Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistics and programming go hand in hand. Mastering statistical techniques and knowing how to implement them via a programming language are essential building blocks for advanced analytics. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

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. Because statistics: Last is the inherently probabilistic nature of ML.

Risk 364

Risk Modeling Data Processing Reporting 364

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 272

Modeling Machine Learning Predictive Modeling Consulting 272

Smarten

NOVEMBER 26, 2024

The business can harness the power of statistics and machine learning to uncover those crucial nuggets of information that drive effective decision, and to improve the overall quality of data. This helps you select the predictors that have the greatest impact, making it easier to create an effective predictive model.

Machine Learning 64

Machine Learning Data Quality Predictive Modeling Metadata 64

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. Your Chance: Want to extract the maximum potential out of your data?

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

O'Reilly on Data

DECEMBER 12, 2019

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] This article is meant to be a short, relatively technical primer on what model debugging is, what you should know about it, and the basics of how to debug models in practice.

Machine Learning 275

Machine Learning Modeling Testing Risk Management 275

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 Predictive Analytics Recreation/Entertainment 110

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. This article is a short summary of my understanding of the definition of data science in 2018. Numerous articles have been published on the meaning of data science in the past six years.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Smarten

MAY 31, 2022

The technology research firm, Gartner has predicted that, ‘predictive and prescriptive analytics will attract 40% of net new enterprise investment in the overall business intelligence and analytics market.’ It is meant to identify crucial relationships and opportunities and risks and help the organization to accurately predict: Growth.

Predictive Analytics 69

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 69

O'Reilly on Data

MARCH 23, 2021

On top of this, pre-existing societal biases are being reinforced and promulgated at previously unheard of scales as we increasingly integrate machine learning models into our daily lives. Put simply, we are reduced to the inputs of an algorithm.

Internet Publishing and Broadcasting 342

Internet Publishing and Broadcasting Broadcasting Data-driven Publishing 342

Smarten

APRIL 19, 2024

In this article, we will discuss the current state of AI in analytics, as well as the future of this burgeoning industry and how it can be applied to analytics to simplify and clarify results and to make analytics easier for businesses and business users to leverage.

Analytics 59

Analytics Predictive Modeling KPI Machine Learning 59

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. We’ll actually do this later in this article. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. y_pred=predict(xb, y_val) val-auc=auc(y_pred,y_val).

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

DataRobot Blog

JULY 28, 2022

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. I thought of the solutions of the top team in a Data Science Competition for LANL Earthquake Prediction.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Smarten

JUNE 29, 2018

This article focuses on the Independent Samples T Test technique of Hypothesis testing. 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. About Smarten.

Testing 40

Testing Statistics IT Business Intelligence 40

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Smarten

JUNE 20, 2018

In this article, we will focus on the identification and exploration of data patterns and the trends that data reveals. In prediction, the objective is to “model” all the components to some trend patterns to the point that the only component that remains unexplained is the random component. Stationary/Stationarity.

Forecasting 40

Forecasting Statistics Business Intelligence Predictive Modeling 40

Smarten

JUNE 29, 2018

This article explains the Karl Pearson Correlation method of analysis, and how it can be applied in business. Correlation is a statistical measure that indicates the extent to which two variables fluctuate together. What is the Karl Pearson Correlation Analytical Technique?

Enterprise 40

Enterprise Statistics IT Business Intelligence 40

Smarten

JUNE 29, 2018

This article describes the Simple Linear Regression method of analysis. Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). What is Simple Linear Regression?

Enterprise 40

Enterprise Statistics Sales Business Intelligence 40

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Smarten

JUNE 26, 2018

This article describes chi square test of association and hypothesis testing. It is used to determine whether there is a statistically significant association between the two categorical variables. What is the Chi Square Test of Association Method of Hypothesis Testing?

Testing 40

Testing Statistics IT Business Intelligence 40

Smarten

JUNE 29, 2018

This article discusses the Paired Sample T Test method of hypothesis testing and analysis. At 95% confidence level (5% chance of error): As p-value = 0.041 which is less than 0.05, there is a statistically significant difference between means of pre and post sample values. What is the Paired Sample T Test?

Business Analysis 40

Business Analysis Testing Statistics Advertising 40

Smarten

JUNE 29, 2018

This article describes the analytical technique of multiple linear regression. Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more variables (X, and Y). What is Multiple Linear Regression Analysis?

Business Analysis 40

Business Analysis IT Sales Statistics 40

Smarten

JUNE 29, 2018

This article looks at the ARIMAX Forecasting method of analysis and how it can be used for business analysis. An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms.

Forecasting 40

Forecasting Enterprise IT Business Intelligence 40

Smarten

JUNE 29, 2018

This article describes the Spearman’s Rank Correlation and how it is used for enterprise analysis. 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.

Business Analysis 40

Business Analysis IT Measurement Statistics 40

Smarten

JUNE 26, 2018

This article provides a brief explanation of the ARIMA method of analytical forecasting. Autoregressive Integrated Moving Average (ARIMA) predicts future values of a time series using a linear combination of its past values and a series of errors. ’ The ARIMA model is suggested for short term forecasting.

Forecasting 40

Forecasting Enterprise IT Sales 40

Smarten

JUNE 29, 2018

This article discusses the analytical technique known as Sampling and provides a brief explanation of two types of sampling analysis, and how each of these methods is applied. What is Sampling Analysis? A random sample from each of these subgroups is taken in proportion to the subgroup size relative to the population size.

Analytics 40

Analytics Statistics Business Intelligence Predictive Modeling 40

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

Smarten

JULY 10, 2018

This article summarizes our recent article series on the definition, meaning and use of the various algorithms and analytical methods and techniques used in predictive analytics for business users, and in augmented data preparation and augmented data discovery tools. Use Case(s): Weather Forecasting, Fraud Analysis and more.

Analytics 40

Analytics Forecasting Statistics Sales 40

Smarten

JUNE 27, 2018

This article presents a brief explanation of Outliers, and how this type of analysis is used. 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. What is Outlier Analysis?

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 28, 2018

This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes. 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

Smarten

JUNE 25, 2018

This article provides a brief explanation of the KMeans Clustering algorithm. Smarten Augmented Analytics tools include plug n’ play predictive analytics , assisted predictive modeling , smart data visualization , self-serve data preparation and clickless analytics for search analytics with natural language processing (NLP).

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

Smarten

JUNE 26, 2018

This article provides a brief definition of the multinomial-logistic regression classification algorithm and its uses and benefits. 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

Smarten

JUNE 26, 2018

This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining. 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. What is the FP Growth Algorithm?

Business Intelligence 40

Business Intelligence Sales Predictive Modeling Statistics 40

Smarten

JUNE 29, 2018

In this article, we will discuss the Binary Logistic Regression Classification method of analysis, and how it can be used in business. 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

Smarten

JUNE 27, 2018

This article provides a brief explanation of the SVM Classification method of analytics. 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. What is SVM Classification Analysis?

Business Analytics 40

Business Analytics Analytics IT Business Intelligence 40

Domino Data Lab

OCTOBER 23, 2019

This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. 05) in predicting changes in attendance. The lower the RMSE, the better the prediction.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Smarten

JUNE 29, 2018

In this article, we will discuss the KNN Classification method of analysis. Smarten Augmented Analytics tools include plug n’ play predictive analytics , assisted predictive modeling , smart data visualization , self-serve data preparation and clickless analytics for search analytics with natural language processing (NLP).

Enterprise 40

Enterprise Business Intelligence Predictive Modeling Statistics 40