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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. To put it simply, the goal of EDA is to discover underlying patterns, structures, and trends in the datasets and drive meaningful insights from them that would help in driving important business decisions. DSD got you covered!

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The ultimate guide to the Machine Learning Model Deployment

Data Science Dojo

For data scrapping a variety of sources, such as online databases, sensor data, or social media. Cleaning data: Once the data has been gathered, it needs to be cleaned. This involves removing any errors or inconsistencies in the data.

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Data Workflows in Football Analytics: From Questions to Insights

Data Science Dojo

You may combine event data (e.g., shot types and results) with tracking data (e.g., Effective data collection ensures you have all the necessary information to begin the analysis, setting the stage for reliable insights into improving shot conversion rates or any other defined problem.

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

Dataconomy

Today’s question is, “What does a data scientist do.” ” Step into the realm of data science, where numbers dance like fireflies and patterns emerge from the chaos of information. In this blog post, we’re embarking on a thrilling expedition to demystify the enigmatic role of data scientists.

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Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

Summary: Big Data refers to the vast volumes of structured and unstructured data generated at high speed, requiring specialized tools for storage and processing. Data Science, on the other hand, uses scientific methods and algorithms to analyses this data, extract insights, and inform decisions.

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10 Common Mistakes That Every Data Analyst Make

Pickl AI

A data analyst deals with a vast amount of information daily. Continuously working with data can sometimes lead to a mistake. In this article, we will be exploring 10 such common mistakes that every data analyst makes. Working with inaccurate or poor quality data may result in flawed outcomes.

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Understanding Data Science and Data Analysis Life Cycle

Pickl AI

These figures underscore the significance of comprehending data methodologies for anyone navigating the digital landscape. Understanding Data Science Data Science involves analysing and interpreting complex data sets to uncover valuable insights that can inform decision-making and solve real-world problems.