CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance.

Data Analytics 129

Data Analytics Diagnostic Analytics Management Analytics 129

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 122

Predictive Analytics Analytics Predictive Modeling Data mining 122

datapine

AUGUST 14, 2019

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. It helps to automate and makes the usage of the R programming statistical language easier and much more effective. perfect for statistical computing and design.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Smarten

MAY 29, 2023

World-renowned technology analysis firm Gartner defines the role this way, ‘A citizen data scientist is a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics. ‘If Automatic generation of models.

Prescriptive Analytics 52

Prescriptive Analytics Predictive Modeling Digital Transformation Business Objectives 52

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. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

CIO Business Intelligence

OCTOBER 13, 2023

In the next six to 12 months, some of the most popular anticipated uses for gen AI include content creation (42%), data analytics (53%), software development (41%), business insight (51%), internal customer support (45%), product development (40%), security (42%), and process automation (51%).

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

CIO Business Intelligence

APRIL 22, 2022

Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For example, data analysts should be on board to investigate the data before presenting it to the team and to maintain data models. What is data science?

Data Science 138

Data Science Statistics Machine Learning Visualization 138

Smarten

APRIL 25, 2024

Artificial Intelligence (AI) and Machine Learning (ML) elements support Citizen Data Scientists and help users prepare data, achieve automated data insights and create, share and use predictive models. Users can harness the power of statistics and machine learning to uncover hidden insights and improve the overall quality of your data.

Data Quality 62

Data Quality Analytics Machine Learning Visualization 62

Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal. When there are many variables the Curse of Dimensionality changes the behavior of data and standard statistical methods give the wrong answers. Data Has Properties.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

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

Smarten

JUNE 17, 2022

Smarten announces the recent certification of its Smarten Augmented Analytics Software product by CERT-IN. Smarten CEO, Kartik Patel says, “The CERT-IN certification for our Smarten suite of software products is an important credential.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

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

Jedox

MAY 17, 2022

Financial planners , Chief Financial Officers, and analysts have all struggled to build accurate methods for predicting what’s likely to happen. Prior to the dawn of advanced statistical analysis and machine learning, predictive analytics efforts fell into 4 broad categories: Guessing , which is the default that most people revert to.

Predictive Analytics 83

Predictive Analytics Finance Forecasting Analytics 83

Smart Data Collective

MAY 23, 2021

Rodrigo Liang is CEO of SambaNova, which provides both hardware and software to businesses for the purpose of analyzing data. While this can be classed as data science, one difference is that data science tends to use a predictive model to make its analysis, while AI can be capable of analyzing based on learned knowledge and facts.

Data Science 99

Data Science Cost-Benefit Statistics Predictive Modeling 99

CIO Business Intelligence

JULY 28, 2022

Data scientists have extensive academic backgrounds — often in computer science, statistics, and mathematics. They specialize in building powerful algorithms, and analyzing, processing, and modeling data so they can then interpret the results to create actionable plans. These people are making up a data science support system.

Data Science 143

Data Science IT Machine Learning Data-driven 143

Smarten

DECEMBER 17, 2018

Tools like Assisted Predictive Modeling allow the average business user to become a Citizen Data Scientist with tools that offer guidance and auto-suggestions to help the user arrive at the outcome they need without being frustrated or having to call in an army of analysts and IT staff to help them complete their analysis.

Predictive Analytics 40

Predictive Analytics Predictive Modeling Analytics Statistics 40

FineReport

MAY 29, 2020

Excel Vs. BI Reporting Tools, and BI Software in Reporting. Even now, Excel is still the most popular software that companies use it to make a spreadsheet and implement reporting. . BI Software VS BI Reporting Tools. Most BI software has reporting capabilities. The key to BI software is ‘data+business understanding.’ .

Reporting 94

Reporting OLAP Visualization Software 94

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

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

The Data Visualisation Catalogue

APRIL 4, 2019

I believe DataView VR is the next step in the evolution of analysis and presentation software. I’ve implemented DataView in my own work and find it an excellent way to organize investment information, do data discovery and create predictive models. The user can actually see the output of the model in multiple-dimensions.

Dashboards 73

Dashboards Predictive Modeling Visualization Modeling 73

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Smarten

OCTOBER 25, 2017

My team uses augmented data preparation and self-serve data prep, and tools like smart data visualization, plug n’ play predictive analysis and assisted predictive modeling to connect all the dots, find those all-important exceptions, and identify patterns and trends so they can get ahead of the game and help us in the market.

Predictive Modeling 63

Predictive Modeling Statistics Visualization Business Analytics 63

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. Actual Predicted 23.1

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Birst BI

AUGUST 23, 2018

For example, there are a plethora of software tools available to automatically develop predictive models from relational data, and according to Gartner, “By 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.” [1]

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

Smarten

APRIL 19, 2024

Gartner recently estimated that the market for AI software will be nearly $134.8 OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). GPT – Is based on a Large Language Model (LLM). billion, with the market growing by 31.1% in next several years.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Cloudera

DECEMBER 20, 2017

Analytic software and statistical tools help us build models to detect patterns and make predictions to extend our reach. Increasingly, there is enough data and complexity, that we can’t define a model capturing each input and output scenario up front. That’s where machine learning (ML) comes in.

Machine Learning 40

Machine Learning Modeling Reporting Statistics 40

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. Try our professional BI and analytics software for 14 days free!

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Smarten

NOVEMBER 19, 2018

What is Predictive Analytics? Predictive analytics is the process of forecasting or predicting business results for planning purposes.

Business Objectives 40

Business Objectives Predictive Analytics Analytics Forecasting 40

Domino Data Lab

JANUARY 21, 2021

Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task. Each dataset has properties that warrant producing specific statistics or charts.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

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. But it wasn’t clear to me how to market myself – my LinkedIn title at the time was “software engineer with a research background” , which is a bit of a mouthful.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

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. This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. Gathering this data represents a nontrivial software engineering problem. Phrasing the Problem.

Statistics 59

Statistics Machine Learning Testing Modeling 59

BI-Survey

JANUARY 29, 2023

In the context of corporate planning, predictive planning and forecasting, it is therefore a major trend to use predictive models based on statistical methods and ML for forecasting and thorough analysis. Managers need to approve and commit resources, but also understand the benefits and limitations of predictive models.

Forecasting 52

Forecasting Predictive Modeling Measurement Finance 52

BI-Survey

MARCH 16, 2021

Companies consider the improvement of the software they use (56 percent) to be the most important investment for optimizing planning and forecasting. Without sound software support, this high frequency cannot be handled efficiently and quickly enough to derive insights for decision-makers.

Forecasting 52

Forecasting Management Uncertainty Predictive Modeling 52

BI-Survey

MARCH 16, 2021

Companies consider the improvement of the software they use (56 percent) to be the most important investment for optimizing planning and forecasting. Without sound software support, this high frequency cannot be handled efficiently and quickly enough to derive insights for decision-makers.

Forecasting 52

Forecasting Management Uncertainty Predictive Modeling 52