About Data Sciences with R

Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.

Course Content

Lesson 1: Introduction to Business Analytics
 Introduction to Business Analytics
 Types of Analytics
 Case study on Walmart, Signet Bank
 Data Science and its importance
Lesson 2: Introduction to R
 Introduction to R
 Installing R
 Installing R Studio
 Workspace Setup
 R Packages
Lesson 3: R Programming
 R Programming
 if statements
 for statements
 while statements
 repeat statements
 break and next statements
 switch statement
 scan statement
 Executing the commands in a File
Lesson 4: R Data Structure
 Data structures
 Vector
 Matrix
 Array
 Data frame
 List
 Factors
Lesson 5: Apply Functions
 DPLYR & apply Function
 Import Data File
 DPLYP – Selection
 DPLYP – Filter
 DPLYP – Arrange
 DPLYP – Mutate
 DPLYP – Summarize
Lesson 6: Data Visualization
 Data visualization in R
 Bar chart, Dot plot
 Scatter plot, Pie chart
 Histogram and Box plot
 Heat Maps
 World Cloud
Lesson 7: Introduction to Statistics
 Introduction to statistics
 Type of Data
 Distance Measures (Similarity, dissimilarity, correlation)
 Euclidean space.
 Manhattan
 Minkowski
 Cosine similarity
 Mahalanobis distance
 Pearson’s correlation coefficient
 Probability Distributions
Lesson 8: Hypothesis Testing I
 Hypothesis Testing
 Introduction
 Hypothesis Testing – T Test, Anova
Lesson 9: Hypothesis Testing II
 Hypothesis Testing about population
 Chi Square Test
 F distribution and F ratio
Lesson 10: Regression Analysis
 Regression
 Linear Regression Models
 Non Linear Regression Models
Lesson 11: Classification
 Classification
 Decision Tree
 Logistic Regression
 Bayesian
 Support Vector Machines
Lesson 12: Clustering
 Clustering
 K-means Clustering and Case Study
 DBSCAN Clustering and Case study
 Hierarchical Clustering
Lesson 13: Association
 Association
 Apriori Algorithm
 Candidate Generation
 Visualization on Associated Rules
 Summary
 Conclusion

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