Data Science Dojo

JANUARY 22, 2023

In this blog, we will discuss exploratory data analysis, also known as EDA, and why it is important. We will also be sharing code snippets so you can try out different analysis techniques yourself. This can be useful for identifying patterns and trends in the data. So, without any further ado let’s dive right in.

Exploratory Data Analysis 370

Exploratory Data Analysis EDA Data Analysis Data Analysis 370

Analytics Vidhya

JUNE 18, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Pandas Pandas is an open-source data analysis and data manipulation library. The post Data Manipulation Using Pandas | Essential Functionalities of Pandas you need to know! appeared first on Analytics Vidhya.

Data Science 271

Data Science Data Analysis Data Analysis Analytics 271

Pickl AI

MARCH 12, 2023

Accordingly, Data Analysts use various tools for Data Analysis and Excel is one of the most common. Significantly, the use of Excel in Data Analysis is beneficial in keeping records of data over time and enabling data visualization effectively. What is Data Analysis?

Data Analysis 52

Data Analysis Data Analysis Data Analyst Power BI 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

Pickl AI

JUNE 6, 2024

Summary: Data scrubbing is identifying and removing inconsistencies, errors, and irregularities from a dataset. It ensures your data is accurate, consistent, and reliable – the cornerstone for effective data analysis and decision-making. Overview Did you know that dirty data costs businesses in the US an estimated $3.1

Clean Data 40

Clean Data Machine Learning Machine Learning Algorithm 40

Mlearning.ai

JANUARY 26, 2023

It can be gradually “enriched” so the typical hierarchy of data is thus: Raw data ↓ Cleaned data ↓ Analysis-ready data ↓ Decision-ready data ↓ Decisions. For example, vector maps of roads of an area coming from different sources is the raw data.

AWS 98

AWS Database Data Science Clean Data 98