Dataconomy

MARCH 4, 2025

Synthetic data refers to artificially generated data that mirrors the statistical patterns and structures of real datasets without disclosing sensitive information about individuals. Importance of synthetic data The significance of synthetic data lies in its ability to address critical challenges in data handling and analysis.

Decision Trees 91

Decision Trees Machine Learning Machine Learning Deep Learning 91

Data Science Dojo

MARCH 7, 2023

This includes sourcing, gathering, arranging, processing, and modeling data, as well as being able to analyze large volumes of structured or unstructured data. The goal of data preparation is to present data in the best forms for decision-making and problem-solving.

Data Scientist 221

Data Scientist Exploratory Data Analysis Data Science Data Visualization 221

Dataconomy

MARCH 17, 2025

By analyzing data from IoT devices, organizations can perform maintenance tasks proactively, reducing downtime and operational costs. Data preparation Data preparation is a crucial step that includes data cleaning, transforming, and structuring historical data for analysis.

Decision Trees 91

Decision Trees Predictive Analytics Data Preparation Machine Learning 91

Data Science Dojo

FEBRUARY 9, 2023

It allows users to connect to a variety of data sources, perform data preparation and transformations, create interactive visualizations, and share insights with others. The platform includes features such as data modeling, data discovery, data analysis, and interactive dashboards.

Power BI 221

Power BI Data Analyst Data Visualization Data Analysis 221

Data Science Dojo

JUNE 7, 2023

Data Science is a field that encompasses various disciplines, including statistics, machine learning, and data analysis techniques to extract valuable insights and knowledge from data. 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

Data Science Dojo

AUGUST 28, 2023

Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. Exploratory Data Analysis (EDA) Data collection: The first step in LLMOps is to collect the data that will be used to train the LLM.

Exploratory Data Analysis 195

Exploratory Data Analysis Data Preparation Machine Learning Machine Learning 195

Pickl AI

MAY 30, 2024

Summary: The Data Science and Data Analysis life cycles are systematic processes crucial for uncovering insights from raw data. Quality data is foundational for accurate analysis, ensuring businesses stay competitive in the digital landscape. billion INR by 2026, with a CAGR of 27.7%.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Mlearning.ai

MARCH 5, 2023

Photo by Joshua Sortino on Unsplash Data analysis is an essential part of any research or business project. Before conducting any formal statistical analysis, it’s important to conduct exploratory data analysis (EDA) to better understand the data and identify any patterns or relationships.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Analysis Data Analysis EDA 52

IBM Data Science in Practice

JANUARY 2, 2025

Building on the foundation of data fabric and SQL assets discussed in Enhancing Data Fabric with SQL Assets in IBM Knowledge Catalog , this blog explores how organizations can leverage automated microsegment creation to streamline data analysis. For this example, choose MaritalStatus.

SQL 100

SQL Data Quality Data Profiling Data Preparation 100

IBM Journey to AI blog

OCTOBER 23, 2024

With data visualization capabilities, advanced statistical analysis methods and modeling techniques, IBM SPSS Statistics enables users to pursue a comprehensive analytical journey from data preparation and management to analysis and reporting. How to integrate SPSS Statistics with R and Python?

Python 87

Python Data Analysis Data Analysis Data Science 87

Alation

FEBRUARY 20, 2020

On August 25 at 11am PDT, Forrester’s VP and Research Director, Gene Leganza, Alation’s Head of Product, Aaron Kalb, and Trifacta’s Director of Product Marketing, Will Davis, will hold a webinar to discuss “Achieving Productivity with Self-Service Data Preparation.” Get the latest data cataloging news and trends in your inbox.

Data Analysis 52

Data Analysis Data Analysis Data Wrangling Data Preparation 52

Data Science Dojo

SEPTEMBER 11, 2024

In the sales context, this ensures that sales data remains consistent, accurate, and easily accessible for analysis and reporting. Create Workspace: To work with data in Fabric, first create a workspace with the Fabric trial enabled.

Power BI 261

Power BI Data Pipeline Data Warehouse Data Engineering 261

IBM Data Science in Practice

DECEMBER 7, 2022

Studies have shown that 80% of time is spent on data preparation and cleansing, leaving only 20% of time for data analytics. Thus, the earlier in the process that data is cleansed and curated, the more time data consumers can reduce in data preparation and cleansing.

Data Pipeline 130

Data Pipeline Data Quality Data Preparation ETL 130

Smart Data Collective

MAY 13, 2022

It makes data preparation faster. Preparing data for analysis is time-consuming if you do it manually. Using AI-driven analytics can automate the process by collecting, extracting, and loading the appropriate data for analysis. Of course, challenges with data analysis will always be there.

Analytics 141

Analytics Analytics AI AI 141

IBM Journey to AI blog

DECEMBER 7, 2023

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. Organizations can expect to reap the following benefits from implementing OLAP solutions, including the following.

Data Preparation 72

Data Preparation Database Data Analysis Data Analysis 72

IBM Data Science in Practice

NOVEMBER 28, 2022

Studies have shown that 80% of time is spent on data preparation and cleansing, leaving only 20% of time for data analytics. Thus, the earlier in the process that data is cleansed and curated, the more time data consumers can reduce in data preparation and cleansing.

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

Tableau

JUNE 11, 2024

Tableau+ includes: Einstein Copilot for Tableau (only in Tableau+) : Get an intelligent assistant that helps make Tableau easier and analysts more efficient across the platform: In Tableau Prep (coming in 2024.2) : Automate formula creation and speed up data preparation.

Tableau 127

Tableau AI AI Analytics 127

Tableau

JULY 6, 2022

release includes features that speed up and streamline your data preparation and analysis. Automate dashboard insights with Data Stories. If you've ever written an executive summary of a dashboard, you know it’s time consuming to distill the “so what” of the data. Product Marketing Associate, Tableau.

Tableau 98

Tableau Data Preparation Data Analysis Data Analysis 98

Tableau

JULY 6, 2022

release includes features that speed up and streamline your data preparation and analysis. Automate dashboard insights with Data Stories. If you've ever written an executive summary of a dashboard, you know it’s time consuming to distill the “so what” of the data. Product Marketing Associate, Tableau.

Tableau 98

Tableau Data Preparation Data Analysis Data Analysis 98

Dataconomy

JULY 28, 2023

Proper data preprocessing is essential as it greatly impacts the model performance and the overall success of data analysis tasks ( Image Credit ) Data integration Data integration involves combining data from various sources and formats into a unified and consistent dataset.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Alation

FEBRUARY 20, 2020

As data cataloging has matured and gone mainstream the diversity of data catalogs has expanded, with data catalogs embedded in many data preparation and data analysis tools. The embedded data catalogs offer some advantages of technology integration.

Data Preparation 52

Data Preparation Data Analysis Data Analysis 52

Flipboard

MARCH 22, 2023

Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. You can import data from multiple data sources, such as Amazon Simple Storage Service (Amazon S3), Amazon Athena , Amazon Redshift , Amazon EMR , and Snowflake.

AWS 123

AWS Data Preparation Azure Data Scientist 123

Data Science Dojo

JANUARY 12, 2023

Users: data scientists vs business professionals People who are not used to working with raw data frequently find it challenging to explore data lakes. To comprehend and transform raw, unstructured data for any specific business use, it typically takes a data scientist and specialized tools.

Data Lakes 221

Data Lakes Data Warehouse Hadoop Machine Learning 221

Towards AI

FEBRUARY 9, 2024

I am most often prompting this LLM for data visualization code and on-the-fly-visuals because it does all these steps very efficiently. GPT-4 automates the tedious process of data preparation and visualization, which traditionally requires extensive coding and debugging. This saves me a massive amount of time and effort.

Data Visualization 105

Data Visualization Data Preparation AI AI 105

Pickl AI

SEPTEMBER 26, 2023

These methods are particularly useful in naturalistic or controlled settings to gather objective data. Analyzing and Interpreting Sampled Data Data preparation and cleaning Before analysis, sampled data need to undergo cleansing and preparation. How can sampling errors impact data analysis results?

Analytics 98

Analytics Analytics Clustering Data Analysis 98

Flipboard

DECEMBER 11, 2024

SageMaker Unied Studio is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment.

SQL 160

SQL AWS Data Lakes ML 160

Towards AI

JUNE 27, 2023

Offering features like TensorBoard for data visualization and TensorFlow Extended (TFX) for implementing production-ready ML pipelines, TensorFlow stands out as a comprehensive solution for both beginners and seasoned professionals in the realm of machine learning.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

Alation

SEPTEMBER 23, 2021

There are four main data catalog types that offer different functions based on the needs of your enterprise: Standalone – A standalone data catalog allows for the cataloging of data sets and operations, data set search, evaluation, and requires a high level of interoperability for a seamless user experience.

Data Preparation 52

Data Preparation SQL Data Governance Data Analysis 52

AWS Machine Learning Blog

APRIL 17, 2023

In other words, companies need to move from a model-centric approach to a data-centric approach.” – Andrew Ng A data-centric AI approach involves building AI systems with quality data involving data preparation and feature engineering. Custom transforms can be written as separate steps within Data Wrangler.

AWS 100

AWS ML ML Python 100

Pickl AI

OCTOBER 17, 2023

Learn how Data Scientists use ChatGPT, a potent OpenAI language model, to improve their operations. ChatGPT is essential in the domains of natural language processing, modeling, data analysis, data cleaning, and data visualization. It facilitates exploratory Data Analysis and provides quick insights.

Data Scientist 52

Data Scientist Machine Learning Machine Learning Data Science 52

AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

ML 94

ML ML Database AWS 94

Pickl AI

SEPTEMBER 10, 2024

By integrating AI capabilities, Excel can now automate Data Analysis, generate insights, and even create visualisations with minimal human intervention. AI-powered features in Excel enable users to make data-driven decisions more efficiently, saving time and effort while uncovering valuable insights hidden within large datasets.

Power BI 52

Power BI Data Analysis Data Analysis AI 52

DataRobot Blog

FEBRUARY 28, 2023

This includes: Supporting Snowflake External OAuth configuration Leveraging Snowpark for exploratory data analysis with DataRobot-hosted Notebooks and model scoring. Exploratory Data Analysis After we connect to Snowflake, we can start our ML experiment. Learn more about Snowflake External OAuth.

Exploratory Data Analysis 72

Exploratory Data Analysis ML ML AI 72

AWS Machine Learning Blog

JULY 31, 2023

Data preparation, feature engineering, and feature impact analysis are techniques that are essential to model building. These activities play a crucial role in extracting meaningful insights from raw data and improving model performance, leading to more robust and insightful results.

ML 96

ML ML Data Preparation Machine Learning 96

Pickl AI

FEBRUARY 4, 2024

The platform employs an intuitive visual language, Alteryx Designer, streamlining data preparation and analysis. With Alteryx Designer, users can effortlessly input, manipulate, and output data without delving into intricate coding, or with minimal code at most. What is Alteryx Designer?

Data Preparation 52

Data Preparation Tableau Data Visualization Analytics 52

AWS Machine Learning Blog

JUNE 17, 2024

Data preparation and training The data preparation and training pipeline includes the following steps: The training data is read from a PrestoDB instance, and any feature engineering needed is done as part of the SQL queries run in PrestoDB at retrieval time.

ML 122

ML ML AWS Machine Learning 122

Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Virtualization and discovery.

Tableau 102

Tableau Data Quality Analytics Analytics 102