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

AltexSoft

APRIL 27, 2023

This phase involves numerous clinical trial systems and largely relies on clinical data management practices to organize information generated during medical research. How could data analytics boost this process? Obviously, precision medicine requires a large amount of data and is enabled by advanced ML models.

Data Analytics 52

Data Analytics Pharmaceutical Medical Manufacturing 52

Knowledge Hut

JULY 26, 2023

Consider exploring relevant Big Data Certification to deepen your knowledge and skills. What is Big Data? Big Data is the term used to describe extraordinarily massive and complicated datasets that are difficult to manage, handle, or analyze using conventional data processing methods.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

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 106

Datasets Machine Learning Data Data Science 106

Knowledge Hut

JANUARY 11, 2024

In this discussion, I will present some case studies to you that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals.

Data Science 98

Data Science Transportation Hospitality Banking 98

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 Datasets Healthcare 122

Knowledge Hut

DECEMBER 26, 2023

An information and computer scientist, database and software programmer, curator, and knowledgeable annotator are all examples of data scientists. They are all crucial for the administration of digital data collection to be successful. These are the reasons why data science is important in business.

Data Science 52

Data Science Unstructured Data Medical Healthcare 52

Knowledge Hut

NOVEMBER 16, 2023

Data Collection and Preparation To create effective Generative AI models, you should start by gathering a good dataset that matches your project's needs. Make sure the dataset is big enough to train a strong model. Let me help you with an in-depth overview of how to develop Generative AI models.

Medical 52

Medical Datasets Healthcare Finance 52

ProjectPro

DECEMBER 9, 2021

Generator Network ( Source ) As shown in the figure above, this generated image is fed to the Discriminator network as training data with fake labels. For true labels, the Discriminator model uses the raw input images from the MNIST dataset. We recommend using Anime Face Dataset. These are known as SGAN or Semi-Supervised GANs.

Project 52

Project Medical Datasets Deep Learning 52

U-Next

AUGUST 11, 2022

Signal Processing Techniques : These involve changing or manipulating data such that we can see things in it that aren’t visible through direct observation. . Many companies prefer to hire a Data Scientist to stay a step ahead of their competitors and devise plans and strategies for economic gains. is highly beneficial.

Medical 52

Medical Computer Science Scala Machine Learning 52

Knowledge Hut

FEBRUARY 28, 2024

To analyze big data, we require sophisticated software and dedicated databases. It involves extracting valuable insights from these massive datasets to gain a competitive advantage. Once we manage to harness it correctly, the future of big data is undeniable. Looking to dive into the world of data science ?

Big Data 52

Big Data Cloud Storage Healthcare SQL 52

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

Knowledge Hut

JULY 28, 2023

Data Requirements ML models typically require more labelled training data to achieve good performance. DL models can learn from large amounts of labelled or unlabelled data, potentially reducing the need for extensive labelled datasets. Data Pre-processing : Cleaning, transforming, and preparing the data for analysis.

Deep Learning 52

Deep Learning Machine Learning Unstructured Data Algorithm 52

Data Engineering Weekly

MAY 8, 2023

Receipt table (later referred to as table_receipts_index): It turns out that all the receipts were manually entered into the system, which creates unstructured data that is error-prone. This data collection method was chosen because it was simple to deploy, with each employee responsible for their own receipts.

Process 52

Process Banking Raw Data Finance 52

ProjectPro

JANUARY 31, 2023

Ultimately, the most important countermeasure against overfitting is adding more and better quality data to the training dataset. One solution to such problems is data augmentation , a technique for creating new training samples from existing ones. Table of Contents What is Data Augmentation in Deep Learning?

Deep Learning 52

Deep Learning Datasets Machine Learning Data 52

Edureka

AUGUST 2, 2023

What does a Data Processing Analysts do ? A data processing analyst’s job description includes a variety of duties that are essential to efficient data management. They must be well-versed in both the data sources and the data extraction procedures.

Data Process 52

Data Process Process Data Cleanse Data Mining 52

Edureka

SEPTEMBER 11, 2023

Generative algorithms go beyond the capabilities of discriminative models, which are best at identifying and classifying elements within a given data set, such as determining whether an email is spam. Applications include building up fictitious vistas and producing medical imagery for teaching healthcare algorithms.

Algorithm 52

Algorithm Entertainment Healthcare Deep Learning 52

AltexSoft

JUNE 16, 2022

The first ones involve data collection and preparation to ensure it’s of high quality and fits the task. Here, you also do data splitting to receive samples for training, validation, and testing. Then you choose an algorithm and do the model training on historic data and make your first predictions. What does it show?

Machine Learning 91

Machine Learning Hospitality Retail Medical 91

Knowledge Hut

DECEMBER 7, 2023

AI has a plethora of uses, including chatbots, recommendation engines, autonomous cars, and even medical diagnosis. Data Collection: Gather the necessary data that the AI model will use for learning and making predictions. The quality and quantity of data are crucial to the model's performance.

Machine Learning 52

Machine Learning Algorithm Medical Government 52

Knowledge Hut

DECEMBER 21, 2023

The authors use a dataset of emails gathered from a cloud computing environment to train and test the system. For availability in the event of server failure, the data redundancy layer replicates the cloud data across multiple cloud servers. They then assess its performance using metrics like precision, recall, and F1 score.

Cloud Computing 52

Cloud Computing Cloud Algorithm Healthcare 52

AltexSoft

MARCH 22, 2022

There are several key reasons, a business may consider using synthetic data: Cost and time efficiency. Synthetic data maybe way cheaper to generate than to collect from real world events, if you don’t have a proper dataset. Exploring rare data. The last one is specialized in medical synthetic data.

Machine Learning 52

Machine Learning IT Medical Insurance 52

Knowledge Hut

DECEMBER 29, 2023

Learning Outcomes: You will understand the processes and technology necessary to operate large data warehouses. Engineering and problem-solving abilities based on Big Data solutions may also be taught. Possible Careers: Data analyst Marketing analyst Data mining analyst Data engineer Quantitative analyst 3.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

AltexSoft

FEBRUARY 11, 2023

Let’s take an example of healthcare data which contains sensitive details called protected health information (PHI) and falls under the HIPAA regulations. They also must understand the main principles of how these services are implemented in data collection, storage and data visualization.

Data Architect 87

Data Architect Certification Generalist Big Data 87

AltexSoft

MAY 14, 2021

That’s quite a help when dealing with diverse data sets such as medical records, in which any inconsistencies or ambiguities may have harmful effects. 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? Data ingestion.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

NOVEMBER 18, 2021

ii) Targetted marketing through Customer Segmentation With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. We have listed another music recommendations dataset for you to use for your projects: Dataset1.

Data Science 52

Data Science Project Food Pharmaceutical 52

ProjectPro

APRIL 19, 2021

Collect Data Having clearly defined the business problem, a data analyst determines what data needs to be collected from existing data sources or databases. Collecting data in the real world is not as easy as downloading a dataset from Kaggle. Build and deploy data collection systems.

Portfolio 40

Portfolio Data Finance Certification 40

ProjectPro

AUGUST 16, 2021

A typical machine learning project involves data collection, data cleaning, data transformation, feature extraction, model evaluation approaches to find the best model fitting and hyper tuning parameters for efficiency. Outliers in the dataset are dropped, and null values are imputed.

Machine Learning 40

Machine Learning Coding Project Deep Learning 40

AltexSoft

NOVEMBER 23, 2021

With data virtualization, Pfizer managed to cut the project development time by 50 percent. In addition to the quick data retrieval and transfer, the company standardized product data to ensure consistency in product information across all research and medical units. How to get started with data virtualization.

Process 69

Process Data Lake Metadata Data Warehouse 69

ProjectPro

JUNE 2, 2015

Business win online when they use hard-to-copy technology to deliver a superior customer experience through mining larger and larger datasets.”- It is estimated that a data analyst spends close to 80% of the time in cleaning and preparing the big data for analysis whilst only 20% is actually spent on analysis work.

Big Data 40

Big Data Hadoop Banking Retail 40

U-Next

AUGUST 26, 2022

Another recollection of one of our Data Analytics initiatives: In the healthcare industry, the type of segmentation in combination with several applied filters (such as diagnoses and prescribed drugs) permitted determining the impact of pharmaceuticals. Through the use of advanced equipment, medications, etc., Predictive Analytics.

Data Analytics 40

Data Analytics Insurance Transportation Healthcare 40