Data Science with R

DS with R

Detailed Coverage:

  1. Introduction to Business Analytics
    • Objectives:This lesson will help the learners identify the need for Data Analytics and explain the concept of Data warehouse, data mining and statistical analysis
    • Need of Business Analytics
    • Introduction to Business Analytics
    • Features of Business Analytics
    • Types of Business Analytics
    • Descriptive Analytics
    • Predictive Analytics
    • Prescriptive Analytics
    • Supply Chain Analytics
    • Health care Analytics
    • Human Resource Analytics
    • Web Analytics
    •  Business Decisions
    • Business Intelligence (BI)
    • Data Science
    • Importance of Data Science
    • Data Science as a Strategic Asset
    • Big Data
    • Analytical Tools
  2. Introduction to R programing
    • Objectives – This lesson will help the learners to describe R progaming and learn its different operators to perform different kind of operations on the data.
    • An Introduction to R
    • Comprehensive R Archive Network (CRAN)
    • Cons of R
    • Companies Using R
    • Installing R on Various Operating Systems
    • Installing R on Windows from CRAN Website
    • IDEs for R
    • Installing RStudio on Various Operating Systems
    • Steps in R Initiation
    • Benefits of R Workspace
    • Setting the Workplace
    • Functions and Help in R
    • R Packages
    • Installing an R Package
    • Operators in R
    • Arithmetic Operators
    • Relational Operators
    • Logical Operators
    • Assignment Operators
    • Conditional Statements in R
    • Ifelse() Function
    • Loops in R
    • Break Statement
    • Next Statement
    • Scan Function
    • Running an R Script
    • Running a Batch Script
    • R Functions
  3. Data Structure and Apply functions in R
    • Objectives: 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 RStudio
    • Graphics in R
    • Types of Graphics
    • Bar Charts
    • Creating Simple Bar Charts
    • Editing a Simple Bar Chart
    • Pie Charts
    • Histograms
    • Creating a Histogram
    • Kernel Density Plots
    • Creating a Kernel Density Plot
    • Line Charts
    • Creating a Line Chart
    • Box Plots
    • Creating a Box Plot
    • Heat Maps
    • Creating a Heat Map
    • World Clouds
    • Creating a Word Cloud
    • File Formats for Graphic Outputs
    • Saving a Graphic Output as a File
    • Exporting Graphs in RStudio
    • Exporting Graphs as PDFs in RStudio
  4. Introduction to Statistics
    • Objectives: This lesson will help the learners to explain basic concept of Statistics. It will hep them explaining the two types of data,describing the types of measurements,list the steps of statistical investigation and discuss the importance of normal distribution in statistics. This lesson will also explain the various distance measures Explain the various distance measures and describe the various types of correlations.
    • Recap
    • Basics of Statistics
    • Types of Data
    • Qualitative vs. Quantitative Analysis
    • Types of Measurements in Order
    • Nominal Measurement
    • Ordinal Measurement
    • Interval Measurement
    • Ratio Measurement
    • Statistical Investigation
    • Statistical Investigation Steps
    • Normal Distribution
    • Example of Normal Distribution
    • Importance of Normal Distribution in Statistics
    • Use of the Symmetry Property of Normal Distribution
    • Standard Normal Distribution
    • Distance Measures
    • Distance Measures—A Comparison
    • Euclidean Distance
    • Example of Euclidean Distance
    • Manhattan Distance
    • Minkowski Distance
    • Mahalanobis Distance
    • Cosine Similarity
    • Correlation
    • Correlation Measures Explained Pearson Product Moment Correlation (PPMC)
    • Pearson Correlation—Case Study
    • Dist() Function in R
  5. Hypothesis Testing I
    • Objectives: 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.
    • Recap
    • Hypothesis
    • Need of Hypothesis Testing in Businesses
    • Null Hypothesis
    • Alternate Hypothesis
    • Null vs. Alternate Hypothesis
    • Chances of Errors in Sampling
    • Types of Errors
    • Contingency Table
    • Decision Making
    • Critical Region
    • Level of Significance
    • Confidence Coefficient
    • β Risk
    • Power of Test
    • Factors Affecting the Power of Test
    • Types of Statistical Hypothesis Tests
    • An Example of Statistical Hypothesis Tests
    • Upper Tail Test
    • Upper Tail Test
    • Upper Tail Test
    • Test Statistic
    • Factors Affecting Test Statistic
    • Critical Value Using Normal Probability Table
  6. Hypothesis Testing II
    • Objectives: 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.
    • Recap
    • Parametric Tests
    • Z-Test
    • Z-Test in R—Case Study
    • T-Test
    • T-Test in R—Case Study
    • Testing Null Hypothesis
    • Testing Null Hypothesis
    • Testing Null Hypothesis
    • Testing Null Hypothesis
    • Testing Null Hypothesis
    • Testing Null Hypothesis
    • Objectives of Null Hypothesis Test
    • Three Types of Hypothesis Tests
    • Hypothesis Tests About Population Means
    • Decision Rules
    • Hypothesis Tests About Population Means—Case Study 1
    • Hypothesis Tests About Population Means—Case Study 2
    • Hypothesis Tests About Population Proportions
    • Hypothesis Tests About Population Proportions—Case Study 1
    • Chi-Square Test
    • Steps of Chi-Square Test
    • Degree of Freedom
    • Chi-Square Test for Independence
    • Chi-Square Test for Goodness of Fit
    • Chi-Square Test for Independence—Case Study
    • Chi-Square Test in R—Case Study
    • Introduction to ANOVA Test
    • One-Way ANOVA Test
    • The F-Distribution and F-Ratio
    • F-Ratio Test
    • F-Ratio Test in R—Example
    • One-Way ANOVA Test—Case Study
    • One-Way ANOVA Test in R—Case Study
  7. Regression Analysis
    • Objectives: 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
    • Recap
    • Introduction to Regression Analysis
    • Use of Regression Analysis—Examples
    • Types Regression Analysis
    • Simple Regression Analysis
    • Multiple Regression Models  Simple Linear Regression Model
    • Simple Linear Regression Model Explained
    • Correlation
    • Correlation Between X and Y
    • Method of Least Squares Regression Model
    • Coefficient of Multiple Determination Regression Model
    • Standard Error of the Estimate Regression Model
    • Dummy Variable Regression Model
    • Interaction Regression Model
    • Non-Linear Regression
    • Non-Linear Regression Models
    • Non-Linear Models to Linear Models
    • Algorithms for Complex Non-Linear Model
  8. Classification
    • Objectives: 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 .
    • Recap
    • Introduction to Classification
    • Examples of Classification
    • Classification vs. Prediction
    • Classification System
    • Classification Process
    • Classification Process—Model Construction
    • Classification Process—Model Usage in Prediction
    • Issues Regarding Classification and Prediction
    • Data Preparation Issues
    • Evaluating Classification Methods Issues
    • Decision Tree
    • Decision Tree—Dataset
    • Classification Rules of Trees
    • Overfitting in Classification
    • Tips to Find the Final Tree Size
    • Basic Algorithm for a Decision Tree
    • Statistical Measure—Information Gain
    • Calculating Information Gain—Example
    • Calculating Information Gain for Continuous-Value Attributes
    • Enhancing a Basic Tree  Decision Trees in Data Mining
    • Naive Bayes Classifier Model
    • Features of Naive Bayes Classifier Model
    • Bayesian Theorem
    • Naive Bayes Classifier
    • Bayesian Theorem
    • Applying Naive Bayes Classifier—Example
    • Naive Bayes Classifier—Advantages and Disadvantage
    • Nearest Neighbor Classifiers
    • Computing Distance and Determining Class
    • Choosing the Value of K
    • Scaling Issues in Nearest Neighbor Classification
    • Support Vector Machines
    • Advantages of Support Vector Machines
    • Geometric Margin in SVMs
    • Linear SVMs
    • Non-Linear SVMs
  9. Clustering & Association
    • Objectives This lesson will help the learners to Explain clustering, describe clustering use cases and discuss clustering models. This lesson will also explain association rule mining, the parameters of interesting relationships, the strength measures of association rules and the Apriori algorithm
    • Recap
    • Introduction to Clustering
    • Clustering vs. Classification
    • Use Cases of Clustering
    • Clustering Models
    • K-means Clustering
    • K-means Clustering Algorithm
    • Pseudo Code of K-means
    • K-means Clustering Using R
    • K-means Clustering—Case Study
    • Hierarchical Clustering
    • Hierarchical Clustering Algorithms
    • Requirements of Hierarchical Clustering Algorithms
    • Agglomerative Clustering Process
    • Hierarchical Clustering—Case Study
    • DBSCAN Clustering
    • Concepts of DBSCAN
    • DBSCAN Clustering Algorithm
    • DBSCAN Clustering—Case Study
    • Association Rule Mining
    • Application Areas of Association Rule Mining
    • Parameters of Interesting Relationships
    • Association Rules
    • Association Rule Strength Measures
    • Limitations of Support and Confidence
    • Apriori Algorithm
    • Apriori Algorithm—Example
    • Applying Apriori Algorithm
    • Step 1—Mine All Frequent Item Sets
    • Algorithm to Find Frequent Item Set
    • Finding Frequent Item Set—Example
    • Ordering Items
    • Candidate Generation
    • Candidate Generation—Example
    • Step 2—Generate Rules from Frequent Item Sets
    • Generate Rules from Frequent Item Sets—Example
    • Problems with Association Mining


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Course Content

Time: 3 days

Curriculum is empty



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  • Pushpendra

    Loved it. Look forward to more courses coming up

    I never thought learning Data Science could be so easy, all thanks to eAge and the team for the amazing Data Science platform.
  • Sagar

    Great course and great delivery.

    Really appreciate the sessions and the staff who are strong knowledge on Data science.
  • Ravisekhar

    Trainer is exceptionally good

    Best part of the Training is along with Theory concepts, we are asked to practice live in the class itself. All essential things are covered to start our journey in Data Science domain.

Customized, Immersive, Hands-On Driven


  • Real time virtual classes
  • Pre course reading material
  • Suppliment resources
  • Language: English
  • Certificate of completion

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Your Dream Course Is Only A Step Away


Your Dream Course Is Only A Step Away


Your Dream Course Is Only A Step Away