Data Science with R

Created by Uplatz Training | Published 7/2021
Duration: 23h 4m | 34 sections | 39 lectures | Video: 1280×720, 44 KHz | 11.5 GB
Genre: eLearning | Language: English + Sub

Learn Data Science using R from scratch. Build your career as a Data Scientist. Explore knitr, buzz dataset, adv methods

What you’ll 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
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Requirements
Enthusiasm and determination to make your mark on the world!
Description
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.
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Uplatz provides this comprehensive course on Data Science with R covering data science concepts implementation and application using R programming language.
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Data Science with R – Course Syllabus
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1. Introduction to Data Sciencee
1.1 The data science process
1.2 Stages of a data science project
1.3 Setting expectations
1.4 Summary

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