solution offers data movement, data science, real-time analytics, and business intelligence within a single platform. It is a complete tool for data preparation, automation, and modeling, which simplifies data management and speeds up time-to-value. Or does it?

Analytics 52

Analytics Management Reporting Enterprise 52