Automating Data Exploration with R

Automating Data Exploration with R, available at $22.99, has an average rating of 4.9, with 21 lectures, based on 183 reviews, and has 1361 subscribers.

You will learn about Build a pipeline to automate the processing of raw data for discovery and modeling Know the main steps to prepare data for modeling Know how to handle the different data types in R Understand data imputation Treat categorical data properly with binarization (making dummy columns) Apply feature engineering to dates, integers and real numbers Apply variable selection, correlation and significance tests Model and measure prepared data using both supervised and unsupervised modeling This course is ideal for individuals who are Interest and need to process raw data for exploration and modeling in R It is particularly useful for Interest and need to process raw data for exploration and modeling in R.

Enroll now: Automating Data Exploration with R

Summary

Title: Automating Data Exploration with R

Price: $22.99

Average Rating: 4.9

Number of Lectures: 21

Number of Published Lectures: 21

Number of Curriculum Items: 21

Number of Published Curriculum Objects: 21

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Build a pipeline to automate the processing of raw data for discovery and modeling
Know the main steps to prepare data for modeling
Know how to handle the different data types in R
Understand data imputation
Treat categorical data properly with binarization (making dummy columns)
Apply feature engineering to dates, integers and real numbers
Apply variable selection, correlation and significance tests
Model and measure prepared data using both supervised and unsupervised modeling

Who Should Attend

Interest and need to process raw data for exploration and modeling in R

Target Audiences

Interest and need to process raw data for exploration and modeling in R

As data scientists and analysts we face constant repetitive task when approaching new data sets. This class aims at automating a lot of these tasks in order to get to the actual analysis as quickly as possible. Of course, there will always be exceptions to the rule, some manual work and customization will be required. But overall a large swath of that work can be automated by building a smart pipeline. This is what we’ll do here. This is especially important in the era of big data where handling variables by hand isn’t always possible.

It is also a great learning strategy to think in terms of a processing pipeline and to understand, design and build each stage as separate and independent units.

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Covered in this Class

Lecture 2: Big Picture – Data Scrubbing

Lecture 3: Optional – Getting RStudio

Chapter 2: Reading Data

Lecture 1: Common Data Readers

Chapter 3: Data Transformation – Data Scrubbing

Lecture 1: Dates – Reading and Casting Dates

Lecture 2: Text Data – Ways to Quantify Free-Form Text

Lecture 3: Text Data – Categories

Lecture 4: Text Data – Categories 2 & Pipeline Check

Lecture 5: Imputing Data – Dealing with Missing Data

Lecture 6: Pipeline Check

Lecture 7: Caret Library – nearZeroVar

Chapter 4: Feature Engineering

Lecture 1: Engineering Dates – Getting Additional Features out of Dates

Lecture 2: Numerical Engineering – Integers and Real Numbers

Lecture 3: Pipeline Check

Chapter 5: Basic Data Exploration

Lecture 1: Correlations

Lecture 2: Caret Library – findCorrelation

Lecture 3: Hunting Outliers

Chapter 6: Modeling

Lecture 1: Random Forest – Titanic Data Set

Lecture 2: GBM (Generalized Boosted Models)/Caret – Diabetes Data Set – 1

Lecture 3: GBM – 2

Lecture 4: K-means, Unstructured Modeling

Instructors

Manuel Amunategui
Data Scientist & Quantitative Developer

Rating Distribution

1 stars: 1 votes
2 stars: 3 votes
3 stars: 11 votes
4 stars: 63 votes
5 stars: 105 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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