Knowledge Hut

SEPTEMBER 26, 2023

With technological advancements and digital transformations, businesses are taking data very seriously. In today's business environment, data is an invaluable asset. To derive key insights from data, business intelligence systems, and tools assist data scientists and business analysts.

BI 52

BI Business Intelligence Raw Data Data Analysis 52

AltexSoft

MAY 12, 2022

Particularly, we’ll explain how to obtain audio data, prepare it for analysis, and choose the right ML model to achieve the highest prediction accuracy. But first, let’s go over the basics: What is the audio analysis, and what makes audio data so challenging to deal with. Labeling of audio data in Audacity.

Machine Learning 122

Machine Learning Building Datasets Healthcare 122

Databand.ai

AUGUST 30, 2023

Data testing tools: Key capabilities you should know Helen Soloveichik August 30, 2023 Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing and maintaining data quality. There are several types of data testing tools.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Cloudera

OCTOBER 11, 2021

The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.

Data Engineering 92

Data Engineering Data Engineer Cloud Engineering 92

AltexSoft

FEBRUARY 21, 2023

For machine learning algorithms to predict prices accurately, people who do the data preparation must consider these factors and gather all this information to train the model. Data relevance. Data sources In developing hotel price prediction models, gathering extensive data from different sources is crucial. .”

Hospitality 52

Hospitality Datasets Algorithm Machine Learning 52

Knowledge Hut

JANUARY 29, 2024

Data Wrangler: Another data cleaning and transformation tool, offering flexibility in data preparation. Examples of Data Wrangling Data wrangling can be applied in various scenarios, making it a versatile and valuable process.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Knowledge Hut

DECEMBER 26, 2023

Due to its vastness and complexity, no traditional data management system can adequately store or process this data. The New York Stock Exchange, which generates one terabyte of new trade data each day, is a classic example of big data. Key Benefits and Takeaways Learn the basics of big data with Spark.

Big Data 75

Big Data Data Mining Business Intelligence Machine Learning 75

AltexSoft

AUGUST 25, 2021

Besides simply looking for email addresses associated with spam, these systems notice slight indications of spam emails, like bad grammar and spelling, urgency, financial language, and so on. Such dialog systems are the hardest to pull off and are considered an unsolved problem in NLP. Any ML project starts with data preparation.

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Process Deep Learning Datasets Machine Learning 139

Knowledge Hut

FEBRUARY 1, 2024

You cannot expect your analysis to be accurate unless you are sure that the data on which you have performed the analysis is free from any kind of incorrectness. Data cleaning in data science plays a pivotal role in your analysis. It’s a fundamental aspect of the data preparation stages of a machine learning cycle.

Data Science 52

Data Science Process Data Cleanse Datasets 52

Knowledge Hut

MARCH 19, 2024

Machine Learning Software Engineers are at the forefront of this revolution, applying their expertise to develop intelligent systems and algorithms. Data Preparation: The Machine Learning Engineer Software engineers get, clean, and process data so that it can be used in machine learning models.

Software Engineer 52

Software Engineer Software Engineering Machine Learning Engineering 52

ProjectPro

JANUARY 31, 2023

Key features Hadoop RDBMS Overview Hadoop is an open-source software collection that links several computers to solve problems requiring large quantities of data and processing. RDBMS is a part of system software used to create and manage databases based on the relational model. RDBMS stores structured data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Knowledge Hut

JANUARY 24, 2024

Connectivity with Live and In-Memory Data Allows the user to freely combine data from several types of data sources. Provides Great Security Data connections and user access feature a fail-safe security system based on authentication and authorization mechanisms.

BI 98

BI Business Intelligence Non-relational Database Data Analysis 98

Knowledge Hut

JANUARY 30, 2024

In summary, data extraction is a fundamental step in data-driven decision-making and analytics, enabling the exploration and utilization of valuable insights within an organization's data ecosystem. What is the purpose of extracting data? The process of discovering patterns, trends, and insights within large datasets.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Databand.ai

AUGUST 30, 2023

Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality. There are several types of data testing tools. Data profiling tools: Profiling plays a crucial role in understanding your dataset’s structure and content.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

ProjectPro

JANUARY 24, 2023

Time-saving: SageMaker automates many of the tasks, by creating a pipeline starting from data preparation and ML model training, which saves time and resources. Analyze – Data Wrangler allows you to analyze the features in your dataset at any stage of the data preparation process.

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Building Algorithm Machine Learning AWS 52

Knowledge Hut

JUNE 28, 2023

By examining these factors, organizations can make informed decisions on which approach best suits their data analysis and decision-making needs. Parameter Data Mining Business Intelligence (BI) Definition The process of uncovering patterns, relationships, and insights from extensive datasets.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52

AltexSoft

OCTOBER 30, 2021

Regardless of the structure they eventually build, it’s usually composed of two types of specialists: builders, who use data in production, and analysts, who know how to make sense of data. Distinction between data scientists and engineers is similar. Data scientist’s responsibilities — Datasets and Models.

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Data Engineering Data Engineer Engineering Machine Learning 98

Knowledge Hut

JANUARY 29, 2024

On the other hand, data science is a technique that collects data from various resources for data preparation and modeling for extensive analysis. Cloud Computing provides storage, scalable compute, and network bandwidth to handle substantial data applications. Therefore, knowing both areas can be advantageous.

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Cloud Computing Data Science Cloud Amazon Web Services 98

AltexSoft

MAY 12, 2021

This exposure makes the respiratory system extremely susceptible to a wide range of diseases, from long-familiar asthma to novel COVID-19. Particularly, we’ll present our findings on what it takes to prepare a medical image dataset, which models show best results in medical image recognition , and how to enhance the accuracy of predictions.

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Medical Healthcare Datasets Machine Learning 72

AltexSoft

JUNE 21, 2023

For machine learning models to predict ADR effectively, a comprehensive understanding of these variables is required in the data preparation stage. Recognizing which factors to consider and which to exclude is a critical step in the data preparation process. Data shortage and poor quality.

Hospitality 52

Hospitality Management Machine Learning Datasets 52

Precisely

DECEMBER 4, 2023

For example, location-based insights can be delivered through web GIS (geographic information systems) applications or data science models. They also need a strong foundation of data science to underpin those efforts. Many organizations get bogged down with data preparation, which can consume up to 80% of data science efforts.

Telecommunication 62

Telecommunication Data Science Data Integration Data Preparation 62

AltexSoft

JULY 10, 2023

AI image generators are trained on an extensive amount of data, which comprises large datasets of images. Through the training process, the algorithms learn different aspects and characteristics of the images within the datasets. This labeled dataset is the “ground truth” that enables a feedback loop.

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Medical Datasets Algorithm Entertainment 64

Knowledge Hut

SEPTEMBER 29, 2023

Its records modeling prowess and overall performance with large datasets make it ideal for companies. Power BI vs Excel: Size of Data Size of the Data Power BI: Power BI is optimized for coping with massive datasets effectively.

BI 52

BI Business Intelligence Datasets Data Analysis 52

Knowledge Hut

OCTOBER 27, 2023

Microsoft created Power BI, a business analytics tool that enables users to visualize and analyze data from various sources quickly and interactively. It provides a wide range of features and functionalities, including data preparation, data modeling, data visualization, and collaboration tools.

BI 52

BI Project Datasets Healthcare 52

DataKitchen

DECEMBER 9, 2022

DataOps involves close collaboration between data scientists, IT professionals, and business stakeholders, and it often involves the use of automation and other technologies to streamline data-related tasks. One of the key benefits of DataOps is the ability to accelerate the development and deployment of data-driven solutions.

Machine Learning 52

Machine Learning Data Preparation Government Data Analytics 52

AltexSoft

NOVEMBER 17, 2021

Namely, we’ll look at how rule-based systems and machine learning models work in this context. Sentiment analysis is a narrow case of NLP-based systems that focuses on understanding the emotion, opinion, or attitude expressed through the text. An NLP-based system can be implemented for a ticket routing task in this case.

Machine Learning 75

Machine Learning Insurance Medical Healthcare 75

Knowledge Hut

MAY 9, 2024

In this blog post, we will look at some of the world's highest paying data science jobs, what they entail, and what skills and experience you need to land them. What is Data Science? Data science also blends expertise from various application domains, such as natural sciences, information technology, and medicine.

Data Science 52

Data Science Data Mining Data Architect Programming Language 52

Knowledge Hut

DECEMBER 7, 2023

It transforms data from many sources in order to create dynamic dashboards and Business Intelligence reports. Data preparation, modelling, and visualization are expedited by this simple, low-cost method. Modern data privacy technology is also considerably more affordable than its competitors.

BI 52

BI Business Intelligence Datasets Data Analysis 52

ProjectPro

FEBRUARY 8, 2023

Scale Existing Python Code with Ray Python is popular among data scientists and developers because it is user-friendly and offers extensive built-in data processing libraries. For analyzing huge datasets, they want to employ familiar Python primitive types. Establish a crawler schedule.

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ProjectPro

AUGUST 30, 2022

This article will walk you through how one can start by exploring a loan prediction system as a data science and machine learning problem and build a system/application for loan prediction using your own machine learning project. Top 5 Loan Prediction Datasets to Practice Loan Prediction Projects Univ.AI

Machine Learning 52

Machine Learning Coding Project Datasets 52

ProjectPro

AUGUST 9, 2021

Top 20 Python Projects for Data Science Without much ado, it’s time for you to get your hands dirty with Python Projects for Data Science and explore various ways of approaching a business problem for data-driven insights. 1) Music Recommendation System on KKBox Dataset Music in today’s time is all around us.

Data Science 52

Data Science Python Project Datasets 52

AltexSoft

NOVEMBER 15, 2022

Dataset preparation and construction. The starting point of any machine learning task is data. A lot of data, to be exact. A lot of quality data, to be even more exact. To learn the basics, you can read our dedicated article on how data is prepared for machine learning or watch a short video.

Hospitality 52

Hospitality Building Datasets Machine Learning 52

AltexSoft

DECEMBER 15, 2021

Namely, AutoML takes care of routine operations within data preparation, feature extraction, model optimization during the training process, and model selection. In the meantime, we’ll focus on AutoML which drives a considerable part of the MLOps cycle, from data preparation to model validation and getting it ready for deployment.

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Machine Learning Deep Learning Algorithm Telecommunication 59

ProjectPro

MAY 31, 2021

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications. Kicking off a big data analytics project is always the most challenging part.

Big Data 52

Big Data Coding Project Hadoop 52

AltexSoft

MAY 14, 2021

As you now know the key characteristics, it gets clear that not all data can be referred to as Big Data. What is Big Data analytics? Big Data analytics is the process of finding patterns, trends, and relationships in massive datasets that can’t be discovered with traditional data management techniques and tools.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Rockset

JUNE 17, 2021

Support tickets, sales transactions and marketing interactions are stored in operational databases which serve as a system of record. As operational databases were not designed for analytics, data is replicated to Rockset and automatically indexed for fast search, aggregations and joins.

Data Lake 40

Data Lake Architecture Data Preparation Database 40

ProjectPro

AUGUST 24, 2021

And if you are aspiring to become a data engineer, you must focus on these skills and practice at least one project around each of them to stand out from other candidates. Explore different types of Data Formats: A data engineer works with various dataset formats like.csv,josn,xlx, etc.

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Data Engineering Data Engineer Coding Project 52