Data Science Dojo

JANUARY 22, 2023

In this blog, we will discuss exploratory data analysis, also known as EDA, and why it is important. EDA is an iterative process of conglomerative activities which include data cleaning, manipulation and visualization. We will also be sharing code snippets so you can try out different analysis techniques yourself.

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Exploratory Data Analysis EDA Data Analysis Data Analysis 370

Analytics Vidhya

SEPTEMBER 4, 2021

This article was published as a part of the Data Science Blogathon Image 1In this blog, We are going to talk about some of the advanced and most used charts in Plotly while doing analysis. Table of content Description of Dataset Data Exploration Data Cleaning Data visualization […].

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EDA Clean Data Data Visualization Data Science 324

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

Mlearning.ai

APRIL 25, 2023

In today’s blog, we will explore the Netflix dataset using Python and uncover some interesting insights. In this blog, we’ll be using Python to perform exploratory data analysis (EDA) on a Netflix dataset that we’ve found on Kaggle. Let’s explore the dataset further by cleaning data and creating some visualizations.

Data Analysis 52

Data Analysis Data Analysis Python Exploratory Data Analysis 52

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.

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Big Data Big Data Data Science Machine Learning 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. Must Check Out: How to Use ChatGPT APIs in Python: A Comprehensive Guide.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Dataconomy

AUGUST 16, 2023

” The answer: they craft predictive models that illuminate the future ( Image credit ) Data collection and cleaning : Data scientists kick off their journey by embarking on a digital excavation, unearthing raw data from the digital landscape. Interprets data to uncover actionable insights guiding business decisions.

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Data Scientist Data Science Machine Learning Machine Learning 172