Smarten

JUNE 10, 2022

Data Discovery including self-serve data preparation, smart data visualization with charts, graphs and other visualizations for clarity and decisions. Predictive Modeling to support business needs, forecast, and test theories. KPIs allow the business to establish and monitor KPIs for objective metrics.

Slice and Dice 52

Slice and Dice Analytics Key Performance Indicator Business Intelligence 52

Juice Analytics

MARCH 11, 2021

3) That’s where our data visualization and user experience capabilities helped them turn this data into a web-based analytical tool that focused users on the metrics and peer groups they cared about. There are many paths to consider: Visual representations that reveal patterns in the data and make it more human readable.

Predictive Modeling 128

Predictive Modeling Visualization Reporting Interactive 128

Smarten

NOVEMBER 22, 2019

Augmented analytics and tools like Smart Visualization and Self-Serve Data Preparation , as well as Assisted Predictive Modeling can provide guidance and auto-suggestions and recommendations to make users more comfortable in adopting analytics and achieving positive outcomes.

Predictive Modeling 67

Predictive Modeling Data-driven Forecasting Visualization 67

Smart Data Collective

SEPTEMBER 17, 2020

Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. It can come in handy when tracking, analyzing, and sharing metrics connected with employee performance.

Big Data 142

Big Data Finance Risk Management Risk 142

Smarten

NOVEMBER 8, 2022

By providing this course as a free online offering Smarten hopes to further support and encourage users and businesses to embrace the very real benefits of the Citizen Data Scientist approach to analytics and objective, data-driven metrics and results. About Smarten.

Business Intelligence 52

Business Intelligence Predictive Modeling Statistics Data-driven 52

Birst BI

OCTOBER 30, 2018

Descriptive analytics techniques are often used to summarize important business metrics such as account balance growth, average claim amount and year-over-year trade volumes. The credit scores generated by the predictive model are then used to approve or deny credit cards or loans to customers. Accounts in use.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

FineReport

APRIL 9, 2024

The integration of clinical data analysis tools empowers healthcare providers to leverage predictive analytics for proactive decision-making. Through the utilization of predictive models, clinicians can forecast patient outcomes and resource needs, enabling early intervention and personalized care delivery.

Digital Transformation 52

Digital Transformation Business Intelligence Data-driven Visualization 52

Sisense

APRIL 10, 2020

It’s quite popular for its visualizations: charts, graphs, pictures, and various plots. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Smarten

FEBRUARY 17, 2022

If a user is presenting a recommendation or suggestion in a staff meeting, the organization must be willing to support the idea that the user must base his/her recommendation on data acquired from the augmented analytics solution, and require that reporting and suggestions are based on facts and metrics rather than opinion and guesswork.

Predictive Modeling 76

Predictive Modeling Digital Transformation Enterprise Data Science 76

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. Interpretable ML models and explainable ML.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. It’s also necessary to understand data cleaning and processing techniques.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

DataKitchen

SEPTEMBER 21, 2023

Moreover, advanced metrics like Percentage Regional Sales Growth can provide nuanced insights into business performance. Data in Use pertains explicitly to how data is actively employed in business intelligence tools, predictive models, visualization platforms, and even during export or reverse ETL processes.

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

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

Birst BI

AUGUST 23, 2018

Data scientists building AI applications require numerous skills – data visualization, data cleansing, artificial intelligence algorithm selection and diagnostics. Assess and validate the model with analysts and business users. Use Birst Pronto to implement the model by creating an R transform step that creates a new metric.

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

IBM Big Data Hub

AUGUST 28, 2023

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. If necessary, make adjustments to the preprocessing, representation and/or modeling steps to improve the results.

Data mining 58

Data mining Machine Learning Deep Learning Marketing 58

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

AWS Big Data

MARCH 7, 2023

It enables you to create interactive dashboards, visualizations, and advanced analytics with ML insights. It helps you build, train, and deploy models consuming the data from repositories in the data hub. You can collect metrics and events and analyze them for operational efficiency.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

Smarten

DECEMBER 3, 2023

You cannot present a comprehensive solution and set metrics for success if you don’t understand the issues. What problems will it solve? What opportunities does it present? Involving Stakeholders, Customers, Users, IT, etc. Do not work in a vacuum! in the discussion.

Analytics 40

Analytics Data Warehouse Metrics Interactive 40

Decision Management Solutions

NOVEMBER 13, 2018

Begin with an agile analytic deployment platform, not with visualization. ” They analyze the data available and try to see what analytic models it contains. Begin with an agile analytic deployment platform, not with visualization. This framework has three elements: Begin with decisions, not with data.

Insurance 40

Insurance Analytics Modeling Visualization 40

Smarten

JUNE 29, 2018

In this case, the metrics would be, “salary”, and the two related groups would be the two different “time points”; that is, salaries “before” and “after” completion of the PhD program.

Business Analysis 40

Business Analysis Testing Statistics Advertising 40

Smarten

APRIL 19, 2024

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

O'Reilly on Data

JANUARY 17, 2023

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

Smarten

DECEMBER 3, 2023

‘Giving your team the right tools and a simple way to manage the overwhelming flow of data is crucial to business success.’

Analytics 40

Analytics Cost-Benefit Data Quality Consulting 40

CIO Business Intelligence

MARCH 5, 2024

As copilot technology capabilities are changing rapidly, leaders should frequently identify metrics and evaluate strategies. DePaul’s Dumiak adds, “While in Excel, I can ask Microsoft Copilot to summarize tables and give me charts, and suddenly, it’s created pivot tables without ever having to learn the command generation sequence.”

Cost-Benefit 124

Cost-Benefit Advertising Experimentation Software 124

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. In this example, we can define what happened, how , and then why.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

IBM Big Data Hub

DECEMBER 20, 2023

Unsupervised machine learning Unsupervised learning algorithms—like Apriori, Gaussian Mixture Models (GMMs) and principal component analysis (PCA)—draw inferences from unlabeled datasets, facilitating exploratory data analysis and enabling pattern recognition and predictive modeling.

Machine Learning 82

Machine Learning Modeling Deep Learning Predictive Modeling 82

DataKitchen

JANUARY 17, 2022

The business analysts creating analytics use the process hub to calculate metrics, segment/filter lists, perform predictive modeling, “what if” analysis and other experimentation. Visualizations updated per week increased from 50 to 1500. Requirements continually change. Data is not static.

Experimentation 130

Experimentation Data Lake Marketing Predictive Modeling 130

CIO Business Intelligence

AUGUST 15, 2023

Mugunth Vaithylingam, CIO, College of Southern Nevada College of Southern Nevada Teams overseen by CSN CIO Mugunth Vaithylingam combined custom AI visuals, voice, and content to create this first-of-its-kind custom avatar, which is deployed and rendered from a web browser using client-side CPUs.

IT 98

IT Manufacturing IoT Cost-Benefit 98

DataRobot Blog

JANUARY 10, 2023

Now that we have set up the notebook environment, let’s explore the capabilities available in DataRobot Notebooks while walking through the process of training and deploying a readmissions prediction model end-to-end. Built-in Code Snippets and Interactive Visualizations to Accelerate Experimentation. Did you notice?

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

DataRobot Blog

DECEMBER 1, 2022

For example, you could set up a data pipeline that delivers DataRobot predictions to HubSpot to automatically initiate offers within the business rules you set. You could also use the predictions to visualize a BI dashboard or report for your marketing managers to access. Training and Testing Different AI Models.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

AWS Big Data

NOVEMBER 14, 2023

Canva is an online design and visual communication platform. Amazon Managed Service for Apache Flink is used to run Flink applications and consumes data from Kinesis Data Streams to call the prediction model in Amazon SageMaker to deliver the real-time Face-off Probability metric.

IoT 55

IoT Data-driven Data Lake Data Strategy 55

Sisense

SEPTEMBER 25, 2020

No matter your skill, career level, or title, the ability to analyze, organize, and visualize data are vital skills in our world of quickly growing and ever-changing data. Linear regression is a form of supervised learning (or predictive modeling). We have predicted review text with 91.27% accuracy via logistic regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Occam's Razor

JANUARY 7, 2019

Take a class in data visualization. Anyone can make a complicated visual, it takes someone very special (you!) My recommendations: Free Courses: Data Visualization and D3.js js and Data Analysis and Visualization at Udacity. Occam’s Razor : Start with this one: Closing Data's Last-Mile Gap: Visualizing For Impact.

Marketing Analytics 88

Marketing Analytics Marketing Analytics Strategy 88

Occam's Razor

NOVEMBER 18, 2014

Like a vast majority on planet Earth, I love data visualizations. A day-to-day manifestation of this love is on my Google+ or Facebook profiles where 75% of my posts are related to my quick analysis and learnings from a visualization. Data visualized is data understood. But for a visual person like me, this is the ah-ha moment.

Visualization 143

Visualization Slice and Dice Predictive Modeling Metrics 143

Smarten

JUNE 29, 2018

Given a set of transactions, this process aims to find the rules that enable us to predict the occurrence of a specific item based on the occurrence of other items in the transaction. While there are numerous metrics and factors used in this technique, for this example, we will only consider two factors namely, Support and Confidence.

Business Analysis 40

Business Analysis IT Business Intelligence Sales 40

Domino Data Lab

AUGUST 22, 2019

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. Visualizing data using t-SNE. Natural Language Processing.] together at Stanford University. Example 11.9

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

OCTOBER 23, 2019

One of the things this data set doesn’t have, however, is a single metric to rank both offensive and defensive performance in a single statistic. A further diagnostic step is to plot the predicted values of the linear regression versus the actual values. R has yet one more way to visualize these relationships in multiple dimensions.

Statistics 59

Statistics Machine Learning Testing Modeling 59