- Feb 25, 2019
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Video: .MP4, 1920x1080 30 fps | Audio: AAC, 48kHz, 2ch | Duration: 4h 43m
Genre: eLearning | Language: English | Size: 1.83 GB
The greatest number of mistakes and failures in data analysis comes from not performing adequate Exploratory Data Analysis (EDA).
Harness the skills to analyze your data effectively with EDA and R Key Features
Explore the most popular and advanced R package to place you on the cutting-edge of technology
Learn what you need to do when you see your data for the first time, through to when you make it ready for modeling activities, and pitfalls to avoid along the way
Includes data and code to practice every concept introduced within the course, to consolidate your learning and turn EDA into a habit
What You Will Learn
Set up your data and code to avoid mistakes and ensure reproducibility
Really understand the structure and content of your data
Build clear plots to evaluate the distribution of your data with ggplot
Construct summaries of your variables with dplyr
Implement data cleaning and validation tasks to get your data ready for data mining activities
Test a hypothesis or check assumptions related to a specific model
Estimate parameters and figure the margins of error
Lack of EDA knowledge can expose you to the great risk of drawing incorrect, and potentially harmful, conclusions from your data analysis.
In this course, you will learn how EDA helps you draw conclusions to make better sense of your data and implement correct techniques. We'll begin with a brief introduction to EDA, its importance, and advantages over BI tools. Using R libraries like dplyr and ggplot2, we will generate insights and formulate relevant questions for investigation and communicate the results effectively using visualizations. You will learn how to spot missing data and errors, validate assumptions, and identify the patterns for understanding the problem. Based on this, you'll be able to select a correct ML model to use for your data.
By the end of the course, you will be able to quickly get know and interpret various kinds of data sets you will be presented with, and easily understand how to handle and work with them in order to make them ready for further modeling activities.