# Data Science With R ###### English ###### 40 hours ###### \$\$\$\$\$
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### Course Contents

• In this course we will get to know how to use R in Data Science and Data Analytics Platform
• Installing and configuring essential library for Statistical Analysis and for Predictive Model
• Using Different Libraries, Creating Function and different type of R code
• How to use different graphical chart in R

1. This lesson will explain the learners about basics of statistics which are used in Data Science
• Introduction of course and setting expectation for pre-requisites
• What is Statistics
• Population and Sample
• Types of Data
• Measurements
• Scale of Measurement – Nominal, Ordinal, Interval and Ratio
• Descriptive and Inferential Statistics
• Frequency Distributions
• Measures of Central Tendency
• Comparisons of different measures of Tendency
• Measures of Dispersion
• Comparisons of different measures of Dispersion
• Coefficient of Variation
• Moments and Quantiles
• Skewness and Kurtosis
• Correlation and Regression
• Distribution
2. This lesson will help the learners identify the need for Data Analytics and explain the concept of Data warehouse, data mining and statistical analysis
• Coke Case study
• Business Analytics and Data Science
3. This lesson will help the learners to describe R programing and learn its different operators to  perform different kind of operations on the data
• Supermarket Case Study
• An Introduction to R
• Features of R
• Importance of R for Data Analytics
• Installing R on Windows from CRAN Website
• Lab Demo for R
• Data Types and variables in R
• Operators in R
• Conditional Statements in R
• Loops in R
• Features of R Script
• Functions and Help in R
4. Explain the different data structures used in R  it will Discuss the elements of the different data structures in R. This lesson will also  explain the acceptable formats to import and export data in R
• Identifying Data Structures
• Types of Data Structures – Atomic Vectors
• Matrix
• Arrays
• Factors
• Data Frames
• Lists
• Assigning Values to Data Structures – Importing Data
• Assigning Values to Data Structures – Exporting Data
• Data Manipulation
• Types of apply functions
• DPLYR package
5. This lesson will help the learners to explain the various types of graphics available in R,List the possible file formats of graphic outputs,Describe the methods to save graphics as files and Describe the procedure to export graphs in R
• Introduction to Data Visualization
• Data Visualization in R
• Bar Chart
• Pie chart
• Histogram
• Kernel Density Plot
• Line chart
• Box Plot
• Heat map
• Word Cloud
• GGPLOT2
• File formats of Graphic Outputs
6. This lesson will help the learners to explain the discuss the need of hypothesis testing in businesses. This lesson will also help them differentiate between null and alternate hypotheses, Interpret the confidence level, significance level, and power of a test and explain the types of hypothesis tests
• Introduction to Hypothesis
• Types of Hypothesis
• Data Sampling
• Types of Errors
• Confidence levels
• Critical Region
• Decision Making
• Level of Significance
• Confidence Coefficient
• Critical Region Deviation
• B risk
• Power of Test
• Factors affecting the Power of Test
7. This lesson will help the learners to explain the various parametric tests,discuss the types of null hypothesis tests and expalined them chi-square and ANOVA test
• Parametric Test
• Types of Parametric Test
• Z-Test
• T-Test
• ANOVA
• One way ANOVA
• Two way ANOVA
• Non-Parametric Tests
• Types of Non-Parametric Tests
• Chi-square test
• Types of Chi-square test
8. This lesson will help the learners to explain regression analysis,describe them the different types of regression analysis models. This lesson will also help them list the functions to covert non-linear models to linear models
• Introduction to Regression Analysis
• Types of Regression Analysis Model
• Simple
• Multiple
• Linear
• Non-linear
• Types of Linear Regression Model
• Types of Non-linear Regression Model
• Cross Validation
• Non-linear Model to Linear Model
• Measures of Regression Models
• Principal Component Analysis
• Factor Analysis of Dimensionality Reduction
• Demo Project on Regression (Project 1)
9. This lesson will help the learners to  explain classification,describe the classification system and process. This lesson will also help them list the various issues related to classification and prediction and explain the various classification techniques
• Bank Loan Case study
• Introduction to Classification
• Types of Classification Algorithm
• Logistic Regression
• Support Vector Machine
• KNN(k-Nearest Neighbors)
• Naive Bayes Classifier
• Detecting Spam
• Decision Tree Classification
• Random Forest Classification
• Evaluating Classifier Models
• Confusion Matrix
• AUC-ROC Curve
• K-fold Cross Validation
• Demo Project on Classification (Project 2)
10. This lesson will help the learners to Explain clustering, describe clustering use cases and  discuss clustering models.
• Introduction to Clustering
• Example of Clustering
• Clustering Methods
• Prototype based Clustering
• Hierarchical Clustering
• DBSCAN (Density based Clustering)
• Demo Project on Clustering (Project 3)
11. This lesson will explain association rule mining, the parameters of interesting relationships,the strength measures of association rules and  the Apriori algorithm
• Case study on Association Rule
• Measures of Association Rule
• Apriori Algorithm(Project 4)

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### Instructor Profile Data Scientist with 10 years of experience executing data-driven solutions to increase efficiency, accuracy, and utility of internal data processing.

#### This course includes:

• 100% Online Sessions
• Instructor led
• Customizable Syllabus
• Customizable Schedule
• Certificate of Completion
• Training Recordings
• Training Resources
• Learner Assessment
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