Flipboard

MARCH 22, 2023

Data Wrangler simplifies the data preparation and feature engineering process, reducing the time it takes from weeks to minutes by providing a single visual interface for data scientists to select and clean data, create features, and automate data preparation in ML workflows without writing any code.

AWS 123

AWS Data Preparation Azure Data Scientist 123

Pickl AI

JULY 12, 2023

The data professionals deploy different techniques and operations to derive valuable information from the raw and unstructured data. The objective is to enhance the data quality and prepare the data sets for the analysis. What is Data Manipulation?

Data Analysis 52

Data Analysis Data Analysis Database Clean Data 52

Pickl AI

JULY 25, 2024

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. Files: Data stored in flat files, CSVs, or Excel sheets.

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

Pickl AI

SEPTEMBER 12, 2024

Summary: This comprehensive guide explores data standardization, covering its key concepts, benefits, challenges, best practices, real-world applications, and future trends. By understanding the importance of consistent data formats, organizations can improve data quality, enable collaborative research, and make more informed decisions.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Tableau

JANUARY 27, 2021

We also reached some incredible milestones with Tableau Prep, our easy-to-use, visual, self-service data prep product. In 2020, we added the ability to write to external databases so you can use clean data anywhere. Tableau Prep can now be used across more use cases and directly in the browser.

Tableau 102

Tableau Business Intelligence Business Intelligence Analytics 102

Pickl AI

OCTOBER 6, 2024

By employing ETL, businesses ensure that their data is reliable, accurate, and ready for analysis. This process is essential in environments where data originates from various systems, such as databases , applications, and web services. The key is to ensure that all relevant data is captured for further processing.

ETL 52

ETL Data Warehouse Data Quality Data Lakes 52

Pickl AI

JULY 8, 2024

Organisations leverage diverse methods to gather data, including: Direct Data Capture: Real-time collection from sensors, devices, or web services. Database Extraction: Retrieval from structured databases using query languages like SQL. Aggregation: Summarising data into meaningful metrics or aggregates.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Pickl AI

MAY 30, 2024

Overview of Typical Tasks and Responsibilities in Data Science As a Data Scientist, your daily tasks and responsibilities will encompass many activities. You will collect and clean data from multiple sources, ensuring it is suitable for analysis. Sources of Data Data can come from multiple sources.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

DECEMBER 3, 2024

Summary: Data preprocessing in Python is essential for transforming raw data into a clean, structured format suitable for analysis. It involves steps like handling missing values, normalizing data, and managing categorical features, ultimately enhancing model performance and ensuring data quality.

Python 52

Python ML ML Exploratory Data Analysis 52

Pickl AI

JUNE 6, 2024

Data scrubbing is often used interchangeably but there’s a subtle difference. Cleaning is broader, improving data quality. This is a more intensive technique within data cleaning, focusing on identifying and correcting errors. Data scrubbing is a powerful tool within this cleaning service.

Clean Data 40

Clean Data Machine Learning Machine Learning Algorithm 40

Dataconomy

JULY 28, 2023

Data scientists must decide on appropriate strategies to handle missing values, such as imputation with mean or median values or removing instances with missing data. The choice of approach depends on the impact of missing data on the overall dataset and the specific analysis or model being used.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

NOVEMBER 14, 2023

So, let me present to you an Importing Data in Python Cheat Sheet which will make your life easier. For initiating any data science project, first, you need to analyze the data. In this Importing Data in Python Cheat Sheet article, we will explore the essential techniques and libraries that will make data import a breeze.

Python 52

Python SQL Database Data Analysis 52

DagsHub

OCTOBER 23, 2024

There are 5 stages in unstructured data management: Data collection Data integration Data cleaning Data annotation and labeling Data preprocessing Data Collection The first stage in the unstructured data management workflow is data collection. We get your data RAG-ready.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Tableau

JANUARY 27, 2021

We also reached some incredible milestones with Tableau Prep, our easy-to-use, visual, self-service data prep product. In 2020, we added the ability to write to external databases so you can use clean data anywhere. Tableau Prep can now be used across more use cases and directly in the browser.

Tableau 52

Tableau Business Intelligence Business Intelligence Analytics 52

Pickl AI

MAY 15, 2023

With the help of data pre-processing in Machine Learning, businesses are able to improve operational efficiency. Following are the reasons that can state that Data pre-processing is important in machine learning: Data Quality: Data pre-processing helps in improving the quality of data by handling the missing values, noisy data and outliers.

Machine Learning 52

Machine Learning Machine Learning Data Analysis Data Analysis 52

Pickl AI

DECEMBER 16, 2024

This step includes: Identifying Data Sources: Determine where data will be sourced from (e.g., databases, APIs, CSV files). Ensuring Time Consistency: Ensure that the data is organized chronologically, as time order is crucial for time series analysis. Making Data Stationary: Many forecasting models assume stationarity.

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

phData

NOVEMBER 13, 2023

Sidebar Navigation: Provides a catalog sidebar for browsing resources by type, package, file tree, or database schema, reflecting the structure of both dbt projects and the data platform. Deploy jobs are designed to build into production databases, running sequentially to prevent conflicts.

Database 52

Database Business Intelligence Business Intelligence Data Silos 52

Pickl AI

DECEMBER 3, 2024

Pandas are widely use for handling missing data and cleaning data frames, while Scikit-learn provides tools for normalisation and encoding. NumPy and SciPy can also help apply statistical methods for data imputation and feature transformation. Frequently Asked Questions What is the UCI Machine Learning Repository?

Machine Learning 52

Machine Learning Machine Learning Clustering Supervised Learning 52

Snorkel AI

APRIL 28, 2023

It’s about how to draw and analyze data quality and machine learning quality, which is actually very related to this current trend of data-centric AI. You could have a missing value, you could have a wrong value, and you have a whole bunch of those data examples. We’d like to bring them together.

ML 52

ML ML Machine Learning Machine Learning 52

Snorkel AI

APRIL 28, 2023

It’s about how to draw and analyze data quality and machine learning quality, which is actually very related to this current trend of data-centric AI. You could have a missing value, you could have a wrong value, and you have a whole bunch of those data examples. We’d like to bring them together.

ML 52

ML ML Machine Learning Machine Learning 52

Women in Big Data

JANUARY 25, 2025

AI Agents and GenAI: Enhancing Data Quality and Compliance Dr. Martin Manhem bu , GovTech Founder & Professor, shared insights on how AI agents can transform data acquisition, management, and governance. Key Points: Data Acquisition: Automated data collection from APIs, IoT devices, and databases.

Data Governance 52

Data Governance Big Data Big Data Data Quality 52