Data Science Dojo

AUGUST 19, 2024

Business Benefits: Organizations are recognizing the value of AI and data science in improving decision-making, enhancing customer experiences, and gaining a competitive edge An AI research scientist acts as a visionary, bridging the gap between human intelligence and machine capabilities. Privacy: Protecting user privacy and data security.

AI 364

AI AI Machine Learning Machine Learning 364

Dataconomy

JUNE 16, 2025

How structured data works Understanding how structured data operates involves recognizing the role of data models and repositories. These frameworks facilitate the organization and integrity of data across various applications. They represent the structure and constraints that govern how data is stored.

Database 91

Database Data Lakes ETL Natural Language Processing 91

Data Science Dojo

JUNE 7, 2023

The primary aim is to make sense of the vast amounts of data generated daily by combining statistical analysis, programming, and data visualization. It is divided into three primary areas: data preparation, data modeling, and data visualization.

Data Science 195

Data Science Data Visualization Data Scientist Machine Learning 195

AWS Machine Learning Blog

NOVEMBER 15, 2023

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model.

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Algorithm AWS ML ML 118

Smart Data Collective

DECEMBER 19, 2021

Accordingly, before using that data in machine learning or an algorithm, you need to convert it into a precise format suitable for the system to inherit it. For instance, the Random Forest Algorithm in Python doesn’t support null values. Hence, data preprocessing is essential and required.

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Python Machine Learning Machine Learning Algorithm 141

Dataconomy

AUGUST 25, 2023

Performance metrics in machine learning are the most accurate way to measure how close your algorithm is to what you want. As we develop and fine-tune machine learning models, it’s imperative to have a way to measure their performance accurately. Just as students learn from textbooks, the algorithm learns from data examples.

Algorithm 91

Algorithm ML ML Machine Learning 91

Hacker News

NOVEMBER 19, 2024

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)

Computer Science 152

Computer Science Computer Science ML ML 152

Dataconomy

APRIL 2, 2025

The Bitcoin price outlook is being reshaped by machine learning models, real-time analytics and sentiment-driven algorithms that enhance traditional charting methods. Clustering algorithms (K-Means) classify wallet activity to forecast shifts on a larger scale. What does Bitcoin price forecast data models say?

Data Science 103

Data Science Natural Language Processing Machine Learning Machine Learning 103

Pickl AI

JUNE 12, 2025

The values of these parameters are optimized iteratively to minimize prediction error, allowing the model to capture complex patterns in data. Model parameters are distinct from hyperparameters, which are set externally before training and guide the learning process itself. What Are Hyperparameters?

Machine Learning 52

Machine Learning Machine Learning Algorithm Support Vector Machines 52

Hacker News

FEBRUARY 26, 2024

Thus, power and time are saved through parallel execution and usage of processing components with local memory elements, optimized for running data-intensive algorithms. Specifically, MeMPA can perform up to three different instructions, each on different data blocks, concurrently.

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Algorithm Data Models Data Modeling 127

Smart Data Collective

JUNE 4, 2021

Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models.

Data Science 133

Data Science Exploratory Data Analysis Machine Learning Machine Learning 133

Dataconomy

MAY 2, 2023

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. In the context of a business, machine learning engineers are responsible for creating bots that are utilized for chat purposes or data collection. They build the future.

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ML ML Machine Learning Machine Learning 110

phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Models Data Modeling Data Warehouse 52

Data Science Dojo

FEBRUARY 6, 2025

Large Language Models ( LLMs ) have emerged as a cornerstone technology in the rapidly evolving landscape of artificial intelligence. These models are trained using vast datasets and powered by sophisticated algorithms. Below are a few reasons that make data annotation a critical component for language models.

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AI AI ML ML 235

Data Science Dojo

JANUARY 16, 2023

Every individual analysis the data obtained via their experience to generate a final decision. Put more concretely, data analysis involves sifting through data, modeling it, and transforming it to yield information that guides strategic decision-making.

Data Analysis 195

Data Analysis Data Analysis Data Analyst Big Data 195

Data Science Dojo

JANUARY 10, 2023

So, what makes a good data science profile? On the technical end, there are mathematical concepts, algorithms, data structures, etc. To conclude, a tool can be an excellent way to implement data science skills. Data scientists only work on predictive modeling Another myth!

Data Science 195

Data Science Data Scientist Data Analyst Machine Learning 195

Dataconomy

MAY 26, 2025

Data collection and storage These engineers design frameworks to collect data from diverse sources and store it in systems like data warehouses and data lakes, ensuring efficient data retrieval and processing.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

Dataversity

JANUARY 13, 2021

In marketing, artificial intelligence (AI) is the process of using data models, mathematics, and algorithms to generate insights that marketers can use. Click here to learn more about Gilad David Maayan. What Is Artificial Intelligence Marketing?

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Artificial Intelligence Artificial Intelligence Data Models Data Modeling 69

Data Science Dojo

APRIL 7, 2025

This means you can keep data completely private and still derive value from it without ever exposing the raw information. These are the steps to homomorphic encryption: Sensitive data is encrypted using a special homomorphic encryption algorithm. This party performs analysis or computations directly on the encrypted data.

AI 195

AI AI Machine Learning Machine Learning 195

The Data Administration Newsletter

AUGUST 17, 2021

If you are planning on using predictive algorithms, such as machine learning or data mining, in your business, then you should be aware that the amount of data collected can grow exponentially over time.

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Data Mining Data Mining Data Mining Big Data 115

Smart Data Collective

OCTOBER 20, 2021

AI-data mapping tools allow even non-technical business users to create intelligent data mappings using Machine Learning algorithms. Not only will this increase the speed but also the accuracy of the data mapping process. Legacy solutions lack precision and speed while handling big data.

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ML ML Big Data Big Data 133

Data Science Dojo

MARCH 7, 2023

These skills include programming languages such as Python and R, statistics and probability, machine learning, data visualization, and data modeling. To perform exploratory data analysis effectively, data scientists must have a strong understanding of math and statistics.

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Data Scientist Exploratory Data Analysis Data Science Data Visualization 221

Heartbeat

MARCH 8, 2023

Data mining, text classification, and information retrieval are just a few applications. To extract themes from a corpus of text data and then use these themes as features in text classification algorithms, topic modeling can be used in text classification. Naive Bayes is commonly used for spam classification.

Machine Learning 52

Machine Learning Machine Learning Algorithm Data Mining 52

AWS Machine Learning Blog

JULY 29, 2024

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time series forecasts. With SageMaker Canvas, you get faster model building , cost-effective predictions, advanced features such as a model leaderboard and algorithm selection, and enhanced transparency.

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ML ML Algorithm AWS 117

Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

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Analytics Analytics Big Data Analytics Big Data Analytics 203

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.

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Predictive Analytics Analytics Analytics Decision Trees 139

Pickl AI

JUNE 5, 2025

Model Development and Validation: Building machine learning models tailored to business problems such as customer churn prediction, fraud detection, or demand forecasting. Validation techniques ensure models perform well on unseen data.

Data Scientist 52

Data Scientist Data Science Power BI Machine Learning 52

AWS Machine Learning Blog

SEPTEMBER 26, 2024

During the iterative research and development phase, data scientists and researchers need to run multiple experiments with different versions of algorithms and scale to larger models. However, they require more sophisticated modeling techniques and increased computational resources.

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Clustering Algorithm ML ML 119

ODSC - Open Data Science

APRIL 28, 2023

Whether it’s an insurance company leveraging location for better underwriting or risk assessment, a financial services organization enriching transactions for validation and accurate merchant assignment, or a telecommunications company optimizing 5G rollouts and creating new services, there’s one essential commonality: location data.

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ML ML Data Silos Data Quality 98

Smart Data Collective

MAY 29, 2023

This bias can emerge due to multiple factors, such as the training data, algorithmic design, and human influence. Recognizing and comprehending the different forms of algorithm bias is crucial to develop effective strategies for bias mitigation.

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AI AI Algorithm Artificial Intelligence 96

AWS Machine Learning Blog

DECEMBER 9, 2024

The scenario is using the XGBoost algorithm to train a binary classification model. Both the data processing job and model training job use @remote decorator so that the jobs are running in the SageMaker-associated private subnets and security group from your private VPC. config_yaml = f""" SchemaVersion: '1.0'

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AWS ML ML Data Scientist 103

Data Science Connect

JULY 25, 2023

Data scientists must strike a balance between the platform’s simplicity and the customization required for complex data models and algorithms. Additionally, the use of these platforms may raise security concerns, as sensitive data could be mishandled by non-experts.

Data Scientist 100

Data Scientist Data Analysis Data Analysis Data Models 100

IBM Journey to AI blog

APRIL 5, 2024

Predict functionality builds predictive models to predict imminent failures and calculate assets’ remaining useful life. These models often incorporate machine learning and AI algorithms to detect the onset of degradation mechanisms in an early stage.

Data Models 82

Data Models Data Modeling Machine Learning Machine Learning 82

Smart Data Collective

NOVEMBER 24, 2019

One of the biggest applications is that new predictive analytics models are able to get a better understanding of the relationships between employees and find areas where they break down. These big data algorithms can offer insights to improve harmony within the team. Big Data is the Key to Stronger Team Extension Models.

Big Data 101

Big Data Big Data Predictive Analytics Data Models 101

Tableau

APRIL 12, 2022

In addition, the new relevance algorithm intelligently corrects for common issues like misspellings, spacing, and punctuation. Easily swap root tables in your data model. Data models can become difficult to manage and understand as you add additional tables. To learn more, read Relate Your Data in Tableau Help.

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Tableau Data Preparation Data Models Data Modeling 98

Tableau

APRIL 12, 2022

In addition, the new relevance algorithm intelligently corrects for common issues like misspellings, spacing, and punctuation. Easily swap root tables in your data model. Data models can become difficult to manage and understand as you add additional tables. To learn more, read Relate Your Data in Tableau Help.

Tableau 98

Tableau Data Preparation Data Models Data Modeling 98

Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. What is Machine Learning?

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Machine Learning Machine Learning Support Vector Machines Decision Trees 52