O'Reilly on Data

DECEMBER 12, 2019

If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. Small residuals usually mean a model is right, and large residuals usually mean a model is wrong. You’ve even discovered a few problems with your ML model. What can you do?

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

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. Based on this value, the grocery store manager can decide on its marketing strategies for better sales and increased revenue.

Testing 40

Testing Statistics IT Business Intelligence 40

Sisense

AUGUST 11, 2019

Typically, this involves using statistical analysis and predictive modeling to establish trends, figuring out why things are happening, and making an educated guess about how things will pan out in the future. BA primarily predicts what will happen in the future. What About “Business Intelligence”?

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

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). For instance, if promotions and holiday seasons are significant factors, these factors should be given more focus when devising a marketing strategy. What is Multiple Linear Regression Analysis?

Business Analysis 40

Business Analysis IT Sales Statistics 40

Smarten

JUNE 25, 2018

Business Problem: Discount Analysis and Customer Retention will help the organization to target discounts to specific customers and the business will need to visualize ‘segments of sales group based on discount behavior’ and ‘customer churn to identify segments of customers on the verge of leaving’. Use Case – 2.

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

Smarten

JUNE 20, 2018

The business can use this information for forecasting and planning, and to test theories and strategies. 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. Linear Trend. Stationary/Stationarity.

Forecasting 40

Forecasting Statistics Business Intelligence Predictive Modeling 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

Smarten

JUNE 26, 2018

Business Problem: A research agency wants to predict the likelihood of each election candidate being voted on by each voter and in turn devise a strategy to take proactive steps. Let’s look at two use cases: Use Case – 1.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 29, 2018

The enterprise can also identify any gap between the targeted and estimated growth and develop an appropriate strategy to reduce this gap in order to achieve targets and results. Business Benefit: By analyzing the various combinations of predictor variables, the business can forecast product growth, trends, patterns and seasonality, if any.

Forecasting 40

Forecasting Enterprise IT Business Intelligence 40

Smarten

JUNE 27, 2018

In this example, the segments that have a response rate higher than the overall response rate can be targeted first since they will require less effort to convert to a purchase, whereas a different marketing strategy must be devised for the lower segments (segments that have a response rate less than the overall rate).

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Birst BI

AUGUST 23, 2018

Data scientists building AI applications require numerous skills – data visualization, data cleansing, artificial intelligence algorithm selection and diagnostics. Our recruits come from various disciplines – chemistry, physics, math, statistics, operations research, and economics. Mona Patel works in Birst’s Product Strategy team.

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

FineReport

APRIL 24, 2024

What is Data Visualization Understanding the Concept Data visualization, in simple terms, refers to the presentation of data in a visual format. By utilizing visual elements, data visualization allows individuals to grasp difficult concepts or identify new patterns within the data.

Visualization 52

Visualization Dashboards Interactive Key Performance Indicator 52

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

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

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

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

Smarten

JUNE 26, 2018

Business Problem: A retail store manager wants to conduct Market Basket analysis to come up with better strategy of products placement and product bundling. 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.

Business Intelligence 40

Business Intelligence Sales Predictive Modeling Statistics 40

Cloudera

NOVEMBER 2, 2020

As such, many customers are building RTDW applications as part of their overall strategy of using Cloudera to modernize their data warehouse practice. alert when threshold exceeded over a rolling window of statistics on the data, score the event data against a predictive model to decide which action to take next).

Data Warehouse 91

Data Warehouse Optimization Dashboards Interactive 91

Smarten

JUNE 29, 2018

Business Problem: A retail store manager wants to conduct Market Basket analysis to come up with a better strategy of product placement and product bundling. 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.

Business Analysis 40

Business Analysis IT Business Intelligence Sales 40

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.

Modeling 40

Modeling Statistics Advertising Testing 40

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

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. Visually, the shape has relatively flat sides.

Testing 68

Testing Modeling Interactive Measurement 68

Smarten

MARCH 13, 2024

Self-Serve Data Preparation Assisted Predictive Modeling Smart Data Visualization Machine Learning and Natural Language Processing (NLP) Clickless Search Analytics EXPECTATIONS AND RESULTS Once you have chosen the right augmented analytics solution, you must establish appropriate expectations.

Analytics 52

Analytics Predictive Modeling Strategy Enterprise 52

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

datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. In 2020, BI tools and strategies will become increasingly customized. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

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

JUNE 21, 2018

Time series forecasting methods are used to extract and analyze data and statistics and characterize results to more accurately predict the future based on historical data. Business Benefit : If projected claims are lower than expected then proper marketing strategy can be devised to improve sales.

Forecasting 40

Forecasting Enterprise IT Predictive Modeling 40

Jet Global

MAY 1, 2023

Plus, there is an expectation that tools be visually appealing to boot. In the past, data visualizations were a powerful way to differentiate a software application. Their dashboards were visually stunning. Today, free visualizations seem to be everywhere. It’s all about context. End users expect more from analytics too.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MAY 9, 2024

For example, streaming data from sensors to an analytics platform where it is processed and visualized immediately. Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52