O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. And last is the probabilistic nature of statistics and machine learning (ML).

Risk 359

Risk Modeling Data Processing Software 359

Domino Data Lab

OCTOBER 7, 2020

The Curse of Dimensionality , or Large P, Small N, ((P >> N)) , problem applies to the latter case of lots of variables measured on a relatively few number of samples. Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Smarten

APRIL 12, 2023

Smarten Augmented Analytics tools include Assisted Predictive Modeling , Smart Data Visualization , Self-Serve Data Preparation , Sentiment Analysis , and Clickless Analytics with natural language processing (NLP) for search analytics.

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

BizAcuity

APRIL 1, 2023

Widely used to discover trends, patterns, check assumptions and spot anomalies or outliers, EDA involves a variety of techniques including statistical analysis, and machine learning to gain a better understanding of data. Building a predictive model is a continuous process and commitment.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

CIO Business Intelligence

JULY 24, 2023

We started by giving this data to the technical staff of the clubs, but we decided it was the moment to offer these advanced statistics to the fans and the media,” Bruno says. “We It has also developed predictive models to detect trends, make predictions, and simulate results.

Broadcasting 98

Broadcasting Recreation/Entertainment Testing Machine Learning 98

BizAcuity

JANUARY 18, 2023

Widely used to discover trends, patterns, check assumptions and spot anomalies or outliers, EDA involves a variety of techniques including statistical analysis, and machine learning to gain a better understanding of data. Predictive Analytics. Predictive modeling for flagging suspicious activity. These include-.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

Smarten

JUNE 17, 2022

.” The Information Technology Amendment Act of 2009 designated CERT-IN as the national agency to perform functions for cyber security, including the collection, analysis and dissemination of information on cyber incidents, as well as taking emergency measures to handle incidents and coordinating cyber incident response activities.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

Smart Data Collective

MARCH 28, 2022

There are four main types of data analytics: Predictive data analytics: It is used to identify various trends, causation, and correlations. It can be further classified as statistical and predictive modeling, but the two are closely associated with each other. Improved decision-making will create more successful outcomes.

Data Analytics 86

Data Analytics Software Analytics Statistics 86

DataRobot Blog

JULY 28, 2022

Companies are emphasizing the accuracy of machine learning models while at the same time focusing on cost reduction, both of which are important. In addition to the accuracy of the models we built, we had to consider business metrics, cost, interpretability, and suitability for ongoing operations. Sensor Data Analysis Examples.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Jedox

MAY 17, 2022

Predictive analytics is a discipline that’s been around in some form since the dawn of measurement. We’ve always been trying to predict the future; go back in history to look at prognosticators like Nostradamus and many other prophets. What is Predictive Analytics? The stock market is an excellent example of this.

Predictive Analytics 81

Predictive Analytics Finance Forecasting Analytics 81

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. Your marketing strategy is only as good as your ability to deliver measurable results. Machine Learning and Predictive Modeling of Customer Churn.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

Smarten

JUNE 29, 2018

For example, one might consider two groups of participants that are measured at two different “time points” or two groups that are subjected to two different “conditions” Paired T Test is used to evaluate the before and after of a situation, treatment, condition, etc. is the same in two related groups.

Business Analysis 40

Business Analysis Testing Statistics Advertising 40

Smarten

JUNE 29, 2018

Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more variables (X, and Y). Business Problem: An ecommerce company wants to measure the impact of product price, product promotions, and holiday seasonality on product sales. What is Multiple Linear Regression Analysis?

Business Analysis 40

Business Analysis IT Sales Statistics 40

Smarten

JUNE 29, 2018

Correlation is a statistical measure that indicates the extent to which two variables fluctuate together. The Karl Pearson’s correlation measures the degree of linear relationship between two variables. A positive correlation indicates the extent to which those variables increase or decrease in parallel.

Enterprise 40

Enterprise IT Statistics Business Intelligence 40

Smarten

JUNE 29, 2018

Correlation is a statistical measure that indicates the extent to which two variables fluctuate together A positive correlation indicates the extent to which those variables increase or decrease in parallel. The Spearman’s Rank Correlation is a measure of correlation between two ranked (ordered) variables. About Smarten.

Business Analysis 40

Business Analysis IT Measurement Statistics 40

Smarten

JUNE 29, 2018

The independent sample t-test is a statistical method of hypothesis testing that determines whether there is a statistically significant difference between the means of two independent samples. One is a dimension containing two values and the other is a measure. Let’s look at a sample of the Independent t-test on two variables.

Testing 40

Testing Statistics IT Business Intelligence 40

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

Smarten

JUNE 29, 2018

Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). Business Problem: An eCommerce company wants to measure the impact of product price on product sales. This article describes the Simple Linear Regression method of analysis.

Enterprise 40

Enterprise Sales Statistics Business Intelligence 40

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

Alation

APRIL 25, 2023

Predictive models indicate that the machine learning market will grow at a compound annual growth rate (CAGR) of 38.8% Whether you deal in customer contact information, website traffic statistics, sales data, or some other type of valuable information, you’ll need to put a framework of policies in place to manage your data seamlessly.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

Smarten

JUNE 26, 2018

Logistic regression measures the relationship between the categorical target variable and one or more independent variables It deals with situations in which the outcome for a target variable can have two or more possible types. What is the Multinomial-Logistic Regression Classification Algorithm?

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Alation

APRIL 25, 2023

Predictive models indicate that the machine learning market will grow at a compound annual growth rate (CAGR) of 38.8% Whether you deal in customer contact information, website traffic statistics, sales data, or some other type of valuable information, you’ll need to put a framework of policies in place to manage your data seamlessly.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Smarten

JUNE 29, 2018

Logistic regression measures the relationship between the categorical target variable and one or more independent variables. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Insight

AUGUST 5, 2020

Summary statistics (i.e. This created a summary features matrix of 7472 recordings x 176 summary features, which was used for training emotion label prediction models. Prediction models An Exploratory Data Analysis showed improved performance was dependent on gender and emotion. the Mel-frequency cepstrum).

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

Domino Data Lab

NOVEMBER 17, 2020

High throughput screening technologies have been developed to measure all the molecules of interest in a sample in a single experiment (e.g., Predictive models fit to noise approach 100% accuracy. Each of these behaviors wreak havoc on statistical analyses. Introduction. Pairwise distances between points become the same.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

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. It enables us to build context around the data at hand and lets us develop appropriate models that then can be interpreted correctly. Pandas Profiler. of missing cells.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

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. Let's start with a simple example to illustrate the problems of predicting and intervening.

Machine Learning 73

Machine Learning Modeling Statistics Predictive Modeling 73

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. Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

BI-Survey

JANUARY 29, 2023

In the context of corporate planning, predictive planning and forecasting, it is therefore a major trend to use predictive models based on statistical methods and ML for forecasting and thorough analysis. Nearly every organization that adopts predictive planning is likely to profit from it.

Forecasting 52

Forecasting Predictive Modeling Measurement Finance 52

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. This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. All of this will be done in the spirit of solving real-world problems in a real-world way.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Insight

FEBRUARY 19, 2020

Arthur de Vany’s Hollywood Economics and Kaggle’s recent box office prediction challenge ) and current attempts are using increasingly sophisticated techniques. Feature Selection and Engineering Most of the inputs to my model were taken either as is from the data source, or with minimal processing.

Risk 67

Risk ROI Modeling Metrics 67

Cloudera

NOVEMBER 2, 2020

the speed at which queries run is faster – small, selective queries are measured in 10s or 100s of milliseconds; large, scan- or compute-heavy queries are processed at very high bandwidth. Data Model. Small or medium sized models; dimensional and denormalized mainly, occasionally more normalized model.

Data Warehouse 93

Data Warehouse Optimization Dashboards Interactive 93

Domino Data Lab

AUGUST 20, 2019

Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models. It is a framework for approaching ML as well as providing techniques for extracting features from raw data that can be used within the models. We can also modify the features before we start the modeling process.

Testing 68

Testing Modeling Interactive Measurement 68

Smarten

JULY 10, 2018

Use Case(s): Manufacturing unit manager analyzes statistical significance of cycle time difference, pre and post process change, determine whether sales increased following a particular campaign and more. Use Case(s): Measure the impact of product price on product sales, measure the impact of temperature on crop yield an more.

Analytics 40

Analytics Forecasting Statistics Sales 40

Smarten

MARCH 13, 2024

Augmented Analytics and Citizen Data Scientists are not meant to replace refined data modeling or the role of Data Scientists, but rather can supplement and support analytics across the enterprise. Role – Analyze and refine data for 100% accuracy and strategic use, act as expert, statistical expert.

Analytics 52

Analytics Predictive Modeling Strategy Enterprise 52

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

datapine

NOVEMBER 27, 2019

While we work on programs to avoid such inconvenience , AI and machine learning are revolutionizing the way we interact with our analytics and data management while increment in security measures must be taken into account. However, businesses today want to go further and predictive analytics is another trend to be closely monitored.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361