Data Science Dojo

JULY 5, 2023

The following steps are involved in pipeline development: Gathering data: The first step is to gather the data that will be used to train the model. For data scrapping a variety of sources, such as online databases, sensor data, or social media. This involves removing any errors or inconsistencies in the data.

Machine Learning 195

Machine Learning Machine Learning EDA ML 195

Pickl AI

APRIL 21, 2025

Key Takeaways Big Data focuses on collecting, storing, and managing massive datasets. Data Science extracts insights and builds predictive models from processed data. Big Data technologies include Hadoop, Spark, and NoSQL databases. Data Science uses Python, R, and machine learning frameworks.

Big Data 52

Big Data Big Data Data Science Machine Learning 52

Smart Data Collective

OCTOBER 17, 2022

It detaches from the complicated and computes heavy transformations to deliver clean data into lakes and DWHs. . Their data pipelining solution moves the business entity data through the concept of micro-DBs, which makes it the first of its kind successful solution.

Data Pipeline 145

Data Pipeline Data Warehouse ETL Data Lakes 145

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 98

ML ML Database AWS 98

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

Raw data often contains inconsistencies, missing values, and irrelevant features that can adversely affect the performance of Machine Learning models. Proper preprocessing helps in: Improving Model Accuracy: Clean data leads to better predictions. Loading the dataset allows you to begin exploring and manipulating the data.

Python 52

Python ML ML Exploratory Data Analysis 52

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