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Performing EDA of Netflix Dataset with Plotly

Analytics Vidhya

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. All you need to know is Plotly for visualization! The post Performing EDA of Netflix Dataset with Plotly appeared first on Analytics Vidhya.

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Mastering Exploratory Data Analysis (EDA): A comprehensive guide

Data Science Dojo

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. So, without any further ado let’s dive right in. DSD got you covered!

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Data Workflows in Football Analytics: From Questions to Insights

Data Science Dojo

This is where a data workflow is essential, allowing you to turn your raw data into actionable insights. In this article, well explore how that workflow covering aspects from data collection to data visualizations can tackle the real-world challenges. Do not be afraid to dive deep and explore other techniques.

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Data Analysis vs. Data Visualization – More Than Just Pretty Charts

Pickl AI

Summary: Data Analysis focuses on extracting meaningful insights from raw data using statistical and analytical methods, while data visualization transforms these insights into visual formats like graphs and charts for better comprehension. EDA: Calculate overall churn rate. Its about storytelling with data.

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Life of modern-day alchemists: What does a data scientist do?

Dataconomy

Imagine data scientists as modern-day detectives who sift through a sea of information to uncover hidden patterns, trends, and correlations that can inform decision-making and drive innovation. Just like sifting through ancient artifacts, they meticulously clean and refine the data, preparing it for the grand unveiling.

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ML | Data Preprocessing in Python

Pickl AI

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. Matplotlib/Seaborn: For data visualization.

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Understanding Data Science and Data Analysis Life Cycle

Pickl AI

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. Data Cleaning Data cleaning is crucial for data integrity.