Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. For one, Python remains the leading language for data science research.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). A reference to a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, Machine Learning, and real-time data. They cannot process language inputs generally.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

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

FineReport

JULY 18, 2022

The features you or your company need are core factors influencing your selection of the data analytics tool. For example, if you want the features of data visualization , such as stunning dashboards and rich charts, business intelligence tools are more suitable for you than a pure programming tool. Free Download. From FineBI.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

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

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. ML model interpretability and data visualization. – back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Rocket-Powered Data Science

SEPTEMBER 18, 2018

For the modern digital organization, the proof of any inference (that drives decisions) should be in the data! Rich and diverse data collections enable more accurate and trustworthy conclusions. Higher variety data means that we are adding data from other sensors, other signals, other sources, and of different types.

Big Data 103

Big Data Data Science Measurement Data Collection 103

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. Data Collection. Deployment. Regression.

Data mining 77

Data mining KDD Data Science Forecasting 77

Domino Data Lab

AUGUST 12, 2021

In this blog we will continue our discussion about data exploration with Pandas Profiler and include another tool that may come handy for those of us that may be more inclined to use visual clues, D-Tale. The purpose of Data Exploration. Data exploration is a very important step before jumping onto the machine learning wagon.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

IBM Big Data Hub

AUGUST 7, 2023

Therefore, IBM observes that more clients tend to consult AI leaders to help establish governance and enhance AI and data science capabilities, an operating model in the form of co-delivery partnerships. This results in many groups using a large gamut of AI-based tools that are not fully integrated into a cohesive system and platform.

Optimization 62

Optimization Forecasting Data-driven Strategy 62

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. Some people seemed to be naturally good at doing this kind of high quality data analysis. If you don’t have the right tools/visualization to look at your data, you need to work on those first.

Metrics 107

Metrics Measurement Statistics Data Collection 107

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

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. Eighty percent of this problem is collecting the data and then transforming the data. The other 20 percent is ML- and data science–related tasks like finding the right model, doing EDA, and feature engineering.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Instead, I built the AIgent.

Modeling 59

Modeling Metadata Publishing Sales 59

CIO Business Intelligence

JANUARY 20, 2023

But the benefits of BI extend beyond business decision-making, according to data visualization vendor Tableau , including the following: Data-driven business decisions: The ability to drive business decisions with data is the central benefit of BI.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

Domino Data Lab

AUGUST 20, 2019

Real-world datasets can be missing values due to the difficulty of collecting complete datasets and because of errors in the data collection process. Recentering the data means that we translate the values so that the extremes are different and the intermediate values are moved in some consistent way. Discretization.

Testing 68

Testing Modeling Interactive Measurement 68

datapine

APRIL 11, 2019

In the contemporary world of business, the age-old art of storytelling is far from forgotten: rather than speeches on the Senate floor, businesses rely on striking data visualizations to convey information, drive engagement, and persuade audiences. . Download our 10-step checklist and see how to tell the best data story.

Dashboards 169

Dashboards Visualization Data-driven Key Performance Indicator 169

datapine

AUGUST 29, 2022

Best for : the new intern who has no idea what data science even means. An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

JUNE 23, 2019

All you need to know, for now, is that machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn based on data by being trained on past examples. They have the foundations of data infrastructure.

Management 110

Management Machine Learning Experimentation Metrics 110

CIO Business Intelligence

SEPTEMBER 22, 2023

What are the benefits of data management platforms? Modern, data-driven marketing teams must navigate a web of connected data sources and formats. Others aim simply to manage the collection and integration of data, leaving the analysis and presentation work to other tools that specialize in data science and statistics.

Management 105

Management Advertising Data Lake Sales 105

CIO Business Intelligence

JUNE 9, 2022

In these instances, data feeds come largely from various advertising channels, and the reports they generate are designed to help marketers spend wisely. Others aim simply to manage the collection and integration of data, leaving the analysis and presentation work to other tools that specialize in data science and statistics.

Management 144

Management Advertising Data Lake Sales 144

datapine

APRIL 16, 2019

As a direct result, less IT support is required to produce reports, trends, visualizations, and insights that facilitate the data decision making process. From these developments, data science was born (or at least, it evolved in a huge way) – a discipline where hacking skills and statistics meet niche expertise.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. For example, streaming data from sensors to an analytics platform where it is processed and visualized immediately.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52