Data Science with R (beginner to guru)

Data Science with R (beginner to guru), available at $39.99, has an average rating of 3.85, with 39 lectures, 1 quizzes, based on 80 reviews, and has 16888 subscribers.

You will learn about Data Science using R programming Become a Data Scientist Data Science Learning Path How to learn Data Science Data Collection and Management Model Deployment and Maintenance Setting Expectations Loading Data into R Exploring Data in Data Science and Machine Learning Exploring Data using R Benefits of Data Cleaning Cross Validation in R Data Transformation Modeling Methods Solving Classification Problems Working without Known Targets Evaluating Models Confusion Matrix Introduction to Linear Regression Linear Regression in R Simple and Multiple Regression Linear and Logistic Regression Support Vector Machines (SVM) in R Unsupervised Methods Clustering in Data Science K-means Algorithm in R Hierarchical Clustering Market Basket Analysis MBA and Association Rule Mining Implementing MBA Association Rule Learning Decision Tree Algorithm Exploring Advanced Methods Using Kernel Methods Documentation and Deployment This course is ideal for individuals who are Data Scientists or Anyone aspiring for a career in Data Science and Machine Learning or Machine Learning Engineers or R Programmers or Newbies and Beginners wishing to start their career in R Programming and Data Science or Data Analysts & Advanced Data Analytics Professionals or Software Engineers & Developers or Senior Data Scientists or Chief Technology Officers (CTOs) or Statisticians and Data Science Researchers or Data Engineers or R Programmers Analytics or Senior Data Analysts – R, Python Programming or Data Science Engineers It is particularly useful for Data Scientists or Anyone aspiring for a career in Data Science and Machine Learning or Machine Learning Engineers or R Programmers or Newbies and Beginners wishing to start their career in R Programming and Data Science or Data Analysts & Advanced Data Analytics Professionals or Software Engineers & Developers or Senior Data Scientists or Chief Technology Officers (CTOs) or Statisticians and Data Science Researchers or Data Engineers or R Programmers Analytics or Senior Data Analysts – R, Python Programming or Data Science Engineers.

Enroll now: Data Science with R (beginner to guru)

Summary

Title: Data Science with R (beginner to guru)

Price: $39.99

Average Rating: 3.85

Number of Lectures: 39

Number of Quizzes: 1

Number of Published Lectures: 39

Number of Published Quizzes: 1

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Data Science using R programming
Become a Data Scientist
Data Science Learning Path
How to learn Data Science
Data Collection and Management
Model Deployment and Maintenance
Setting Expectations
Loading Data into R
Exploring Data in Data Science and Machine Learning
Exploring Data using R
Benefits of Data Cleaning
Cross Validation in R
Data Transformation
Modeling Methods
Solving Classification Problems
Working without Known Targets
Evaluating Models
Confusion Matrix
Introduction to Linear Regression
Linear Regression in R
Simple and Multiple Regression
Linear and Logistic Regression
Support Vector Machines (SVM) in R
Unsupervised Methods
Clustering in Data Science
K-means Algorithm in R
Hierarchical Clustering
Market Basket Analysis
MBA and Association Rule Mining
Implementing MBA
Association Rule Learning
Decision Tree Algorithm
Exploring Advanced Methods
Using Kernel Methods
Documentation and Deployment

Who Should Attend

Data Scientists
Anyone aspiring for a career in Data Science and Machine Learning
Machine Learning Engineers
R Programmers
Newbies and Beginners wishing to start their career in R Programming and Data Science
Data Analysts & Advanced Data Analytics Professionals
Software Engineers & Developers
Senior Data Scientists
Chief Technology Officers (CTOs)
Statisticians and Data Science Researchers
Data Engineers
R Programmers Analytics
Senior Data Analysts – R, Python Programming
Data Science Engineers

Target Audiences

Data Scientists
Anyone aspiring for a career in Data Science and Machine Learning
Machine Learning Engineers
R Programmers
Newbies and Beginners wishing to start their career in R Programming and Data Science
Data Analysts & Advanced Data Analytics Professionals
Software Engineers & Developers
Senior Data Scientists
Chief Technology Officers (CTOs)
Statisticians and Data Science Researchers
Data Engineers
R Programmers Analytics
Senior Data Analysts – R, Python Programming
Data Science Engineers

A warm welcome to the Data Science with Rcourse by Uplatz.

Data Science includes various fields such as mathematics, business insight, tools, processes and machine learning techniques. A mix of all these fields help us in discovering the visions or designs from raw data which can be of major use in the formation of big business decisions. As a Data scientist it’s your role to inspect which questions want answering and where to find the related data. A data scientist should have business insight and analytical services. One also needs to have the skill to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.

R is a commanding language used extensively for data analysis and statistical calculating. It was developed in early 90s. R is an open-source software. R is unrestricted and flexible because it’s an open-source software. R’s open lines permit it to incorporate with other applications and systems. Open-source soft wares have a high standard of quality, since multiple people use and iterate on them. As a programming language, R delivers objects, operators and functions that allow employers to discover, model and envision data. Data science with R has got a lot of possibilities in the commercial world. Open R is the most widely used open-source language in analytics. From minor to big initiatives, every other company is preferring R over the other languages. There is a constant need for professionals with having knowledge in data science using R programming.

Uplatzprovides this comprehensive course on Data Science with R covering data science concepts implementation and application using R programming language.

Data Science with R – Course Syllabus

1. Introduction to Data Science

1.1 The data science process
1.2 Stages of a data science project
1.3 Setting expectations
1.4 Summary

2. Loading Data into R

2.1 Working with data from files
2.2 Working with relational databases
2.3 Summary

3. Managing Data

3.1 Cleaning data
3.2 Sampling for modeling and validation
3.3 Summary

4. Choosing and Evaluating Models

4.1 Mapping problems to machine learning tasks
4.2 Evaluating models
4.3 Validating models
4.4 Summary

5. Memorization Methods

5.1 Using decision trees 127
5.2 Summary

6. Linear and Logistic Regression

6.1 Using linear regression
6.2 Using logistic regression
6.3 Summary

7. Unsupervised Methods

7.1 Cluster analysis
7.2 Association rules
7.3 Summary

8. Exploring Advanced Methods

8.1 Using bagging and random forests to reduce training variance
8.2 Using generalized additive models (GAMs) to learn nonmonotone relationships
8.3 Using kernel methods to increase data separation
8.4 Using SVMs to model complicated decision boundaries

9. Documentation and Deployment

9.1 The buzz dataset
9.2 Using knitr to produce milestone documentation

Course Curriculum

Chapter 1: Introduction to Data Science with R

Lecture 1: Introduction to Data Science with R

Chapter 2: Data Science Learning Path

Lecture 1: Data Science Learning Path

Chapter 3: How to learn Data Science

Lecture 1: Part 1 – How to learn Data Science

Lecture 2: Part 2 – How to learn Data Science

Chapter 4: Data Collection and Management

Lecture 1: Data Collection and Management

Chapter 5: Model Deployment and Maintenance

Lecture 1: Model Deployment and Maintenance

Chapter 6: Setting Expectations

Lecture 1: Setting Expectations

Chapter 7: Loading Data into R

Lecture 1: Loading Data into R

Chapter 8: Exploring Data in Data Science and Machine Learning

Lecture 1: Exploring Data in Data Science and Machine Learning

Chapter 9: Exploring Data using R

Lecture 1: Exploring Data using R

Chapter 10: Benefits of Data Cleaning

Lecture 1: Benefits of Data Cleaning

Chapter 11: Cross Validation in R

Lecture 1: Cross Validation in R

Chapter 12: Data Transformation

Lecture 1: Data Transformation

Chapter 13: Modeling Methods

Lecture 1: Modeling Methods

Chapter 14: Solving Classification Problems

Lecture 1: Solving Classification Problems

Chapter 15: Working without known Targets

Lecture 1: Working without known Targets

Chapter 16: Evaluating Models

Lecture 1: Evaluating Models

Chapter 17: Confusion Matrix

Lecture 1: Confusion Matrix

Chapter 18: Introduction to Linear Regression

Lecture 1: Introduction to Linear Regression

Chapter 19: Linear Regression in R

Lecture 1: Part 1 – Linear Regression in R

Lecture 2: Part 2 – Linear Regression in R

Chapter 20: Simple and Multiple Regression

Lecture 1: Simple and Multiple Regression

Chapter 21: Linear and Logistic Regression

Lecture 1: Linear and Logistic Regression

Chapter 22: Support Vector Machines (SVM) in R

Lecture 1: Part 1 – Support Vector Machines (SVM) in R

Lecture 2: Part 2 – Support Vector Machines (SVM) in R

Chapter 23: Unsupervised Methods

Lecture 1: Unsupervised Methods

Chapter 24: Clustering in Data Science

Lecture 1: Clustering in Data Science

Chapter 25: K-means Algorithm in R

Lecture 1: K-means Algorithm in R

Chapter 26: Hierarchical Clustering

Lecture 1: Part 1 – Hierarchical Clustering

Lecture 2: Part 2 – Hierarchical Clustering

Lecture 3: Part 3 – Hierarchical Clustering

Chapter 27: Market Basket Analysis

Lecture 1: Market Basket Analysis

Chapter 28: MBA and Association Rule Mining

Lecture 1: MBA and Association Rule Mining

Chapter 29: Implementing MBA

Lecture 1: Implementing MBA

Chapter 30: Association Rule Learning

Lecture 1: Association Rule Learning

Chapter 31: Decision Tree Algorithm

Lecture 1: Decision Tree Algorithm

Chapter 32: Exploring Advanced Methods

Lecture 1: Exploring Advanced Methods

Chapter 33: Using Kernel Methods

Lecture 1: Using Kernel Methods

Chapter 34: Documentation and Deployment

Lecture 1: Documentation and Deployment

Chapter 35: End of Course Quiz

Instructors

Uplatz Training
Fastest growing global Technology & Cloud Training Provider

Rating Distribution

1 stars: 4 votes
2 stars: 2 votes
3 stars: 9 votes
4 stars: 24 votes
5 stars: 41 votes

Frequently Asked Questions

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