Smart Data Collective

JUNE 4, 2021

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. These data models predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century.

Data Science 133

Data Science Exploratory Data Analysis Machine Learning Machine Learning 133

Smart Data Collective

NOVEMBER 18, 2020

Complex mathematical algorithms are used to segment data and estimate the likelihood of subsequent events. Every Data Scientist needs to know Data Mining as well, but about this moment we will talk a bit later. Where to Use Data Science? Data Mining Techniques and Data Visualization.

Data Mining 144

Data Mining Data Mining Data Mining Data Science 144

AWS Machine Learning Blog

JANUARY 5, 2024

MLOps – The architecture implements a SageMaker model monitoring pipeline for continuous model quality governance by validating data and model drift as required by the defined schedule. Whenever drift is detected, an event is launched to notify the respective teams to take action or initiate model retraining.

AWS 137

AWS Data Science ML ML 137

Dataconomy

MAY 2, 2023

By acquiring expertise in statistical techniques, machine learning professionals can develop more advanced and sophisticated algorithms, which can lead to better outcomes in data analysis and prediction. These techniques can be utilized to estimate the likelihood of future events and inform the decision-making process.

ML 110

ML ML Machine Learning Machine Learning 110

Cambridge Intelligence

NOVEMBER 8, 2023

Analysts rely on our data visualization toolkits to spot hidden patterns in their visualized data. They investigate these patterns and use them to predict – and, if possible, prevent – future events. What role can interactive data visualization play?

Data Visualization 64

Data Visualization Clustering Database Data Modeling 64

IBM Journey to AI blog

SEPTEMBER 19, 2023

Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred. js and Tableau Data science, data analytics and IBM Practicing data science isn’t without its challenges. There can be fragmented data, a short supply of data science skills and rigid IT standards for training and deployment.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Cambridge Intelligence

FEBRUARY 20, 2024

FREE: Managing fraud The ultimate guide to fraud detection, investigation and prevention using data visualization GET YOUR FREE GUIDE The role of new & existing technology For many years, credit card companies have relied on analytics, algorithms and decision trees to power their fraud strategy.

AI 59

AI AI Machine Learning Machine Learning 59

Cambridge Intelligence

NOVEMBER 20, 2023

Bring world champion chess matches to life with KronoGraph & KeyLines Many of our customers integrate their visual analysis tools with Azure Cosmos DB because it lets them store both graph and tabular data, and choose which API they want to use for queries. Which data elements should be nodes and what should connect them?

Azure 64

Azure Database Data Modeling Data Models 64

Tableau

DECEMBER 1, 2021

Four reference lines on the x-axis indicate key events in Tableau’s almost two-decade history: The first Tableau Conference in 2008. April 2018), which focused on users who do understand joins and curating federated data sources. Visual encoding, in particular, tapped the power of the human visual system. Release v1.0

Tableau 145

Tableau ML ML Database 145

Cambridge Intelligence

OCTOBER 25, 2023

When you design your data model, you’ll probably begin by sketching out your data in a graph format – representing entities as nodes and relationships as links. Working in a graph database means you can take that whiteboard model and apply it directly to your schema with relatively few adaptations.

Database 64

Database Data Modeling Data Models Algorithm 64

Pickl AI

JULY 23, 2023

Data Analysis Data analysis is the heart of deriving insights from the gathered information. Data Visualization Data visualization transforms complex data sets into intuitive and visually appealing representations. Graphs, charts, and infographics aid in conveying information effectively.

Analytics 52

Analytics Analytics Predictive Analytics Data Analysis 52

Cambridge Intelligence

NOVEMBER 19, 2024

Graph visualization SDKs would have been a huge asset to those projects. To prove this, I built my own digital twin using the KeyLines graph visualization toolkit. Let’s see how advanced data visualization can illuminate these models and uncover powerful insights. What is a digital twin? Here are the results.

Data Visualization 52

Data Visualization Algorithm Data Wrangling Python 52

Mlearning.ai

FEBRUARY 13, 2023

These databases are designed for fast data retrieval and are ideal for applications that require quick data access and low latency, such as caching, session management, and real-time analytics. Time-Series Databases Time-Series databases are like a chronicle of events, where each event is recorded along with a timestamp ⏲.

Database 52

Database Data Modeling Data Models Big Data 52

Pickl AI

MARCH 19, 2025

Apache Spark Apache Spark is a powerful data processing framework that efficiently handles Big Data. It supports batch processing and real-time streaming, making it a go-to tool for data engineers working with large datasets. Apache Kafka Apache Kafka is a distributed event streaming platform used for real-time data processing.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Cambridge Intelligence

JUNE 18, 2024

We now have a Neo4j instance connected to a working ReGraph application, ready to visualize graphs. The data model Our Sandbox contains a subset of Neo4j-related Stack Overflow questions. There are five node types, connected in the following model: The Stack Overflow data model, created using the arrows.app.

Database 59

Database Data Modeling Data Models Data Visualization 59

Tableau

DECEMBER 1, 2021

Four reference lines on the x-axis indicate key events in Tableau’s almost two-decade history: The first Tableau Conference in 2008. April 2018), which focused on users who do understand joins and curating federated data sources. Visual encoding, in particular, tapped the power of the human visual system. Release v1.0

Tableau 98

Tableau ML ML Database 98

phData

DECEMBER 10, 2024

With a focus on data visualization and behavioral analytics, Ive found Sigmas speed to insight, flexible platform, and intuitive UI to be game-changers for my work. Meet the Team The team leading these events at the Sigma Computing offices in Manhattan was just as impressive as the setting.

Clustering 52

Clustering Analytics Analytics Data Visualization 52

Cambridge Intelligence

OCTOBER 19, 2023

That’s why our data visualization SDKs are database agnostic: so you’re free to choose the right stack for your application. Multi-model databases combine graphs with two other NoSQL data models – document and key-value stores. Transactional, analytical, or both…?

Database 59

Database Azure Analytics Analytics 59

ODSC - Open Data Science

APRIL 26, 2023

These programs allow them to design and build scalable and efficient data pimples that can handle large volumes of data, and ensure that the data is stored in a secure and reliable manner. As you can imagine, data architects require a strong background in database design, data modeling, and data management.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Heartbeat

DECEMBER 27, 2023

These steps include defining business and project objectives, acquiring and exploring data, modeling the data with various algorithms, interpreting and communicating the project outcome, and implementing and maintaining the project. catboost is the machine learning algorithm for model building. Flask==2.1.2 xgboost==1.6.1

Machine Learning 52

Machine Learning Machine Learning Python ML 52