Exploratory Data Analysis In Data Science & AI

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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

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