# Data Science using R Programming

Data Science using R Programming

As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data. R is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling.

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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Description

A warm welcome to the Data Science with R course 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.

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