Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 121

Predictive Analytics Analytics Predictive Modeling Data mining 121

Smarten

JUNE 29, 2018

This article looks at the ARIMAX Forecasting method of analysis and how it can be used for business analysis. What is ARIMAX Forecasting? This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity. About Smarten.

Forecasting 40

Forecasting Enterprise IT Business Intelligence 40

Smarten

JUNE 26, 2018

This article provides a brief explanation of the ARIMA method of analytical forecasting. What is ARIMA 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. p: to apply autoregressive model on series.

Forecasting 40

Forecasting Enterprise IT Sales 40

Jedox

MAY 17, 2022

What Predictive Analytics Cannot Forecast. Predictive Analytics Example in Finance. A Brief History of Predictive Analytics. No industry has attempted to do more with predictive analytics than the financial services industry. What is Predictive Analytics?

Predictive Analytics 83

Predictive Analytics Finance Forecasting Analytics 83

Smarten

AUGUST 21, 2019

Using the Smarten approach, users can quickly and easily prepare and analyze data and visualize and explore data, notate and highlight data and share data with others. Users can highlight trends and patterns, test hypotheses and theories to reduce business risk, and easily predict and forecast results.

Analytics 51

Analytics Predictive Modeling Visualization Statistics 51

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 59

Machine Learning Data Science Statistics Deep Learning 59

FineReport

MAY 29, 2020

Business Intelligence is commonly divided into four different types: reporting, analysis, monitoring, and prediction. Static reports cannot be changed by the end-users, while interactive reports allow you to navigate the report through various hierarchies and visualization elements. The ‘data’ part is the statistics and data display.

Reporting 94

Reporting OLAP Visualization Software 94

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. Stationary/Stationarity.

Forecasting 40

Forecasting Statistics Business Intelligence Predictive Modeling 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). 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. About Smarten.

Business Analysis 40

Business Analysis IT Sales Statistics 40

Smarten

JUNE 29, 2018

Weather Forecasting – Based on temperature, humidity, pressure etc., an organization can predict if it will be rainy/sunny/windy tomorrow. a business can predict the likelihood of fraud. It is useful for making predictions and forecasting data based on historical results. About Smarten.

Enterprise 40

Enterprise IT Business Intelligence Forecasting 40

Smarten

JUNE 27, 2018

SVM Classification Analysis can be used for many analytical tasks: Credit/Loan Approval Analysis – Given a list of client transactional attributes, a business can predict whether a client will default on a loan. Medical Diagnosis – Given a list of symptoms, a doctor can predict if a patient has a particular disease.

Business Analytics 40

Business Analytics Analytics IT Business Intelligence 40

Smarten

JUNE 29, 2018

Business Benefit: Given the health and body profile of a patient and the recent treatments and drugs prescribed for the patient, the doctor can predict the probability and make recommendations on changes in treatment/drugs. About Smarten.

Enterprise 40

Enterprise 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.

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

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

Smarten

JULY 10, 2018

Use Case(s): Weather Forecasting, Fraud Analysis and more. Use Case(s): Predict if loan default based on attributes of applicant; predict likelihood of successful treatment of new patient based on patient attributes and more. ARIMAX Forecasting: What is ARIMAX Forecasting and How is it Used for Enterprise Analysis?

Analytics 40

Analytics Forecasting Statistics Sales 40

datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2) Data Discovery/Visualization. Data exploded and became big.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

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. Human brains are not well suited to visualizing anything in greater than three dimensions. Example 11.6 Example 11.9

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Smarten

JULY 3, 2017

When an enterprise selects a self-serve business intelligence solution with Advanced Data Discovery , Smart Data Visualization , Plug n’ Play Predictive Analysis and Self-Serve Data Preparation , it can create an environment where business users are empowered and become greater assets to the organization. It’s simple!

Predictive Modeling 40

Predictive Modeling Visualization Business Intelligence Experimentation 40

Smarten

DECEMBER 3, 2023

So, you can ask, ‘Who sold the most bakery items in the Southwest region from September through November of 2022,’ and you will receive your results and can visualize your data in a way that makes sense to you and to your team. What is self-service analytics? We should probably explain before we move on.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

Smarten

JUNE 21, 2018

This article provides a brief explanation of the Holt-Winters Forecasting model and its application in the business environment. What is the Holt-Winters Forecasting Algorithm? The Holt-Winters algorithm is used for forecasting and It is a time-series forecasting method. 2) Double Exponential Smoothing Use Case.

Forecasting 40

Forecasting Enterprise IT Predictive Modeling 40

datapine

DECEMBER 4, 2019

Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. 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.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Jet Global

MAY 1, 2023

Healthcare is forecasted for significant growth in the near future. 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. It’s all about context.

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