Smarten

JANUARY 30, 2024

The right self-serve data prep solution can provide easy-to-use yet sophisticated data prep tools that are suitable for your business users, and enable data preparation techniques like: Connect and Mash Up Auto Suggesting Relationships JOINS and Types Sampling and Outliers Exploration, Cleaning, Shaping Reducing and Combining Data Insights (Data Quality (..)

Analytics 67

Analytics Data Quality Visualization Metadata 67

IBM Big Data Hub

SEPTEMBER 19, 2023

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets.

Data Science 86

Data Science Data Analytics Prescriptive Analytics Analytics 86

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 Recreation/Entertainment Predictive Analytics 110

Cloudera

MAY 22, 2023

Report Standardization Take these steps: Inventory reports, including ownership, usage statistics, and report frequency. If your visualization tool employs extract jobs, stop them, and note any database archival targets. Target temporary/testing databases and user databases owned by former FTE. Repeat the exercise quarterly.

Statistics 73

Statistics Data-driven Reporting Dashboards 73

O'Reilly on Data

MARCH 26, 2024

This seems to be emerging as a feature, not a bug, and hopefully it’s obvious to you why they called their IEEE opinion piece Generative AI Has a Visual Plagiarism Problem. This essay first appeared on Hugo Bowne-Anderson’s blog. Also, training models doesn’t seem to me to infringe copyright because it doesn’t yet produce output!

Modeling 275

Modeling Machine Learning Risk Advertising 275

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. appeared first on IBM Blog.

Machine Learning 61

Machine Learning Data Science Statistics Deep Learning 61

IBM Big Data Hub

JANUARY 25, 2024

Over time, inventory managers have tested different approaches to determine the best fit for their organizations. Consider these questions: Do you have a platform that combines statistical analyses, prescriptive analytics and optimization algorithms? Can you conduct what-if scenarios to visualize your options? Results may vary.

Optimization 83

Optimization Cost-Benefit Prescriptive Analytics Manufacturing 83

Octopai

MAY 2, 2022

Your dashboards, charts, visualizations… they’re all products. . Common elements of DataOps strategies include: Collaboration between data managers, developers and consumers A development environment conducive to experimentation Rapid deployment and iteration Automated testing Very low error rates. Issue detected?

Manufacturing 87

Manufacturing Testing Experimentation Data Quality 87

datapine

MARCH 19, 2019

According to the statistics portal Statista , there are currently around 4.78 Here, we will consider what a mobile dashboard is, the dashboard mobile design, making visualizations on mobile, and real business examples to explain the benefits this most flexible of data-driven technology can offer your business.

Dashboards 112

Dashboards Visualization Metrics KPI 112

datapine

JULY 17, 2019

Typically presented in the form of an interactive dashboard , this kind of report provides a visual representation of the data associated with your predetermined set of key performance indicators – or KPI data, for short. Set up a report which you can visualize with an online dashboard. 5) Drill down into data visualization.

KPI 223

KPI Reporting Key Performance Indicator Dashboards 223

Depict Data Studio

JUNE 2, 2020

One of the primary reasons for taking Ann’s data visualization course, Great Graphs , was to learn better ways to use Microsoft Excel as a visualization tool. Here’s how my own work and my students’ work evolved after taking the data visualization course. Training University Students on Data Visualization.

Visualization 52

Visualization Statistics Reporting Finance 52

Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. All descriptive statistics can be calculated using quantitative data. Digging into quantitative data. This is quantitative data.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

Depict Data Studio

AUGUST 28, 2023

Numbers are test scores, ages, number of people, amount of money donated, etc. For numbers , we need to tabulate them using descriptive statistics , which often aren’t possible with pivot tables. Here’s an older blog post that shows you how to get started with pivot tables within minutes. Pivot tables don’t.)

Statistics 52

Statistics Dashboards Interactive Measurement 52

Domino Data Lab

JULY 10, 2019

This blog post considers Ludwig, offering a brief overview of the package and providing tips for practitioners such as when to use Ludwig’s command-line syntax and when to use its Python API. This blog also provides code examples with a Jupyter notebook that you can download or run via hosting provided by Domino. ludwig experiment.

Deep Learning 110

Deep Learning Visualization Recreation/Entertainment Data Processing 110

The Unofficial Google Data Science Blog

JANUARY 16, 2018

We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. Figure 4 is a visual representation of these steps.

Testing 58

Testing Experimentation Measurement Modeling 58

Smarten

JUNE 17, 2022

After completion of the testing procedure, the certificate is provided to show that all requirements were met. The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

Smarten

APRIL 12, 2023

About Smarten The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

BizAcuity

MAY 16, 2022

His name was William Gosset and he is credited to have developed the student t-test. He went on to be the head brewer of Guinness and we thank him for not just great hand-crafted beers but subsequent research breakthroughs in statistical research as well. Visual Analytics and Data Visualization. That was in the 1900’s.

Business Intelligence 52

Business Intelligence Data mining Visualization Data Lake 52

datapine

SEPTEMBER 29, 2022

As quality issues are often highlighted with the use of dashboard software , the change manager plays an important role in the visualization of data quality. It involves: Reviewing data in detail Comparing and contrasting the data to its own metadata Running statistical models Data quality reports. 2 – Data profiling.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Cloudera

JUNE 28, 2022

In a previous blog of this series, Turning Streams Into Data Products , we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. This blog will be published in two parts. This is what we call the first-mile problem.

Dashboards 83

Dashboards Machine Learning Statistics KPI 83

Alation

AUGUST 3, 2021

DataOps was created to leverage the underlying manufacturing methodologies of lean manufacturing, statistical process control, and, of course, agile development. DataOps then works to continuously improve and adjust data models, visualizations, reports, and dashboards to achieve business goals. Test , to ensure quality.

Testing 59

Testing Manufacturing Software Statistics 59

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

Metrics 52

Metrics Statistics Visualization Metadata 52

CIO Business Intelligence

OCTOBER 13, 2023

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. The role of algorithm engineer requires knowledge of programming languages, testing and debugging, documentation, and of course algorithm design.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Cloudera

FEBRUARY 8, 2021

Continuing from my previous blog post about how awesome and easy it is to develop web-based applications backed by Cloudera Operational Database (COD), I started a small project to integrate COD with another CDP cloud experience, Cloudera Machine Learning (CML). . Now, let’s start testing our model! Build the application.

Machine Learning 73

Machine Learning Statistics Modeling Dashboards 73

Smarten

MAY 31, 2022

Hypothesis Testing. Descriptive Statistics. Analyze the Model with Visualization and Interpretation. Trends and Patterns. Forecasting. Classification. Correlation. Regression. Clustering. Frequent Pattern Mining. Access to Flexible, Intuitive Predictive Modeling. Select an Algorithm Technique. Select the Dataset or Cube.

Predictive Analytics 67

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 67

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

Risk 52

Risk Modeling Metrics Business Objectives 52

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. The doctor uploads the patient’s medical history and recent test results to an AGI-powered medical analysis system. Example : A patient visits a doctor with concerning symptoms.

Cost-Benefit 97

Cost-Benefit Modeling Manufacturing Interactive 97

DataRobot

APRIL 20, 2021

This is the first in a series of three blog posts that shares our perspective on AI you can trust. This includes the basics: Computing summary statistics on each feature Measuring associations between features Observing feature distributions and their correlation with the predictive target Identifying outliers.

Measurement 52

Measurement Visualization Advertising Metrics 52

Occam's Razor

AUGUST 22, 2011

Here you'll find all my blog posts categorized into a structure that will hopefully make it easy for you to discover new content, find answers to your questions, or simply wallow in some excellent analytics narratives. Blogging Experience Articles. + Podcast: Measuring Rich Media (Ajax, Flash / Flex, RSS & Blogs).

KPI 124

KPI Metrics Measurement ROI 124

BizAcuity

APRIL 1, 2023

Exploratory Data Analysis (EDA) EDA is used to analyze data and summarize their main properties and characteristics using visual techniques. Predictive Analytics It is a subset of business analytics that uses statistical techniques (algorithms) to find patterns in historical data points and predict future outcomes with high accuracy.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

IBM Big Data Hub

AUGUST 11, 2023

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. These insights can help drive decisions in business, and advance the design and testing of applications. MLOps and IBM Watsonx.ai

Data Science 60

Data Science Machine Learning Cost-Benefit Deep Learning 60

datapine

MARCH 22, 2019

Determining accurate CES, NPS, and CSAT is easier when you are using an interactive, real-time dashboard that’s capable of providing elevated visualizations coupled with concise textual details. You can use different scales to measure the experience or emojis (visual representation of satisfactory levels).

Metrics 134

Metrics Dashboards Measurement Interactive 134

BizAcuity

JANUARY 18, 2023

EDA is used to analyze data and summarize their main properties and characteristics using visual techniques. 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.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

datapine

FEBRUARY 9, 2024

4) How to Select Your KPIs 5) Avoid These KPI Mistakes 6) How To Choose A KPI Management Solution 7) KPI Management Examples Fact: 100% of statistics strategically placed at the top of blog posts are a direct result of people studying the dynamics of Key Performance Indicators, or KPIs. What Are KPI Best Practices?

KPI 221

KPI Management Key Performance Indicator Measurement 221

Occam's Razor

OCTOBER 31, 2011

Creating graphs is easy, and I could fill five blog posts with all the nonsense one can accomplish by playing with the axes. In this case for my data it is not statistically significant (more on that later in this post), but there is no way you would know that (or not know that) just from the data in front of you. Look at it carefully?

Statistics 130

Statistics Testing Reporting Key Performance Indicator 130

datapine

NOVEMBER 6, 2023

That is because visuals make it easier to convey and understand critical information, breaching the knowledge gap between audiences across industries. That being said, for data visualizations to be successful and avoid misleading your audience, it is necessary to understand the different use cases for each visual.

Visualization 186

Visualization Cost-Benefit KPI Sales 186

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Data science is an interdisciplinary field within STEM that combines math and statistics, computer science, and business acumen to find patterns in complex datasets that can be used to improve decision making. What are Data Scientists?

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45