Domino Data Lab

JULY 15, 2021

In the multiverse of data science, the tool options continue to expand and evolve. While there are certainly engineers and scientists who may be entrenched in one camp or another (the R camp vs. Python, for example, or SAS vs. MATLAB), there has been a growing trend towards dispersion of data science tools.

Data Science 145

Data Science Visualization Modeling Enterprise 145

CIO Business Intelligence

APRIL 25, 2022

An education in data science can help you land a job as a data analyst , data engineer , data architect , or data scientist. Here are the top 15 data science boot camps to help you launch a career in data science, according to reviews and data collected from Switchup.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

Testing 300

Testing Machine Learning Consulting Data Quality 300

Domino Data Lab

JULY 28, 2021

Data science is an exciting, interdisciplinary field that is revolutionizing the way companies approach every facet of their business. Data Science — A Venn Diagram of Skills. Data science encapsulates both old and new, traditional and cutting-edge. 3 Components of Data Science Skills.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

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 58

Machine Learning Data Science Statistics Deep Learning 58

Smart Data Collective

MARCH 13, 2020

It doesn’t matter what the project or desired outcome is, better data science workflows produce superior results. 5 Tips for Better Data Science Workflows. Data science is a complex field that requires experience, skill, patience, and systematic decision-making in order to be successful. Adding it All Up.

Data Science 100

Data Science Key Performance Indicator Knowledge Discovery Visualization 100

IBM Big Data Hub

AUGUST 11, 2023

These insights can help drive decisions in business, and advance the design and testing of applications. Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights.

Data Science 56

Data Science Machine Learning Cost-Benefit Deep Learning 56

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The way that I explained it to my data science students years ago was like this. The data scientists need to find the right data as inputs for their models — they also need a place to write-back the outputs of their models to the data repository for other users to access. That’s data democratization.

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

CIO Business Intelligence

JUNE 14, 2023

Organization: AWS Price: US$300 How to prepare: Amazon offers free exam guides, sample questions, practice tests, and digital training. CDP Data Analyst The Cloudera Data Platform (CDP) Data Analyst certification verifies the Cloudera skills and knowledge required for data analysts using CDP.

Big Data 125

Big Data Data Analytics Analytics Predictive Modeling 125

DataKitchen

JUNE 12, 2023

We had been talking about “Agile Analytic Operations,” “DevOps for Data Teams,” and “Lean Manufacturing For Data,” but the concept was hard to get across and communicate. I spent much time de-categorizing DataOps: we are not discussing ETL, Data Lake, or Data Science.

Testing 130

Testing Data Lake Dashboards Data Science 130

Analytics Vidhya

APRIL 14, 2022

This article was published as a part of the Data Science Blogathon. Though in this article we will not only test the frontal face but also different angles of the image and see where our model will perform […]. The post Face Detection Using the DLIB Face Detector Model appeared first on Analytics Vidhya.

Modeling 279

Modeling Testing Data Science Publishing 279

CIO Business Intelligence

OCTOBER 13, 2023

Data architect role Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles, often in support of data or digital transformations. Data architects are frequently part of a data science team and tasked with leading data system projects.

Data Architecture 127

Data Architecture Data Warehouse Statistics Visualization 127

DataKitchen

FEBRUARY 2, 2023

A combined, interoperable suite of tools for data team productivity, governance, and security for large and small data teams. Ultimately, there will be an interoperable toolset for running the data team , just like a more focused toolset (ELT/Data Science/BI) for acting upon data.

Metadata 130

Metadata Testing Risk Data Science 130

Cloudera

JANUARY 30, 2019

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. What is CDSW?

Data Science 72

Data Science Machine Learning Experimentation Cost-Benefit 72

DataKitchen

JANUARY 5, 2021

Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance. Each of the roles mentioned above views the world through a preferred set of tools: Data Center/IT – Servers, storage, software.

Testing 147

Testing Data Governance Metadata Visualization 147

CIO Business Intelligence

JUNE 7, 2022

Prescriptive analytics is a type of advanced analytics that involves the application of testing and other techniques to recommend specific solutions that will deliver desired outcomes. Data analytics and data science are closely related. Generally, the output of data analytics are reports and visualizations.

Data Analytics 128

Data Analytics Diagnostic Analytics Management Analytics 128

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. Data science. Session Summary.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Ontotext

NOVEMBER 25, 2021

As a highly popular language for scripting and data science, Python is often favored for development. Data virtualization with ONTOP. This GraphDB-native solution allows you to map relational data to RDF. However, perhaps you want to integrate relational data. It’s open source as well. Python RDFLib.

Visualization 98

Visualization Interactive Data-driven Enterprise 98

Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

Analytics 67

Analytics Data Quality Visualization Metadata 67

CIO Business Intelligence

SEPTEMBER 14, 2023

They’re often responsible for building algorithms for accessing raw data, too, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.

Analytics 132

Analytics Data Science Unstructured Data Data mining 132

DataKitchen

AUGUST 13, 2021

A drug company tests 50,000 molecules and spends a billion dollars or more to find a single safe and effective medicine that addresses a substantial market. DataOps automation provides a way to boost innovation and improve collaboration related to data in pharmaceutical research and development (R&D).

Testing 246

Testing Dashboards Marketing Measurement 246

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

Smarten

JULY 2, 2019

Self-Service Data Prep and Advanced Analytics for Business Users! Are your business users trying their best to leverage advanced analytics tools, only to find that the software solution requires them to be an expert in data science?

Visualization 51

Visualization IT Testing Interactive 51

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

Cloudera

JUNE 3, 2022

When it comes to the early stages in the data science process, data scientists often find themselves jumping between a wide range of tooling. First of all, there’s the question of what data is currently available within their organization, where it is, and how it can be accessed. Let’s see this in action.

Dashboards 88

Dashboards Visualization Machine Learning Data Science 88

Cloudera

JULY 9, 2018

Most organizations struggle to unlock data science in the enterprise. To that end, Cloudera offers the Data Science Workbench, a collaborative, scalable, and highly extensible platform for data exploration, analysis, modeling, and visualization. Why the Data Science Iron Triangle Matters.

Machine Learning 41

Machine Learning Data Science Testing Visualization 41

Smarten

DECEMBER 3, 2023

‘Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. What is self-service analytics? We should probably explain before we move on.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

The Data Visualisation Catalogue

JANUARY 30, 2019

While researching on the Buzz Surrounding VR Data Visualization , I found the most common claim being made was that VR allows for more “Natural” interactions with the data. So, just as you walk around your sofa or computer table, you can literally move around huge volumes of data. Pretty cool, uh?

Interactive 67

Interactive Visualization Big Data Technology 67

Cloudera

JUNE 26, 2023

Here are some tips and tricks of the trade to prevent well-intended yet inappropriate data engineering and data science activities from cluttering or crashing the cluster. For data engineering and data science teams, CDSW is highly effective as a comprehensive platform that trains, develops, and deploys machine learning models.

Testing 75

Testing Data Processing Visualization Data Science 75

AWS Big Data

AUGUST 2, 2023

This post was co-written by Sheel Saket, Senior Data Science Manager at FIS, and Rupesh Tiwari, Senior Architect at Amazon Web Services. You’ll learn how to implement a transformative solution that empowers your organization with near-real-time data indexing and visualization capabilities.

Visualization 83

Visualization Insurance Cost-Benefit Finance 83

CIO Business Intelligence

AUGUST 9, 2022

Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.

Analytics 122

Analytics Data Science Statistics Unstructured Data 122

Cloudera

NOVEMBER 17, 2021

It’s no secret that Data Scientists have a difficult job. It feels like a lifetime ago that everyone was talking about data science as the sexiest job of the 21st century. There’s recognition that it’s nearly impossible to find the unicorn data scientist that was the apple of every CEO’s eye in 2012.

Machine Learning 121

Machine Learning Visualization Data Science Metrics 121

datapine

OCTOBER 14, 2020

Visualizing the data and interacting on a single screen is no longer a luxury but a business necessity. Your Chance: Want to test interactive dashboard software for free? They enable you to easily visualize your data, filter on-demand, and slice and dice your data to dig deeper. Let’s get started.

Dashboards 235

Dashboards Interactive Reporting KPI 235

DataKitchen

OCTOBER 25, 2022

The goal of DataOps Observability is to provide visibility of every journey that data takes from source to customer value across every tool, environment, data store, data and analytic team, and customer so that problems are detected, localized and raised immediately. A data journey spans and tracks multiple pipelines.

Testing 130

Testing Data-driven Visualization Dashboards 130

DataKitchen

JANUARY 3, 2022

Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units. Business analysts sometimes perform data science, but usually, they integrate and visualize data and create reports and dashboards from data supplied by other groups.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

CIO Business Intelligence

MARCH 7, 2023

Align data science and data governance programs Remember when infosec was brought in at the end of the application development process and had little time and opportunity to address issues? Here are some force-multiplying differences achievable by agile data teams: Want that dashboard, then update the data catalog.

Digital Transformation 144

Digital Transformation Data Governance Experimentation Machine Learning 144

datapine

JANUARY 31, 2022

The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214