Exploratory Data Analysis and Visualization

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About Course

Welcome to the course on data analysis! In this course, you will learn the fundamental concepts and techniques of data analysis, as well as how to apply them using various tools and software.

The field of data analysis has grown rapidly in recent years due to the increasing availability of data from various sources such as social media, online transactions, and sensor networks. The ability to extract insights and make informed decisions from this data is essential for individuals and organizations in various industries, such as finance, healthcare, marketing, and more.

Throughout this course, you will learn how to clean, transform, and explore data using various tools and programming languages such as Python, R and SQL. You will also learn how to visualize data to uncover patterns and trends, and how to use statistical methods and machine learning algorithms to make predictions and draw conclusions from the data.

By the end of this course, you will have a solid understanding of data analysis and be able to use the tools and techniques you’ve learned to analyze and make sense of real-world data. You will also have the opportunity to apply your new skills by completing a final project.

We will be covering variety of data wrangling, visualization and statistical/machine learning techniques to work with data and generating insights out of it. This course also focus on the application of data analysis in real world scenarios. By the end of this course you will be able to extract insights from data sets and be able to communicate your findings effectively.

Let’s dive in and start learning about data analysis together!

 

 

 

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What Will You Learn?

  • Understanding of the fundamental concepts and techniques of data analysis, including data cleaning, exploration, and visualization.
  • Ability to apply statistical models and machine learning algorithms to analyze and make predictions from data.
  • Knowledge of ethical and legal considerations in data analysis, including issues of privacy and bias.
  • Ability to communicate and present data analysis results effectively to a non-technical audience, and to use data to inform decision making and drive business results.

Course Content

Introduction to Data Analysis

  • Course Overview
  • What is data analysis and why is it important
  • Types of data and data sources
  • Basic concepts and terminology in data analysis
  • Quiz 1

Exploratory Data Analysis

Statistical Modeling

Data Visualization

Data Wrangling

Machine Learning

Big Data and Cloud Computing

Applications of Data Analysis

Conclusion

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