DataKitchen

APRIL 28, 2021

Gartner included data fabrics in their top ten trends for data and analytics in 2019. Data fabrics provide reusable services that span data integration, access, transformation, modeling, visualization, governance, and delivery. From an industry perspective, the topic of data fabrics is on fire. What is a Data Fabric?

Statistics 246

Statistics Optimization Technology Data Analytics 246

BizAcuity

DECEMBER 8, 2021

The years 2019 and 2020 have seen a spike in mobile gaming as a whole because people have been finding it extremely difficult to cope with social isolation and finally got some respite in such platforms. Their AI model will focus and optimize the retention activities to the best customers. Such a tool can also calculate the churn rate.

Statistics 96

Statistics Measurement Forecasting Consulting 96

Insight

MAY 14, 2020

Image source: [link] Motivation Beginning around December 2019, patients with severe respiratory infections began arriving at hospitals in Wuhan, kicking off a race to identify the cause of the illness. Data Pipeline Input to the model is a FASTA file containing sequences of 100 base pairs.

Deep Learning 98

Deep Learning Statistics Modeling Machine Learning 98

Smart Data Collective

MARCH 23, 2020

According to the SensorTower statistics , in 2019, a simple arcade game Stack Ball reached 100 million installs and only continued to grow. In 2019, this number increased to 2.4 They can even create models that predict what users will do after an update or a patch is released. In 2014, there were about 1.82

Big Data 103

Big Data Recreation/Entertainment Data mining Optimization 103

Cloudera

DECEMBER 3, 2021

In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI. For example, training an interview screening model using education data often contains gender information. As discussed in this article , model design can also be a source of bias too. Model Drift. Governance.

Uncertainty 102

Uncertainty Measurement Metrics Risk 102

Rocket-Powered Data Science

MARCH 10, 2020

In 2019, I was asked to write the Foreword for the book “ Graph Algorithms: Practical Examples in Apache Spark and Neo4j “ , by Mark Needham and Amy E. And this: perhaps the most powerful node in a graph model for real-world use cases might be “context”. How does one express “context” in a data model?

Metadata 250

Metadata Machine Learning ROI Prescriptive Analytics 250

DataRobot

AUGUST 5, 2021

In 2019, Utah struck a deal with Banjo, a threat detection firm selling AI services to process live traffic feeds, dispatch logs, and other data. After making their first estimate, all participants were shown rental price predictions from a machine learning model. Is there too much hype about AI or too much doomsaying?

Uncertainty 98

Uncertainty Sales Risk Statistics 98

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. For any given input, the same program won’t necessarily produce the same output; the output depends entirely on how the model was trained.

Management 333

Management Machine Learning Experimentation Metrics 333

CIO Business Intelligence

MARCH 15, 2022

Synthetic data is artificially generated information that can be used in place of real historic data to train AI models when actual data sets are lacking in quality, volume, or variety. Many models will decide that the most successful policy will be to label every transaction as non-fraudulent.”. Synthetic data use cases.

Strategy 145

Strategy Testing Sales Modeling 145

IBM Big Data Hub

AUGUST 9, 2023

Why phishing simulations are important Recent statistics show phishing threats continue to rise. Since 2019, the number of phishing attacks has grown by 150% percent per year— with the Anti-Phishing Working Group (APWG) reporting an all-time high for phishing in 2022 , logging more than 4.7 million phishing sites.

Testing 74

Testing Measurement Behavioral Analytics Reporting 74

Sisense

JULY 7, 2020

Another feature of In-Warehouse Data Prep is the warehouse statistics profiling view, which allows users to understand their data at a glance, identifying patterns, relationships, and unexpected values. Her debut novel, The Book of Jeremiah , was published in 2019. Self-service dashboards: Your insights, your way.

Slice and Dice 92

Slice and Dice Data Warehouse Analytics Dashboards 92

Smart Data Collective

SEPTEMBER 22, 2020

Statistics over time have proven that the firearms industry does exceptionally well under two conditions: right before a presidential election and during a national crisis. For instance, firearm sales are up 95% from 2019 and ammunition sales are up by 139%. Currently, the U.S. is facing both of these conditions at once.

Big Data 95

Big Data Marketing Sales Manufacturing 95

Domino Data Lab

JANUARY 24, 2019

Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve seen in the market. These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. Reflections. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

Cloudera

JULY 11, 2021

According to statistics by Singapore’s Ministry of Education, there has been a 3% increase in the number of women pursuing STEM university degrees from 2017 to 2019, as reported by The Straits Times. . Despite companies in the tech industry trying to be more inclusive and diverse, gender diversity still remains an issue.

Sales 104

Sales Consulting Technology Statistics 104

Insight

APRIL 10, 2020

In 2019, over 1,000 Insight Fellows completed one of our 7 programs across 6 locations & remote. Their latest placement and salary statistics are: 88% of Insight Fellows accept a job offer in their chosen field within 6 months of finishing the Fellows Program, and the median time to accept a job offer is 8 weeks.

Uncertainty 74

Uncertainty Statistics Machine Learning Publishing 74

Smart Data Collective

APRIL 11, 2019

Big data is playing a monumental role in economic development in 2019. It is being used in every industry from healthcare delivery to insurance modeling to political science. In addition to a variety of core courses that cover programming, statistics, and databases, students may choose one of several data analysis courses.”.

Big Data 76

Big Data Insurance Statistics Technology 76

Smart Data Collective

JUNE 15, 2022

trillion in 2019? Statistics such as average time on page, bounce rate, conversion rate, and others may be used to perfect your website and make changes to make it more appealing to visitors. One of the secrets to attracting and retaining customers is to become more data-centric. The retail industry is expanding all the time.

Advertising 97

Advertising Cost-Benefit ROI Sales 97

KDnuggets

AUGUST 21, 2019

Understanding Cancer using Machine Learning; Statistical Modelling vs Machine Learning; Is Kaggle Learn a "Faster Data Science Education?"; This week's news: Become More Marketable as a Data Scientist; Command Line Basics Every Data Scientist Should Know; Chatbots with Keras!; and much more!

Data Science 48

Data Science Marketing Statistics Machine Learning 48

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

FineReport

MAY 6, 2020

Metode kuantitatif: analisis axis waktu, model probabilitas, optimalisasi. Prinsip database: model data, desain database. Analisis prediktif: analisis axis waktu, principal component analysis, nonparametric regression, statistical process control. The Elements of Statistical Learning. Machine Learning. Bahasa Programing.

Data mining 98

Data mining Data Warehouse Machine Learning Big Data 98

FineReport

DECEMBER 19, 2019

If you want to learn more about self-service BI tools, you can take a look at this review: 5 Most Popular Business Intelligence (BI) Tools in 2019 , to understand your own needs and then choose the tool that is right for you. Of course, other BI tools such as Power BI and Qlikview also have their own advantages. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

O'Reilly on Data

FEBRUARY 18, 2020

Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%. But sustained interest in cloud migrations—usage was up almost 10% in 2019, on top of 30% in 2018—gets at another important emerging trend. Still cloud-y, but with a possibility of migration.

Data-driven 336

Data-driven Software Statistics Marketing 336

Smart Data Collective

AUGUST 8, 2019

FireEye claims that email is the launchpad for more than 90 percent of cyber attacks, while a multitude of other statistics confirm that email is the preferred vector for criminals. The utilization and success of these techniques could help explain a 17 percent spike in this area of cybercrime during the first quarter of 2019.

Big Data 85

Big Data Data Processing Machine Learning Predictive Analytics 85

Sisense

DECEMBER 3, 2019

The merger of Periscope Data in May 2019 brings robust functionality for cloud data experts to work with their data as needed while supporting a wide breadth of users and use cases. With a single click, an ensemble of univariate forecast models run against your data. Unleash the power of advanced analytics. Talk to your data.

Data Warehouse 90

Data Warehouse Analytics Digital Transformation Forecasting 90

O'Reilly on Data

MARCH 18, 2020

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey. It seems as if the experimental AI projects of 2019 have borne fruit. But what kind? Bottlenecks to AI adoption.

Enterprise 310

Enterprise Deep Learning Data Governance Risk 310

KDnuggets

SEPTEMBER 17, 2019

For some people anything below 60% is acceptable and for certain others, even a correlation of 30% to 40% is considered too high because it one variable may just end up exaggerating the performance of the model or completely messing up parameter estimates.

Modeling 107

Modeling IT Statistics Analytics 107

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. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Business Over Broadway

MAY 22, 2019

Remember that digital transformation is about transforming your business and operating models with technology. How employees can drive transformation and emerging technology adoption: Both are data-heavy endeavors; We know that statistics/math knowledge is related to data science project success. IoT Artificial Intelligence.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Domino Data Lab

AUGUST 8, 2019

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. Sci Foo 2019. then building machine learning models to recommend methods and potential collaborators to scientists. Across the board, organizations struggle with hiring enough data scientists.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

datapine

NOVEMBER 19, 2019

While we’ve seen traces of this in 2019, it’s in 2020 that computer vision will make a significant mark in both the consumer and business world. Already in our shortlist of tech buzzwords 2019, artificial intelligence is on the front scene for next year again. Artificial Intelligence (AI). Connected Retail. Hyperautomation.

Technology 279

Technology Internet of Things IT IoT 279

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

DataKitchen

OCTOBER 5, 2022

Observability also validates that your data transformations, models, and reports are performing as expected. A 2019 DataKitchen/Eckerson survey found that 79% of companies have more than three data-related errors in their pipelines per month. A survey of data engineers conducted by DataKitchen in 2022 revealed some shocking statistics.

Testing 169

Testing Risk Data Processing Statistics 169

DataKitchen

OCTOBER 5, 2022

Observability also validates that your data transformations, models, and reports are performing as expected. A 2019 DataKitchen/Eckerson survey found that 79% of companies have more than three data-related errors in their pipelines per month. A survey of data engineers conducted by DataKitchen in 2022 revealed some shocking statistics.

Testing 130

Testing Risk Data Processing Statistics 130

Sisense

JUNE 16, 2020

By mid-March, Soft Stuff’s business plummeted, falling to 20% of where it had been at the same time in 2019. With no end to the pandemic in sight and no clear future date when hotels would again be filled and convention centers hosting events, Soft Stuff needed to pivot its business model to survive.

Analytics 69

Analytics Data-driven Sales Data Processing 69

Insight

FEBRUARY 19, 2020

My goal here is not to improve upon the current prediction algorithms but rather to describe a model I devised, called ReelRisk , that uses random resampling to generate a range of predictions which can then be used as a risk assessment tool to determine early on whether to fund a movie. The solid line shows the y=x line for comparison.

Risk 67

Risk ROI Modeling Metrics 67