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 83

Testing IT Statistics Data Science 83

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 92

Metadata Metrics Testing Management 92

Smart Data Collective

DECEMBER 31, 2020

Big data can be useful for all of these aspects of your campaign. PPC Hero talked about the evolving role of data science in PPC. By carefully structuring your campaigns with big data, you can benefit from profitable PPC campaigns that deliver long-standing results for your business.

Data-driven 97

Data-driven Marketing Advertising Big Data 97

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 82

Deep Learning Machine Learning Forecasting Visualization 82

Smart Data Collective

JULY 16, 2023

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. By analyzing vast amounts of data, we unveil patterns and correlations that were previously hidden. The law of big numbers reinforces the reliability and accuracy of our analyses.

Big Data 77

Big Data Marketing Strategy Insurance 77

Rita Sallam

JUNE 2, 2023

We did two Bake-Offs at the Gartner Data and Analytics Summit in London last week. The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms. In 2000, the Netherlands had 8.5

Optimization 52

Optimization Insurance Machine Learning Risk 52

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 103

Enterprise Data Lake Data Collection Data-driven 103

Smarten

APRIL 26, 2022

Now, consider the relevance of the knowledge gained from a degree achieved in 2000 in an IT related discipline. If you look at the culture and fabric of information technology, it doesn’t take long to recognize that it has been changing rapidly and that change is faster and more intense than ever. How much has changed since then?

Technology 67

Technology Software Enterprise Machine Learning 67

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. This statement may sound confusing so let us see an example!!! But we also note that the population growth in 2010-2020 is 3 times the other decade.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Peter James Thomas

AUGUST 18, 2018

That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. One of the early promises of a Data Lake approach was that – once all relevant data had been ingested – this would be directly leveraged by Data Scientists to derive insight.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

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. Though when the dot com boom came everything changed! We hired all sorts of people from many backgrounds – most of them turned out to be amazing coders. It was awesome… until it wasn’t.

Consulting 111

Consulting Testing Data Processing IT 111

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 78

Enterprise Data Lake Data Collection Data-driven 78

IBM Big Data Hub

JANUARY 10, 2023

Leveraging data to replace the ‘gut feel’ on which too many business decisions are made enables change practitioners to separate perceptions from reality and decide which processes need the most focus. This Index proved that in almost 2000 companies, organizational health is closely linked to performance.

Management 52

Management Data-driven Data mining Business Objectives 52

Rita Sallam

SEPTEMBER 11, 2020

The United States grew the least at only 2% from 2000 to 2016. Don’t miss the Data Science and Machine Learning Bake-Off on September 15th at 1:pm EDT. In 2016, life expectancy in the United States (78.5) was almost 4yrs less than the average of all other countries combined (82). To register and find the replay click here.

Statistics 96

Statistics Machine Learning Sales Data Science 96

Insight

MARCH 27, 2019

Want to advance your career in Data Science and Artificial Intelligence? deepaugmentusage AutoML for Data Augmentation was originally published in Insight Data on Medium, where people are continuing the conversation by highlighting and responding to this story. DeepAugment takes 4.2 x2large instance.

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). 2000): 451-476. [6] 2014): 276.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Cloudera

APRIL 4, 2019

Use customer lifetime value data to segment customers and tailor offerings to meet their desires and goals. Two very important things happened during the economic slowdown of the mid to late 2000’s.

Insurance 53

Insurance IT Interactive Cost-Benefit 53

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 136

Machine Learning Metrics Data mining Knowledge Discovery 136

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. Revenue takes center stage Herlihy’s vision for top IT leadership is not an anomaly.

IT 98

IT Sales Business Objectives Marketing 98

DataRobot

MAY 6, 2021

In 2000, Netflix offered Blockbuster a partnership, but the home movie provider turned it down. To reduce the impact of cognitive biases, use data science tools that: Generate explanations for each AI decision. Do not rely on self-reporting, opinions, assertions about AI reliability, or a claimed lack of reliability.

Forecasting 145

Forecasting Measurement Reporting Data-driven 145

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 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 88

Enterprise Machine Learning Recreation/Entertainment IoT 88

The Unofficial Google Data Science Blog

APRIL 16, 2019

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% 0.86% - [Artist Title BPM] 2000 60 3.00±0.75% 1.14% [BPM Artist Title] 2000 30 1.50±0.53% 0.43% - [Artist Title BPM] 8000 440 5.50±0.50% 0.66% [BPM Artist Title] 8000 570 7.12±0.56%

Experimentation 68

Experimentation Sales Metrics Measurement 68

The Unofficial Google Data Science Blog

APRIL 23, 2024

Biostatistics, 1(1):27-34, 03 2000. [2] If you do try this out, please let us know how it goes in the comments. References [1] Andrew Gelman. Should we take measurements at an intermediate design point? 2] Nist / sematech. e-handbook of statistical methods: Summary tables of useful fractional factorial designs , 2018 [3] Ulrike Groemping.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

DECEMBER 2, 2020

For a more contrived but impressive illustration of DBSCAN’s capabilities, let’s consider a toy example informally known as the “half-moons” dataset where each data point belongs to one of the two “half-moons”. from sklearn.datasets import make_moons #moons_X: Data, moon_y: Labels moons_X, moon_y = make_moons(n_samples = 2000).

Metrics 116

Metrics KDD Testing Machine Learning 116

Cloudera

NOVEMBER 13, 2020

Data science experiment result and performance analysis, for example, calculating model lift. Don’t forget even for your data science use cases you can leverage Impala as a fast data cruncher with the awesome pandas dataframe feature of the Impyla library or as part of the Ibis framework.

Optimization 144

Optimization Metadata Statistics Cost-Benefit 144

Domino Data Lab

AUGUST 29, 2019

class DQNAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=2000) self.gamma = 0.95 Example 13.2 A deep Q-learning agent. self.epsilon = 1.0 self.epsilon_decay = 0.995 self.epsilon_min = 0.01 self.learning_rate = 0.001 self.model = self._build_model()

Optimization 111

Optimization Modeling Deep Learning Metrics 111

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

Sisense

SEPTEMBER 3, 2019

About the authors: Neha Kumar is a member of the Solutions Team at Periscope Data by Sisense, providing architecture guidance and technical support for enterprise customers. Outside of work, Neha is pursuing a Masters in Information and Data Science from UC Berkeley and enjoys dancing and painting.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Suppose a data scientist works at The Hill Climber, a climbing shop in the Himalayas. She wants to evaluate the performance of the Ice Axe 2000. She finds these axes to be in very good shape after controlling for age, and concludes that the Ice Axe 2000 can be recommended on the basis of its durability.

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

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.

Deep Learning 110

Deep Learning Machine Learning Visualization Data Science 110

Jet Global

SEPTEMBER 1, 2023

Enhanced Skill Development: Building your own software allows your application team to develop new skills in data science, machine learning, and analytics. Higher Costs: In-house development incurs costs not only in terms of hiring or training data science experts but also in ongoing maintenance, updates, and potential debugging.

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. Microsoft recently introduced a comprehensive analytics solution for its enterprise customers, Microsoft Fabric.

Analytics 52

Analytics Management Reporting Enterprise 52