Dataconomy

MARCH 4, 2025

By utilizing algorithms and statistical models, data mining transforms raw data into actionable insights. The data mining process The data mining process is structured into four primary stages: data gathering, data preparation, data mining, and data analysis and interpretation.

Data Mining 91

Data Mining Data Mining Data Mining Decision Trees 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

Augmented analytics is revolutionizing how organizations interact with their data. By harnessing the power of machine learning (ML) and natural language processing (NLP), businesses can streamline their data analysis processes and make more informed decisions. What is augmented analytics?

Augmented Analytics 91

Augmented Analytics Analytics Analytics Natural Language Processing 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

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

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

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

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 337

Power BI Data Pipeline Data Warehouse Data Engineer 337

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

Data Science Dojo

AUGUST 16, 2024

Generative Visualizations : The AI generates appropriate visualizations based on the user’s query, automatically selecting the best chart types, layouts, and data representations to convey the requested insights. This capability automates much of the manual work traditionally involved in data analytics.

Analytics 195

Analytics Analytics Power BI AI 195

Pickl AI

APRIL 21, 2025

Introduction Data visualization is no longer just a niche skill; it’s a fundamental component of Data Analysis , business intelligence, and data science. Q1: What is data visualization, and why is it important in Data Analysis? The approach depends on the context and the amount of missing data.

Data Visualization 52

Data Visualization Power BI Data Analysis Data Analysis 52

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

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 Quality 130

Data Quality Data Pipeline 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

AWS Machine Learning Blog

MARCH 29, 2023

Exploratory data analysis After you import your data, Canvas allows you to explore and analyze it, before building predictive models. You can preview your imported data and visualize the distribution of different features. This information can be used to refine your input data and drive more accurate models.

Machine Learning 97

Machine Learning Machine Learning ML ML 97

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 AI 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

AWS Machine Learning Blog

SEPTEMBER 12, 2023

Email classification project diagram The workflow consists of the following components: Model experimentation – Data scientists use Amazon SageMaker Studio to carry out the first steps in the data science lifecycle: exploratory data analysis (EDA), data cleaning and preparation, and building prototype models.

Data Science 128

Data Science Data Scientist AWS ML 128

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

Tableau

APRIL 12, 2022

introduces a wide range of capabilities designed to improve every stage of data analysis—from data preparation to dashboard consumption. In the case of a failed run, backup flows can be set up to ensure that data is refreshed efficiently, without the need to over-schedule flow runs. Bronwen Boyd. April 13, 2022.

Tableau 98

Tableau Data Preparation Data Modeling Data Models 98

Tableau

APRIL 12, 2022

introduces a wide range of capabilities designed to improve every stage of data analysis—from data preparation to dashboard consumption. In the case of a failed run, backup flows can be set up to ensure that data is refreshed efficiently, without the need to over-schedule flow runs. Bronwen Boyd. April 13, 2022.

Tableau 98

Tableau Data Preparation Data Modeling Data Models 98

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 101

AWS ML ML Python 101