Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal. When there are many variables the Curse of Dimensionality changes the behavior of data and standard statistical methods give the wrong answers. Data Has Properties.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

O'Reilly on Data

JUNE 18, 2019

For example, a complex sophisticated model for finding duplicates or matching schema is the least of our worries if we cannot even enumerate all possible pairs that need to be checked. HoloClean adopts the well-known “noisy channel” model to explain how data was generated and how it was “polluted.”

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

IBM Big Data Hub

MAY 1, 2023

The early versions of AI were capable of predictive modelling (e.g., The four categories of predictive modelling, robotics, speech and image recognition are collectively known as algorithm-based AI or Discriminative AI. It offers customers and the insurer’s system to interact in a human-like manner.

Insurance 83

Insurance Predictive Modeling Cost-Benefit Statistics 83

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. On-site courses are available in Munich. Remote courses are also available. Switchup rating: 5.0 (out Data Science Dojo.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

FineReport

MAY 29, 2020

BI Reports can vary in their interactivity. Static reports cannot be changed by the end-users, while interactive reports allow you to navigate the report through various hierarchies and visualization elements. Interactive reports support drilling down or drilling through multiple data levels at the click of a mouse.

Reporting 94

Reporting OLAP Visualization Software 94

Business Over Broadway

SEPTEMBER 27, 2018

Figure 1 includes a good illustration of different data sets and how they fall along these two size-related dimensions (For the interested reader, check out the figure in an interactive graphic ). Machine Learning and Predictive Modeling of Customer Churn. segmentation on steroids).

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

The Data Visualisation Catalogue

APRIL 4, 2019

I’ve implemented DataView in my own work and find it an excellent way to organize investment information, do data discovery and create predictive models. Application #2: Creating and visualizing multi-variable relationships, which is particularly useful in creating predictive models. Is momentum important? And so on.

Dashboards 73

Dashboards Predictive Modeling Visualization Modeling 73

O'Reilly on Data

MARCH 23, 2021

Content creators and content consumers are connected, share information, and develop mental models of the world, along with shared or distinct realities, based on the information they consume. Online spaces are novel forms of community: people who haven’t met and may never meet in real life interacting in cyberspace.

Internet Publishing and Broadcasting 317

Internet Publishing and Broadcasting Broadcasting Data-driven Interactive 317

Birst BI

OCTOBER 30, 2018

The foundation of predictive analytics is based on probabilities. To generate accurate probabilities of future behavior, predictive analytics combine historical data from any number of applications with statistical algorithms. A well-designed credit scoring algorithm will properly predict both the low- and high-risk customers.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Insight

AUGUST 5, 2020

While most of these signals are implicitly communicated during human-to-human interaction, we do not have a method for quantifying feeling and mood through individual behavioral signals expressed on the digital platform. Summary statistics (i.e. Improvements in AUC values across models were also accompanied by increased precision?

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

Smarten

APRIL 19, 2024

Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that data strategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

datapine

MARCH 25, 2022

While some experts try to underline that BA focuses, also, on predictive modeling and advanced statistics to evaluate what will happen in the future, BI is more focused on the present moment of data, making the decision based on current insights. What Is Business Intelligence And Analytics?

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Domino Data Lab

JANUARY 21, 2021

Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task. Each dataset has properties that warrant producing specific statistics or charts.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

Domino Data Lab

NOVEMBER 17, 2020

Classical statistics, developed in the 20 th century for small datasets, do not work for data where the number of variables is much larger than the number of samples (Large P Small N, Curse of Dimensionality, or P >> N data). Predictive models fit to noise approach 100% accuracy. Antimicrobial. Autoimmunity. IL-4, IL-13.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

IBM Big Data Hub

DECEMBER 20, 2023

Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. In other words, ML leverages input data to predict outputs, continuously updating outputs as new data becomes available.

Machine Learning 81

Machine Learning Modeling Deep Learning Predictive Modeling 81

Domino Data Lab

AUGUST 12, 2021

In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase. We covered the importance of Exploratory Analytics including observing the frequency of missing data and computing interactions of all features. Pandas Profiler.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. I was very comfortable with that definition, having spent my PhD years on several predictive modelling tasks, and having worked as a software engineer prior to that.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

FineReport

APRIL 24, 2024

This simplification allows stakeholders to grasp the underlying patterns and trends within the data without getting lost in the complexity of raw numbers and statistics. Interactivity: Incorporating interactive features allows users to explore the data more deeply, gaining comprehensive insights from the visualizations.

Visualization 52

Visualization Dashboards Interactive Key Performance Indicator 52

The Unofficial Google Data Science Blog

JANUARY 31, 2017

By OMKAR MURALIDHARAN, NIALL CARDIN, TODD PHILLIPS, AMIR NAJMI Given recent advances and interest in machine learning, those of us with traditional statistical training have had occasion to ponder the similarities and differences between the fields. Large scale prediction systems are often bad at counterfactual predictions out of the box.

Machine Learning 73

Machine Learning Modeling Statistics Predictive Modeling 73

Cloudera

NOVEMBER 2, 2020

It is very hard to maintain interactive performance, over large amounts of data that is arriving very fast, some of which might need updates, with a large number of queries of varying patterns. Fast ingest of streaming data, interactive queries, very high scale. Mutability, random access, fast scans, interactive queries.

Data Warehouse 91

Data Warehouse Optimization Dashboards Interactive 91

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. The describe function on a Pandas DataFrame provides descriptive statistics, including the number of columns, in this case 27, and median (this is the 50 percent row), for each column. 05) in predicting changes in attendance.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

AUGUST 20, 2019

There are three major strategies: manual techniques based on our knowledge of the problem and the data, random sampling techniques that use coin flipping to keep or discard data, and model-based techniques that attempt to keep features that interact well with our learning model. Relationships and Interactions.

Testing 68

Testing Modeling Interactive Measurement 68

The Unofficial Google Data Science Blog

MARCH 31, 2016

Finally, through a case study of a real-world prediction problem, we also argue that Random Effect models should be considered alongside penalized GLM's even for pure prediction problems. Random effects models are a useful tool for both exploratory analyses and prediction problems. hi-fly-airlines 123.com

Modeling 40

Modeling Statistics Advertising Testing 40

Darkhorse

JULY 6, 2017

Let’s further pretend you’re starting out with the aim of doing a big predictive modeling thing using machine learning. Summary statistics are not your friend – they may in fact lead you astray. This is level seven – when you interact with your stakeholders using data. I hope you enjoy that sort of thing.

Data Lake 74

Data Lake Sales Machine Learning Visualization 74

Smarten

MAY 17, 2024

For example, a Data Scientist can use PMML integration to Import models created in other languages like R and Python with a PMML format, and use those models with analytical workflows to roll out predictive models to users, enabling business users to participate in analysis and making Data Scientists more productive.

Analytics 52

Analytics Predictive Modeling ROI Cost-Benefit 52

Rocket-Powered Data Science

OCTOBER 6, 2023

What is the point of those obvious statistical inferences? The point is that the 100% association between the event and the preceding condition has no special predictive or prescriptive power. How do predictive and prescriptive analytics fit into this statistical framework?

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Diagnostic Analytics 157

Smarten

JUNE 25, 2018

This article provides a brief explanation of the definition and uses of the Descriptive Statistics algorithms. What is a Descriptive Statistics? Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data.

Statistics 40

Statistics Enterprise Measurement Business Intelligence 40

Smarten

JULY 26, 2022

“Users can analyze and interact with data with full visibility of dashboards, reports and other BI objects.” The Smarten Augmented Analytics and BI platform allows business users to leverage deep dive analysis using highly interactive dashboards, reports and NLP search on a mobile device.

Key Performance Indicator 74

Key Performance Indicator Analytics Business Intelligence Dashboards 74

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] GloVe and word2vec differ in their underlying methodology: word2vec uses predictive models, while GloVe is count based. Interactive bokeh plot of two-dimensional word-vector data. Interactive bokeh plot of two-dimensional word-vector data.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. However, businesses today want to go further and predictive analytics is another trend to be closely monitored. Over the past decade, business intelligence has been revolutionized. Data exploded and became big.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

MARCH 24, 2020

HoloClean decouples the task of data cleaning into error detection (such as recognizing that the location “cicago” is erroneous) and repairing erroneous data (such as changing “cicago” to “Chicago”), and formalizes the fact that “data cleaning is a statistical learning and inference problem.”

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

datapine

DECEMBER 4, 2019

Without a doubt, it’s a big technological advancement, and one of the big statistics buzzwords, but the extent to which it is believed to be already applied is vastly exaggerated. The commercial use of predictive analytics is a relatively new thing. The accuracy of the predictions depends on the data used to create the model.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Smarten

DECEMBER 3, 2023

As Springboard notes, ‘Augmented Analytics is an example of human machine interaction in the data science field.’ The augmented analytics solution is typically supported by natural language processing (NLP) to support users in the query and results process.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

Data Science and Beyond

JANUARY 8, 2017

Popularity is a function of inbound links (weighted by site credibility), and of user interaction with the presented results (e.g., My main problem with the Kissmetrics infographic is that it helps feed an illusion of understanding that is prevalent among those with no statistical training.

Statistics 100

Statistics Modeling Predictive Modeling Marketing 100