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? What is machine learning?

Machine Learning 57

Machine Learning Data Science Statistics Deep Learning 57

datapine

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

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.

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Data Science Statistics Recreation/Entertainment Predictive Analytics 110

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 166

Analytics Machine Learning Deep Learning Data Science 166

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Data Science and Beyond

SEPTEMBER 19, 2016

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. on KDNuggets).

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

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.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

CIO Business Intelligence

OCTOBER 13, 2023

Data scientist As companies embrace gen AI, they need data scientists to help drive better insights from customer and business data using analytics and AI. For most companies, AI systems rely on large datasets, which require the expertise of data scientists to navigate.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

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.

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Data Science Machine Learning Advertising Modeling 112

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 120

Predictive Analytics Analytics Predictive Modeling Data mining 120

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

CIO Business Intelligence

JUNE 7, 2022

Predictive analytics applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes. Data analysts and others who work with analytics use a range of tools to aid them in their roles.

Data Analytics 128

Data Analytics Diagnostic Analytics Management Analytics 128

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning. References.

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Modeling Deep Learning Machine Learning Knowledge Discovery 139

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.

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Machine Learning Data Quality Statistics Modeling 259

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 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs.

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

O'Reilly on Data

DECEMBER 12, 2019

More structured approaches to sensitivity analysis include: Adversarial example searches : this entails systematically searching for rows of data that evoke strange or striking responses from an ML model. Figure 1 illustrates an example adversarial search for an example credit default ML model.

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Machine Learning Modeling Testing Risk Management 190

IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Domino Data Lab

OCTOBER 7, 2020

There are four properties of high dimensional data: Points move far away from each other in high dimensions. The accuracy of any predictive model approaches 100%. Property 4: The accuracy of any predictive model approaches 100%. There should be no model to accurately predict even and odd rows with random data.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. We will go through a typical ML pipeline, where we do data ingestion, exploratory data analysis, feature engineering, model training and evaluation. Knowledge and Data Engineering, IEEE Transactions on, 21, 1263-1284. References. [1] Pavía, Ernesto J.

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Statistics Machine Learning Modeling Metrics 69

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. It seems entirely possible to do the same with customer or transactional data.

Modeling 221

Modeling Machine Learning Predictive Modeling Consulting 221

IBM Big Data Hub

DECEMBER 20, 2023

What is machine learning? ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. Here, we’ll discuss the five major types and their applications.

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Machine Learning Modeling Deep Learning Predictive Modeling 79

DataRobot Blog

DECEMBER 15, 2022

Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning. To accelerate the process, you can also increase the number of modeling workers (number of jobs running at the same time).

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Forecasting Modeling ROI Machine Learning 81

CIO Business Intelligence

MARCH 22, 2023

And even if they’re used, they may not be enough to prevent an attacker from inferring sensitive information by analyzing encrypted or masked data patterns. The new class often uses advanced techniques such as deep learning, natural language processing, and computer vision to analyze and extract insights from the data.

Machine Learning 99

Machine Learning Cost-Benefit Modeling Risk 99

Alation

APRIL 14, 2022

The vision of big data freed organizations to capture more data sources at lower levels of detail and in vastly greater volumes. For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

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. 221) to 2019 (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

O'Reilly on Data

MARCH 24, 2020

Why is high-quality and accessible data foundational? The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. Generating data with a pre-specified analysis plan and running that analysis is good. Re-analyzing existing data is often very bad.”

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Machine Learning Statistics Data Quality Modeling 346