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. Some common tools include: SAS” This proprietary statistical tool is used for data mining, statistical analysis, business intelligence, clinical trial analysis, and time-series analysis.

Data Science 139

Data Science Statistics Machine Learning Visualization 139

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 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

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

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. On-site courses are available in Munich. Remote courses are also available. Switchup rating: 5.0 (out

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

Smarten

MAY 31, 2022

The technology research firm, Gartner has predicted that, ‘predictive and prescriptive analytics will attract 40% of net new enterprise investment in the overall business intelligence and analytics market.’ Descriptive Statistics. Access to Flexible, Intuitive Predictive Modeling. Trends and Patterns. Forecasting.

Predictive Analytics 67

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 67

Smarten

APRIL 12, 2023

Smarten Augmented Analytics tools include Assisted Predictive Modeling , Smart Data Visualization , Self-Serve Data Preparation , Sentiment Analysis , and Clickless Analytics with natural language processing (NLP) for search analytics.

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

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

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

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

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

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 60

Machine Learning Data Science Statistics Deep Learning 60

Smarten

JUNE 17, 2022

Smarten Augmented Analytics tools include Assisted Predictive Modeling , Smart Data Visualization , Self-Serve Data Preparation , Sentiment Analysis , and Clickless Analytics with natural language processing (NLP) for search analytics.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

Smarten

NOVEMBER 8, 2022

Smarten Augmented Analytics tools include Assisted Predictive Modeling , Smart Data Visualization , Self-Serve Data Preparation , Sentiment Analysis , and Clickless Analytics with natural language processing (NLP) for search analytics.

Business Intelligence 52

Business Intelligence Predictive Modeling Statistics Data-driven 52

Data Science 101

JUNE 10, 2019

They can clean large amounts of data, explore data sets to find trends, build predictive models, and create a story around their findings. Data analysts sift through data and provide helpful reports and visualizations. A data scientist can run a project from end-to-end. Data Analysts. Step 4: Explore the data.

Data Science 96

Data Science Statistics Predictive Modeling Machine Learning 96

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 Software Visualization 94

Smarten

OCTOBER 25, 2017

Advanced Data Discovery allows business users to quickly and easily prepare and analyze data and visualize and explore data. And, the best part is that a business user with average skills can do all of this without specialized skills, knowledge of statistical analysis or support from IT or professional data scientists.

Predictive Modeling 63

Predictive Modeling Statistics Visualization Business Analytics 63

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. 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.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

The Data Visualisation Catalogue

APRIL 4, 2019

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. Application #2: Creating and visualizing multi-variable relationships, which is particularly useful in creating predictive models.

Dashboards 74

Dashboards Predictive Modeling Visualization Modeling 74

DataRobot

JANUARY 13, 2022

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. It offers a visual and intuitive UI that enables anyone to explore and prepare data for machine learning, no matter their previous machine-learning experience.

ROI 52

ROI Machine Learning Experimentation Data Science 52

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 81

Predictive Analytics Finance Forecasting Analytics 81

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. This article focuses on the Independent Samples T Test technique of Hypothesis testing. About Smarten.

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

Correlation is a statistical measure that indicates the extent to which two variables fluctuate together. 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.

Enterprise 40

Enterprise IT Statistics Business Intelligence 40

Smarten

JUNE 29, 2018

Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (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.

Enterprise 40

Enterprise Sales Statistics Business Intelligence 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.

Business Analysis 40

Business Analysis IT Sales Statistics 40

Smarten

JUNE 26, 2018

It is used to determine whether there is a statistically significant association between the two categorical variables. At 95% confidence level (5% chance of error) – As p-value = 0.041 which is less than 0.05, there is a statistically significant association between gender and product category purchased.

Testing 40

Testing Statistics IT Business Intelligence 40

Smarten

JUNE 29, 2018

At 95% confidence level (5% chance of error): As p-value = 0.041 which is less than 0.05, there is a statistically significant difference between means of pre and post sample values. Therefore, the treatment was effective. Here the dependent variable would be ‘cycle time values’. Here the dependent variable would be ‘Daily sales’.

Business Analysis 40

Business Analysis Testing Statistics Advertising 40

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

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. 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. Statistics and programming go hand in hand.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

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 29, 2018

Correlation is a statistical measure that indicates the extent to which two variables fluctuate together A positive correlation indicates the extent to which those variables increase or decrease in parallel. This article describes the Spearman’s Rank Correlation and how it is used for enterprise analysis.

Business Analysis 40

Business Analysis IT Measurement Statistics 40

Smarten

JUNE 20, 2018

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. A stationary time series is one with statistical properties such as mean, where variances are all constant over time. Stationary/Stationarity.

Forecasting 40

Forecasting Statistics Business Intelligence Predictive Modeling 40

Ms SQL Girl

MAY 19, 2016

To simplify, “In-database Advanced Analytics”: you can run powerful statistical / predictive modelling (from R) inside SQL Server. R Tools for Visual Studio : tools for developing R script in Visual Studio. R Tools for Visual Studio : tools for developing R script in Visual Studio.

Visualization 45

Visualization Predictive Modeling Statistics Reporting 45

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 29, 2018

Improved statistical precision is achieved through this method due to the low variability within each subgroup, and the fact that a smaller sample size is required for this method as compared to simple random sampling. A random sample from each of these subgroups is taken in proportion to the subgroup size relative to the population size.

Analytics 40

Analytics Statistics Business Intelligence Predictive Modeling 40

Ms SQL Girl

MAY 19, 2016

To simplify, “In-database Advanced Analytics”: you can run powerful statistical / predictive modelling (from R) inside SQL Server. R Tools for Visual Studio : tools for developing R script in Visual Studio. R Tools for Visual Studio : tools for developing R script in Visual Studio.

Visualization 40

Visualization Predictive Modeling Statistics Reporting 40