Analytics Vidhya

JANUARY 15, 2022

This article was published as a part of the Data Science Blogathon. Overview The core of the data science project is data & using it to build predictive models and everyone is excited and focused on building an ML model that would give us a near-perfect result mimicking the real-world business scenario.

Predictive Modeling 396

Predictive Modeling Data Science Publishing Modeling 396

Analytics Vidhya

DECEMBER 3, 2020

This article was published as a part of the Data Science Blogathon. Introduction The general principle of ensembling is to combine the predictions of various. The post Improve your Predictive Model’s Score using a Stacking Regressor appeared first on Analytics Vidhya.

Predictive Modeling 386

Predictive Modeling Data Science Publishing Modeling 386

Analytics Vidhya

AUGUST 1, 2022

This article was published as a part of the Data Science Blogathon. What is equally important here is the ability to communicate the data and insights from your predictive models through reports and dashboards. PowerBI is used for Business intelligence.

Reporting 389

Reporting Predictive Modeling Dashboards Business Intelligence 389

Analytics Vidhya

DECEMBER 4, 2022

This article was published as a part of the Data Science Blogathon. Source: Canva Introduction The real-world data can be very messy and skewed, which can mess up the effectiveness of the predictive model if it is not addressed correctly and in time.

Predictive Modeling 372

Predictive Modeling Data Science Publishing Modeling 372

Analytics Vidhya

MAY 13, 2022

This article was published as a part of the Data Science Blogathon. Introduction on AutoKeras Automated Machine Learning (AutoML) is a computerised way of determining the best combination of data preparation, model, and hyperparameters for a predictive modelling task.

Predictive Modeling 366

Predictive Modeling Machine Learning Data Science Publishing 366

Analytics Vidhya

MARCH 22, 2022

This article was published as a part of the Data Science Blogathon. Introduction Feature analysis is an important step in building any predictive model. It helps us in understanding the relationship between dependent and independent variables.

Predictive Modeling 332

Predictive Modeling Data Science Publishing Modeling 332

Analytics Vidhya

OCTOBER 12, 2020

This article was published as a part of the Data Science Blogathon. Introduction Machine learning is about building a predictive model using historical data. The post Quick Guide to Evaluation Metrics for Supervised and Unsupervised Machine Learning appeared first on Analytics Vidhya.

Machine Learning 306

Machine Learning Metrics Predictive Modeling Data Science 306

Analytics Vidhya

JULY 25, 2022

This article was published as a part of the Data Science Blogathon. Machine Learning is the method of teaching computer programs to do a specific task accurately (essentially a prediction) by training a predictive model using various statistical algorithms leveraging data.

Machine Learning 271

Machine Learning Predictive Modeling Statistics Data Science 271

Analytics Vidhya

DECEMBER 9, 2020

This article was published as a part of the Data Science Blogathon. Introduction While trying to make a better predictive model, we come across. The post Out-of-Bag (OOB) Score in the Random Forest Algorithm appeared first on Analytics Vidhya.

Predictive Modeling 261

Predictive Modeling Data Science Publishing Modeling 261

Analytics Vidhya

SEPTEMBER 4, 2021

This article was published as a part of the Data Science Blogathon Designing a deep learning model that will predict degradation rates at each base of an RNA molecule using the Eterna dataset comprising over 3000 RNA molecules.

Deep Learning 375

Deep Learning Modeling Data Science Publishing 375

Analytics Vidhya

MARCH 20, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction Some time back, I was making the predictive model. The post STANDARDIZED VS UNSTANDARDIZED REGRESSION COEFFICIENT appeared first on Analytics Vidhya.

Predictive Modeling 238

Predictive Modeling Data Science Publishing Modeling 238

Analytics Vidhya

SEPTEMBER 23, 2021

This article was published as a part of the Data Science Blogathon Overview of Electric Vehicle Sector The supply of fossil fuels is constantly decreasing. The post Data Analysis and Price Prediction of Electric Vehicles appeared first on Analytics Vidhya. The situation is very alarming.

Manufacturing 367

Manufacturing Data Science Publishing Analytics 367

Analytics Vidhya

MAY 22, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Deep Learning is a very powerful tool that has now. The post Pneumonia Prediction: A guide for your first CNN project appeared first on Analytics Vidhya.

Deep Learning 343

Deep Learning Data Science Publishing Analytics 343

Data Science and Beyond

JULY 22, 2018

I got my first data science job in 2012, the year Harvard Business Review announced data scientist to be the sexiest job of the 21st century. Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Session Summary. Transcript. Feel free to email me.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 272

Modeling Machine Learning Predictive Modeling Consulting 272

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. Over the life of the forecast, the data scientist will publish historical accuracy metrics. Our team does a lot of forecasting.

Forecasting 115

Forecasting Data Science Statistics Uncertainty 115

Smarten

NOVEMBER 23, 2023

Smarten is pleased to announce that its Smarten Augmented Analytics solution is included as a Representative Vendor in the Market Guide for Augmented Analytics Published October 2, 2023 (ID G00780764). The Smarten solution requires no data science skills, knowledge of statistical analysis or BI expertise.

Publishing 64

Publishing Marketing Analytics Predictive Modeling 64

Rocket-Powered Data Science

FEBRUARY 12, 2021

AAI’s recently published “Now and Next State of RPA” report presents detailed results of that survey. The AAI report covers these industries: energy/utilities, financial/insurance, government, healthcare, industrial/manufacturing, life sciences, retail/consumer, services/consulting, technology, telecom, and transportation/airlines.

ROI 234

ROI Digital Transformation Reporting Machine Learning 234

O'Reilly on Data

MAY 18, 2020

All predictive models are wrong at times?—just As the renowned statistician George Box once quipped , “All models are wrong, but some are useful.” Earlier this month, the FTC even published specific guidelines related to AI , hinting at enforcement actions to come. just hopefully less so than humans.

Risk 364

Risk Modeling Data Processing Reporting 364

O'Reilly on Data

JUNE 18, 2019

Since they consume a significant amount of time spent on most data science projects, we highlight these two main classes of data quality problems in this post: Data unification and integration. An important paradigm for solving both these problems is the concept of data programming.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

Domino Data Lab

JANUARY 11, 2021

Arming data science teams with the access and capabilities needed to establish a two-way flow of information is one critical challenge many organizations face when it comes to unlocking value from their modeling efforts. Domino Data Lab and Snowflake: Better Together. Writing data from Domino into Snowflake.

Data Science 103

Data Science Recreation/Entertainment Data Warehouse Publishing 103

Insight

AUGUST 5, 2020

Photo by Devon Divine on Unsplash Originally published in Maslo - Your Virtual Self. This created a summary features matrix of 7472 recordings x 176 summary features, which was used for training emotion label prediction models. To prevent data-leakage issues, actors in the training dataset did not reappear in the test datasets.

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

O'Reilly on Data

SEPTEMBER 19, 2023

While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” And it was good. For a few years, even. But then we hit another hurdle.

Machine Learning 335

Machine Learning Testing Modeling Cost-Benefit 335

Cloudera

NOVEMBER 18, 2020

We also celebrated the first-ever winner of the Data Impact Achievement Award — a new award category that recognizes one customer who has consistently achieved transformation across their business, pursuing a diverse set of use cases and creating a culture of data-driven innovation. . Data Impact Achievement Award.

Internet Publishing and Broadcasting 123

Internet Publishing and Broadcasting Data-driven Broadcasting Digital Transformation 123

CIO Business Intelligence

MARCH 19, 2025

Even in the absence of a formal C-level sustainability mandate, proactive data leadership can lay the foundation for future ESG integration, helping businesses stay ahead of regulatory and market expectations. Investing in data science and AI for sustainability Advanced analytics and AI can unlock new opportunities for sustainability.

IT 59

IT Data Governance Data-driven Metrics 59

Domino Data Lab

NOVEMBER 17, 2020

Our previous Domino Blog on the Curse of Dimensionality [2] , describes weird behaviors that emerge in data when P >> N: Points move far away from each other. Points fall on the outer edges of the data distribution. Predictive models fit to noise approach 100% accuracy.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

AWS Big Data

MARCH 7, 2023

However, data copy and duplication are allowed considering various consumption needs in terms of formats and latency. Data outbound Data is often consumed using structured queries for analytical needs. Also, datasets are accessed for ML, data exporting, and publishing needs.

Analytics 96

Analytics Data Warehouse Data Lake Metadata 96

Smarten

FEBRUARY 7, 2019

The Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics, published on October 31, 2018, includes the following strategic assumptions: By 2025, a scarcity of data scientists will no longer hinder the adoption of data science and machine learning in organizations.

Optimization 40

Optimization Analytics Predictive Modeling Data Science 40

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

IoT 22

IoT Big Data Data Warehouse Uncertainty 22

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. Are you interested in working on high-impact projects and transitioning to a career in data?

Risk 67

Risk ROI Modeling Metrics 67

Depict Data Studio

SEPTEMBER 17, 2019

Chris Lysy is a professional data designer and illustrator constantly exploring the overlap between contemporary design practice, digital communications, and data science. David Napoli has worked with data for over 20 years as an analyst, actuary, statistician, research manager, and director. Chris Lysy. Kylie Hutchinson.

Visualization 40

Visualization Consulting Dashboards Reporting 40

Insight

MARCH 18, 2019

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more. 5 is labeled as low.

Machine Learning 77

Machine Learning Predictive Modeling Modeling Metrics 77

Birst BI

MAY 29, 2018

Today, BI vendors are already introducing AI capabilities in their products for data preparation, data discovery, and data science. Data from multiple sources was normally stored in silos, and research was typically presented in a fragmented, disjointed report that was open to interpretation.

Slice and Dice 74

Slice and Dice Analytics Business Intelligence Machine Learning 74

AWS Big Data

DECEMBER 19, 2024

The prod-hema-data-catalog is the production-grade catalog that supports data sharing across production services and, in some cases, pre-production services. After the request is received, the core data platform team assesses the requirements and initiates the creation of projects and environments in Amazon DataZone.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52