O'Reilly on Data

NOVEMBER 13, 2018

Data Platforms. Over the last 12-18 months, companies that use a lot of ML and employ teams of data scientists have been describing their internal data science platforms (see, for example, Uber , Netflix , Twitter , and Facebook ). Model lifecycle management. Real modeling begins once in production. Privacy and security.

Machine Learning 277

Machine Learning Risk Management Statistics 277

KDnuggets

APRIL 19, 2022

Statistical performance metrics aren’t enough to pick the right models to bring to market.

Data Science 108

Data Science Modeling Statistics Metrics 108

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. Our team does a lot of forecasting.

Forecasting 115

Forecasting Data Science Statistics Uncertainty 115

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. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Analytics Vidhya

DECEMBER 16, 2020

This article was published as a part of the Data Science Blogathon. Introduction There are so many performance evaluation measures when it comes to. The post Decluttering the performance measures of classification models appeared first on Analytics Vidhya.

Measurement 306

Measurement Modeling Data Science Publishing 306

DataRobot Blog

SEPTEMBER 1, 2022

These and many other questions are now on top of the agenda of every data science team. This also shows how the models compare on standard performance metrics and informative visualizations like Dual Lift. Here the DataRobot view shows that the Challenger beats the Champion on some metrics, but not all.

Metrics 145

Metrics Statistics Modeling Data Science 145

Rocket-Powered Data Science

JULY 19, 2023

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

O'Reilly on Data

DECEMBER 12, 2019

Many non-technological solutions involve promoting a diversity of expertise and experience on data science teams, and ensuring diverse intellects are involved in all stages of model building. [15] For model training and selection, we recommend considering fairness metrics when selecting hyperparameters and decision cutoff thresholds.

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

datapine

MARCH 31, 2022

According to the US Bureau of Labor Statistics, demand for qualified business intelligence analysts and managers is expected to soar to 14% by 2026, with the overall need for data professionals to climb to 28% by the same year. A background in (or a firm grasp of) data warehousing and mining.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

datapine

APRIL 16, 2019

While sometimes it’s okay to follow your instincts, the vast majority of your business-based decisions should be backed by metrics, facts, or figures related to your aims, goals, or initiatives that can ensure a stable backbone to your management reports and business operations. Quantitative data analysis focuses on numbers and statistics.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

Analytics Vidhya

NOVEMBER 9, 2020

This article was published as a part of the Data Science Blogathon. Overview Challenges if we use the Linear Regression model to solve a. The post Binary Cross Entropy aka Log Loss-The cost function used in Logistic Regression appeared first on Analytics Vidhya.

Data Science 241

Data Science Publishing Modeling Analytics 241

CIO Business Intelligence

MARCH 21, 2022

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist salary. Data scientist skills.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Data Science 137

DataRobot Blog

JULY 28, 2022

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. Sensor Data Analysis Examples. The Best Way to Achieve Both Accuracy and Cost Control.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

DataKitchen

MAY 10, 2021

And the worst part – data errors take the fun out of data science. Remember your first data science courses? You probably imagined your career would be about helping drive insights with data instead of having to sit in endless meetings discussing analytics errors and painstaking corrective actions.

Testing 214

Testing Manufacturing Data Quality Statistics 214

Smart Data Collective

NOVEMBER 26, 2021

With the “big data” or insurmountable, high-volume amount of information, data analytics plays a crucial role in many business aspects, including revenue marketing. Data analytics refers to the systematic computational analysis of statistics or data. It lays a core foundation necessary for business planning.

Marketing 131

Marketing Data Analytics Key Performance Indicator Analytics 131

datapine

NOVEMBER 27, 2019

The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. Prescriptive analytics goes a step further into the future.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Smart Data Collective

JUNE 16, 2022

Data science is both a rewarding and challenging profession. One study found that 44% of companies that hire data scientists say the departments are seriously understaffed. Fortunately, data scientists can make due with fewer staff if they use their resources more efficiently, which involves leveraging the right tools.

Online Analytical Processing 139

Online Analytical Processing OLAP Analytics Data Warehouse 139

datapine

DECEMBER 4, 2019

Applied to business, it is used to analyze current and historical data in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company. The accuracy of the predictions depends on the data used to create the model.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. On the flip side, you sometimes have a small volume of data.

Metrics 107

Metrics Measurement Statistics Data Collection 107

CIO Business Intelligence

MAY 27, 2024

The path to securing the boardroom’s buy-in is more complex than simply having the right statistics and studies on paper,” says Dara Warn, the CEO of INE Security , a global cybersecurity training and certification provider. “To Leverage Data and Statistics Presenting data from reputable sources can lend credibility to the argument.

Cost-Benefit 116

Cost-Benefit Statistics ROI Measurement 116

Sisense

APRIL 10, 2020

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R or Python?”.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. Data Science Techniques.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

O'Reilly on Data

OCTOBER 11, 2022

Over the past decade, one problem with data science and its successors has been the assumption that all you need is data, and lots of it; analyzing that data will lead you to new products, new processes, new strategies: just follow the data and let it transform your business. Is retraining needed?

Data Integration 353

Data Integration Insurance Dashboards Data-driven 353

Sisense

SEPTEMBER 3, 2020

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

Statistics 97

Statistics Metrics Visualization Finance 97

CIO Business Intelligence

JANUARY 20, 2023

The potential use cases for BI extend beyond the typical business performance metrics of improved sales and reduced costs. BI analysts use data analytics, data visualization, and data modeling techniques and technologies to identify trends.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. 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.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

Data Science 101

OCTOBER 31, 2019

Therefore, learning some useful data mining procedures may prove beneficial in this regard. As taught in Data Science Dojo’s data science bootcamp , you will have improved prediction and forecasting with respect to your product. The choice of these metrics depends on the nature of the problem. Regression.

Data mining 77

Data mining KDD Data Science Forecasting 77

The Unofficial Google Data Science Blog

AUGUST 27, 2019

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The missing verdicts create two problems.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Cloudera

SEPTEMBER 17, 2020

For enterprise organizations, managing and operationalizing increasingly complex data across the business has presented a significant challenge for staying competitive in analytic and data science driven markets. For further analysis, stage level summary statistics show the number of parallel tasks and I/O distribution.

Visualization 104

Visualization Statistics Metrics Optimization 104

IBM Big Data Hub

NOVEMBER 29, 2023

For instance, if a business prioritizes accuracy in generating synthetic data, the resulting output may inadvertently include too many personally identifiable attributes, thereby increasing the company’s privacy risk exposure unknowingly.

Metrics 80

Metrics Machine Learning Statistics Risk 80

Smart Data Collective

OCTOBER 14, 2019

They can use predictive, descriptive and prescriptive analytics to help CSCOs turn metrics into insights for better decision-making. Statistics, qualitative analysis and quant are some of the backbones of big data. These can help a developer find a career in the data science field. Apache Spark. Other coursework.

Big Data 99

Big Data Software Data mining Prescriptive Analytics 99

Domino Data Lab

OCTOBER 9, 2019

Clustering is a machine learning technique that enables researchers and data scientists to partition and segment data. Segmenting data into appropriate groups is a core task when conducting exploratory analysis. It divides the observations into discrete groups based on some distance metric. > wineUrl <- '[link].

Statistics 100

Statistics Machine Learning Metrics Measurement 100

Birst BI

AUGUST 23, 2018

Data scientists building AI applications require numerous skills – data visualization, data cleansing, artificial intelligence algorithm selection and diagnostics. What if some of these data science tasks could be automated using AI, increasing data science productivity to tackle more AI use cases?

Machine Learning 73

Machine Learning Data Science Testing Metrics 73