DATA SCIENCE IN R

Learn with Inspire Data Science in R which is in an increasingly popular language for data analysis and data science .

Course Description

Data Science in R is an increasingly popular programming language, particularly in the world of data analysis and data science. R is absolutely worth learning! In fact, R has some big advantages over other language for anyone who’s interested in learning data science:

Data visualization in R can be both simple and very powerful.
R was built to perform statistical computing.

What you will Learn !

DATA SCIENCE IN R SYALLABUS

Introduction to Data Science
- - - What is Data Science
    - Scenarios on Data Science
    - How Data Science helps for Organization?
    - Explain different types of data
    - Structured, Unstructured data and Machine generated data
    - Understanding on Data Science Process
    - Explain on Research Goal
    - Data Processing on Data Science
Introduction with R Programming
- - - Overview of R
    - Why R for Data Science
    - Download and Installing R
    - Installing R Studio
    - Eclipse
    - Live-R
    - Project Workspace Setup
    - Understanding on R Packages
    - Installing Packages
    - Load Libraries and Installed Packages
Working with R Programming
- - - Data Types and Syntax
    - Processing on Variables
    - Data Items on Structure
    - Classes and Manipulate Objects
    - Control statements IF, ELSE, SWITCH
    - Loop statements FOR, WHILE, REPEAT
    - Working with String and Date
    - Understanding on Vector, List and Data Frames
    - Working with Arrays and Matrices
    - Explain String and Factors
    - Explain on Input and Output
    - Read and Write data from CSV, Tabular Data and Database
Working with Statistics
- - - Introduction to Descriptive Statistics
    - Box Plot
    - Probability and Sampling
    - Inferential statistics
Working with Hypothesis
- - - Overview of Hypothesis
    - Z-Test
    - Correlation and Covariance
    - Chi Square Distributions
    - F-distribution and F-ratio

Introduction to Predictive Models and Machine Learning
- - - What is Model
    - Introduction to statistics
    - Explain on statistical Modelling
    - Probability Distributions
    - Understanding on Machine Learning
    - Explain on Single Variable and Many Variable Models
    - Explorer on Machine Learning Algorithms
    - Mahalanobis distance
    - Pearson's correlation coefficient
Working with Regression Analysis
- - - Introduction to Regression
    - Explain on Different type of Regression
    - Linear Regression Models
    - Fitting the Model
    - Understanding on K-Nearest Neighbours (k-NN)
    - Distance Metrics
    - K-means
    - Non-Linear Regression Models
    - Understanding on Logical Regression
Working with Spam Filters and Naïve Bayes
- - - Explain limitation on linear Regression
    - Introduction to Spam Filter
    - Understanding on Naïve Bayes
    - In-depth Bayes Law
    - Spam Filter using Bayes
    - Naïve Bayes vs K-NNM
Working with Clustering
- - - Introduction to Clustering
    - Packages
    - K-means Clustering
    - Hierarchical Clustering
    - Medoids Clustering
    - DBSCAN Clustering
Working with Decision Tree
- - - Overview of Decision Tree
    - Explain Decision Tree Algorithm
    - Continuous Variables on Decision Tree
    - Explain on Classification
    - Random Forest Classifier
Working with Data Visualization
- - - Overview of Data Visualization
    - Data visualization in R
    - Packages
    - Interactive Graphics
    - Plotting
    - Scatterplot
    - Bar plot
    - Pie chart
    - Histogram and Box plot
    - Heat Maps
    - XKD-Style Plots