Exploratory Data Analysis In Data Science & AI

如果没有适当的 EDA，机器学习工作会遇到准确性问题，而且很多时候，算法将无法工作。什么是探索性数据分析？数据科学家使用探索性数据分析 (EDA) 来分析和调查数据集并总结其主要特征，通常采用数据可视化方法。

设置上下文

在开始机器学习项目之前，确保数据已准备好进行建模工作非常重要。探索性数据分析 (EDA) 确保数据准备好用于机器学习。EDA 确保数据更有用。如果没有适当的 EDA，机器学习工作会遇到准确性问题，而且很多时候，算法将无法工作。

什么是探索性数据分析？

数据科学家使用探索性数据分析 (EDA) 来分析和调查数据集并总结其主要特征，通常采用数据可视化方法。它有助于确定如何最好地操纵数据源以获得所需的答案，从而使数据科学家更容易发现模式、发现异常、检验假设或检查假设。

EDA 主要用于查看在正式建模或假设检验任务之外可以揭示哪些数据，并提供对数据集变量及其之间关系的更好理解。它还可以帮助确定您正在考虑用于数据分析的统计技术是否合适。

为什么探索性数据分析在数据科学中很重要？

EDA 的主要目的是帮助在做出任何假设之前查看数据。它可以帮助识别明显的错误，更好地理解数据中的模式，检测异常值或异常事件，并找到变量之间有趣的关系。

数据科学家可以使用探索性分析来确保他们产生的结果是有效的并且适用于任何期望的业务成果和目标。EDA 还通过确认他们提出正确的问题来帮助利益相关者。EDA 可以帮助回答有关标准差、分类变量和置信区间的问题。

使用的编程语言

Python：一种具有动态语义的解释型、面向对象的编程语言。它的高级内置数据结构与动态类型和动态绑定相结合，使其对于快速应用程序开发以及用作连接现有组件的脚本或胶水语言非常有吸引力。Python 和 EDA 可以一起使用来识别数据集中的缺失值，这很重要，因此您可以决定如何处理机器学习中的缺失值。

本课程涵盖哪些内容？

本课程将教您探索性数据分析的技术和方法，帮助您从数据中获得最大价值。如果您在没有执行此 EDA 的情况下直接进入机器学习，那么除了以较低的准确度结束之外，您还将面临失败。本课程由 AI 和技术资深人士设计，直接从烤箱中传授给您！

Industry Relevant knowledge of EDA is the key for the intermediate as well as expert level Enthusiasts

Published 10/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 8 lectures (1h 20m) | Size: 1.2 GB

What you’ll learn

Exploratory Data Analysis Mindmap and Concept, Theory as well as Practical
EDA in Machine Learning
EDA in Data Science & AI
Hands on tutorial on industry relevant skills and also the theory and concepts clarity

Requirements
Basic Knowledge of Python Programming Language, Excel and Machine Learning

Description

Come on, Enthusiastic Candidates, boom your knowledge and brush up your skills

Without a proper EDA, Machine Learning work suffers from accuracy issues and many times, the algorithms won’t work. What is exploratory data analysis? Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods.

Setting the context

Before you start a machine learning project, it’s important to ensure that the data is ready for modeling work. Exploratory Data Analysis (EDA) ensures the readiness of the data for Machine Learning. EDA ensures that the data is more usable. Without a proper EDA, Machine Learning work suffers from accuracy issues and many times, the algorithms won’t work.

What is exploratory data analysis?

Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions.

EDA is primarily used to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate.

Why is exploratory data analysis important in data science?

The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, and find interesting relations among the variables.

Data scientists can use exploratory analysis to ensure the results they produce are valid and applicable to any desired business outcomes and goals. EDA also helps stakeholders by confirming they are asking the right questions. EDA can help answer questions about standard deviations, categorical variables, and confidence intervals.

Programming Language Used

Python: an interpreted, object-oriented programming language with dynamic semantics. Its high-level, built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for rapid application development, as well as for use as a scripting or glue language to connect existing components. Python and EDA can be used together to identify missing values in a data set, which is important so you can decide how to handle missing values for machine learning.

What is covered in this course?

This course will teach you the techniques and approaches in exploratory data analysis, which will help you to derive maximum value from the data. If you jump into machine learning without doing this EDA, you are setting yourself up for failure besides ending up with lower accuracy. This course is designed by an AI and tech veteran and comes to you straight from the oven!

Who this course is for
Beginner
Intermediate
Expert
Students
Professionals
Python Developer
Data Scientists
Software Developers