AltexSoft

OCTOBER 18, 2022

Regardless of industry, data is considered a valuable resource that helps companies outperform their rivals, and healthcare is not an exception. In this post, we’ll briefly discuss challenges you face when working with medical data and make an overview of publucly available healthcare datasets, along with practical tasks they help solve.

Medical 52

Medical Datasets Machine Learning Hospitality 52

Monte Carlo

FEBRUARY 26, 2025

To make sure they were measuring real world impacts, Koller and Bosley selected two publicly available datasets characterized by large volumes and imbalanced classifications, reflective of real-world scenarios where classification algorithms often need to detect rare events such as fraud, purchasing intent, or toxic behavior. Who owns it?

Banking 52

Banking Datasets Data Machine Learning 52

Cloudera

SEPTEMBER 15, 2021

The edge is a critical component of many digital transformation implementations, and particularly IoT deployments, for three main reasons — immediacy, fast-changing datasets and scalability. Without them, data collected by IoT sensors, cameras and other devices would have to travel to a data center located hundreds or thousands of miles away.

Medical 120

Medical Retail Datasets Algorithm 120

Cloudera

FEBRUARY 8, 2021

To accomplish this, ECC is leveraging the Cloudera Data Platform (CDP) to predict events and to have a top-down view of the car’s manufacturing process within its factories located across the globe. . Having completed the Data Collection step in the previous blog, ECC’s next step in the data lifecycle is Data Enrichment.

Data Pipeline 118

Data Pipeline Building Manufacturing Data Warehouse 118

U-Next

OCTOBER 20, 2022

An inaccuracy known as bias in data occurs when specific dataset components are overweighted or overrepresented. What Does Bias Mean in Data Analytics? . We must first gather data before we can evaluate it or apply Machine Learning techniques. The source material is not the only way bias can enter data.

Data Collection 40

Data Collection Algorithm Datasets Data Analysis 40

Databand.ai

MAY 30, 2023

Data quality refers to the degree of accuracy, consistency, completeness, reliability, and relevance of the data collected, stored, and used within an organization or a specific context. High-quality data is essential for making well-informed decisions, performing accurate analyses, and developing effective strategies.

Data Cleanse 85

Data Cleanse Datasets Data Governance Data Validation 85

Towards Data Science

AUGUST 29, 2023

Today, we will delve into the intricacies the problem of missing data , discover the different types of missing data we may find in the wild, and explore how we can identify and mark missing values in real-world datasets. Image by Author. Let’s consider an example. Image by Author. Image by Author. Image by Author.

Datasets 117

Datasets Machine Learning Data Data Science 117

AltexSoft

MAY 12, 2022

Audio data transformation basics to know. Before diving deeper into processing of audio files, we need to introduce specific terms, that you will encounter at almost every step of our journey from sound data collection to getting ML predictions. Labeling of audio data in Audacity. Source: Towards Data Science.

Machine Learning 122

Machine Learning Building Deep Learning Datasets 122

Knowledge Hut

FEBRUARY 29, 2024

Then, based on this information from the sample, defect or abnormality the rate for whole dataset is considered. This process of inferring the information from sample data is known as ‘inferential statistics.’ A database is a structured data collection that is stored and accessed electronically.

Data Science 98

Data Science Datasets Machine Learning Database Design 98

Cloudera

JANUARY 20, 2021

The data journey is not linear, but it is an infinite loop data lifecycle – initiating at the edge, weaving through a data platform, and resulting in business imperative insights applied to real business-critical problems that result in new data-led initiatives. Data Collection Challenge. Factory ID.

Manufacturing 110

Manufacturing Data Warehouse Kafka Retail 110

Data Engineering Podcast

NOVEMBER 20, 2022

What are the biggest data-related challenges that you face (technically or organizationally)? How does that influence your approach to instrumentation/data collection in the end-user experience? Can you describe the current architecture of your data platform? Multiplayer games are very sensitive to latency.

Systems 130

Systems Metadata Data Pipeline MongoDB 130

RandomTrees

NOVEMBER 25, 2024

Generative AI employs ML and deep learning techniques in data analysis on larger datasets, resulting in produced content that has a creative touch but is also relevant. In the telecom sector, this technology is assisting with operations, customer satisfaction as well as business development.

Telecommunication 52

Telecommunication IT Unstructured Data Data Mining 52

Knowledge Hut

MAY 2, 2024

They are Statistics Probability Calculus Linear Algebra Machine learning is all about dealing with data. We collect the data from organizations or from any repositories like Kaggle, UCI etc., and perform various operations on the dataset like cleaning and processing the data, visualizing and predicting the output of the data.

Machine Learning 98

Machine Learning Algorithm Datasets Python 98

Cloudera

APRIL 9, 2021

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection.

Machine Learning 98

Machine Learning Manufacturing Data Collection Data Science 98

Towards Data Science

MARCH 17, 2023

Take advantage of the distributive power of Apache Spark and concurrently train thousands of auto-regressive time-series models on big data Photo by Ricardo Gomez Angel on Unsplash 1. Concurrently training multiple models on a huge dataset is actually one of the few cases that justifies training on a distributed cluster, such as Spark.

Datasets 98

Datasets Scala Machine Learning SQL 98

Knowledge Hut

JUNE 26, 2023

Use Stack Overflow Data for Analytic Purposes Project Overview: What if you had access to all or most of the public repos on GitHub? As part of similar research, Felipe Hoffa analysed gigabytes of data spread over many publications from Google's BigQuery data collection. Which queries do you have?

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Engineering at Meta

AUGUST 8, 2023

Then the server will apply the same hash algorithm and blinding operation with secret key b to all the passwords from the leaked password dataset. First, hashing and blinding each password in the leaked password dataset at runtime cause a lot of latency at the server side. Sharding the leaked password dataset.

Datasets 84

Datasets Bytes Algorithm Designing 84

AltexSoft

MAY 27, 2022

The main sources of such data are electronic health record ( EHR ) systems which capture tons of important details. Yet, there’re a few essential things to keep in mind when creating a dataset to train an ML model. Inpatient data anonymization. Medical datasets with inpatient details. Syntegra synthetic data.

Hospitality 98

Hospitality Medical Healthcare Algorithm 98

Data Engineering Podcast

MAY 15, 2022

Summary Industrial applications are one of the primary adopters of Internet of Things (IoT) technologies, with business critical operations being informed by data collected across a fleet of sensors. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets.

Designing 100

Designing MongoDB AWS SQL 100

Knowledge Hut

JULY 4, 2023

Data analysis and Interpretation: It helps in analyzing large and complex datasets by extracting meaningful patterns and structures. By identifying and understanding patterns within the data, valuable insights can be gained, leading to better decision-making, and understanding of underlying relationships.

Machine Learning 98

Machine Learning Medical Algorithm Deep Learning 98

Knowledge Hut

MAY 31, 2023

These projects typically involve a collaborative team of software developers, data scientists, machine learning engineers, and subject matter experts. The development process may include tasks such as building and training machine learning models, data collection and cleaning, and testing and optimizing the final product.

Project 96

Project Healthcare Deep Learning Transportation 96

AltexSoft

JUNE 7, 2021

Apache Hadoop is an open-source framework written in Java for distributed storage and processing of huge datasets. The keyword here is distributed since the data quantities in question are too large to be accommodated and analyzed by a single computer. A powerful Big Data tool, Apache Hadoop alone is far from being almighty.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Cloudera

AUGUST 17, 2020

The resulting set of cases becomes our new dataset to use for the next phase. This began with taking a dataset containing 10k sentences and labeling them as one of the following: Technically Relevant – Contains technical content that’s relevant to the case discussion. Extract Technical Sentences.

Machine Learning 88

Machine Learning Datasets Transportation Accessible 88

Knowledge Hut

MAY 3, 2024

Memory Management RDD is used by Spark to store data in a distributed fashion (i.e., Spark's primary data structure is Resilient Distributed Datasets (RDD). It is a distributed collection of immutable things. Each dataset in an RDD is split into logical divisions that may be calculated on several cluster nodes.

Kafka 98

Kafka Scala Java Amazon Web Services 98

Netflix Tech

JULY 21, 2022

This is done by first elaborating on the dataset curation stage?—?specially Since memory management is not something one usually associates with classification problems, this blog focuses on formulating the problem as an ML problem and the data engineering that goes along with it. The dataset will thus be very biased/skewed.

Machine Learning 73

Machine Learning Datasets Big Data Data Pipeline 73

Engineering at Meta

NOVEMBER 12, 2024

Promoting infrastructure reliability Our root dataset has also served as a useful proxy for client health. Indeed, numerous detectors and alarms have been built off our dataset to help us perform big migrations safely. We also occasionally put an increased load on Scuba , which is optimized to be performant for real-time data (i.e.,

Algorithm 80

Algorithm Datasets Coding Java 80

Knowledge Hut

APRIL 23, 2024

Big Data vs Small Data: Volume Big Data refers to large volumes of data, typically in the order of terabytes or petabytes. It involves processing and analyzing massive datasets that cannot be managed with traditional data processing techniques.

Big Data 52

Big Data Datasets Data Analysis Media 52

RandomTrees

DECEMBER 12, 2023

It is necessary to tailor sensitive or regulated data to specific conditions to achieve the results that authentic data cannot deliver. Additionally, providing DevOps teams with datasets to test and confirm software. Computer vision can generate synthetic data in two ways. Ensures The Privacy Of Personal Data.

Deep Learning 52

Deep Learning Datasets Healthcare Algorithm 52

Databand.ai

JULY 6, 2023

Data Anomaly: Types, Causes, Detection, and Resolution Helen Soloveichik July 6, 2023 What Is Data Anomaly? A data anomaly, also known as an outlier, is an observation or data point that deviates significantly from the norm, making it inconsistent with the rest of the dataset.

Datasets 52

Datasets Algorithm Machine Learning Data Analysis 52

Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Towards Data Science

DECEMBER 4, 2023

We won’t be alone in this data collection; thankfully, there are data integration tools available in the market that can be adopted to configure and maintain ingestion pipelines in one place (e.g. At this stage, it’s not about ingesting data and we’ll focus more and more on business use cases.

Data Engineer 98

Data Engineer Data Engineering Engineering Data Lake 98

Knowledge Hut

DECEMBER 26, 2023

These skills are essential to collect, clean, analyze, process and manage large amounts of data to find trends and patterns in the dataset. The dataset can be either structured or unstructured or both. In this article, we will look at some of the top Data Science job roles that are in demand in 2024.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

Knowledge Hut

FEBRUARY 7, 2023

DATA Step: The data step includes all SAS statements, beginning with line data and ending with line datalines. In this step, we can define and modify the values in the relevant dataset. We use different SAS statements for reading the data, cleaning and manipulating it in the data step prior to analyzing it.

Data Science 52

Data Science Datasets SQL Certification 52

RandomTrees

APRIL 24, 2024

Components of LLMOps Data Collection and Preparation Model Development Prompt Engineering, RAG and Model Fine-tuning Model Deployment Observability RLHF 1. Data Collection and Preparation Data collection and preparation are a must if one wants to train a Large Language Model (LLM) from scratch or fine-tune one.

Machine Learning 52

Machine Learning Datasets Data Collection Engineering 52

Knowledge Hut

APRIL 25, 2024

Recognizing the difference between big data and machine learning is crucial since big data involves managing and processing extensive datasets, while machine learning revolves around creating algorithms and models to extract valuable information and make data-driven predictions.

Machine Learning 52

Machine Learning Big Data Unstructured Data Data Mining 52

Monte Carlo

MARCH 18, 2024

Choose the Right Data to Audit Your organization may want to audit your entire arsenal of data, or you may select a few datasets to audit individually. Choose the right datasets and clearly communicate to the responsible parties why the audit is being performed. Am I repeating someone else’s work?”).

Datasets 52

Datasets Data Pipeline BI Data 52

KDnuggets

OCTOBER 28, 2019

Data collection is one of the first steps of the data lifecycle — you need to get all the data you require in the first place. To collect the right data, you need to know where to find it and determine the effort involved in collecting it.

Data Collection 79

Data Collection Data IT Datasets 79