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Data Science Certification Training - R Programming

Course Summary

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.


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    Course Syllabus


    Course preview

    Data Science with R

    Lesson 01 - Introduction to Business Analytics 22:29

    1.1 Introduction 00:10

    1.2 Objectives 00:15

    1.3 Need of Business Analytics 01:28

    1.4 Business Decisions 00:22

    1.5 Business Decisions (contd.) 00:07

    1.6 Introduction to Business Analytics 01:09

    1.7 Features of Business Analytics 01:20

    1.8 Types of Business Analytics 00:19

    1.9 Descriptive Analytics 00:55

    1.10 Predictive Analytics 01:09

    1.11 Predictive Analytics (contd.) 00:41

    1.12 Prescriptive Analytics 01:13

    1.13 Prescriptive Analytics (contd.) 00:24

    1.14 Supply Chain Analytics 00:56

    1.15 Health Care Analytics 00:40

    1.16 Marketing Analytics 00:44

    1.17 Human Resource Analytics 00:36

    1.18 Web Analytics 00:46

    1.19 Application of Business Analytics - Wal-Mart Case Study 00:16

    1.20 Application of Business Analytics - Wal-Mart Case Study (contd.) 00:29

    1.21 Application of Business Analytics - Wal-Mart Case Study (contd.) 00:35

    1.22 Application of Business Analytics - Signet Bank Case Study 00:29

    1.23 Application of Business Analytics - Signet Bank Case Study (contd.) 00:46

    1.24 Application of Business Analytics - Signet Bank Case Study (contd.) 00:44

    1.25 Business Decisions 00:37

    1.26 Business Intelligence (BI) 01:09

    1.27 Data Science 00:33

    1.28 Importance of Data Science 00:35

    1.29 Data Science as a Strategic Asset 00:25

    1.30 Big Data 00:39

    1.31 Analytical Tools 00:16

    1.32 Quiz

    1.33 Summary 00:52

    1.34 Summary (contd.) 00:39

    1.35 Conclusion 00:11

    Lesson 02 - Introduction to R 15:36

    2.1 Introduction 00:11

    2.2 Objectives 00:21

    2.3 An Introduction to R 00:56

    2.4 Comprehensive R Archive Network (CRAN) 00:39

    2.5 Cons of R 01:00

    2.6 Companies Using R 01:16

    2.7 Understanding R 00:55

    2.8 Installing R on Various Operating Systems 00:09

    2.9 Installing R on Windows from CRAN Website 00:15

    2.10 Installing R on Windows from CRAN Website (contd.) 00:20

    2.11 Installing R on Windows from CRAN Website (contd.) 00:09

    2.12 Demo - Install R 00:06

    2.13 Install R 01:02

    2.14 IDEs for R 00:50

    2.15 Installing RStudio on Various Operating Systems 00:34

    2.16 Demo - Install RStudio 00:06

    2.17 Install RStudio 00:51

    2.18 Steps in R Initiation 00:20

    2.19 Benefits of R Workspace 00:40

    2.20 Setting the Workplace 00:08

    2.21 Functions and Help in R 00:28

    2.22 Demo - Access the Help Document 00:05

    2.23 Access the Help Document 01:11

    2.24 R Packages 00:48

    2.25 Installing an R Package 00:10

    2.26 Demo - Install and Load a Package 00:05

    2.27 Install and Load a Package 00:56

    2.28 Quiz

    2.29 Summary 00:33

    2.30 Summary (contd.) 00:21

    2.31 Conclusion 00:11

    Lesson 03 - R Programming 25:23

    3.1 Introduction 00:10

    3.2 Objectives 00:20

    3.3 Operators in R 00:15

    3.4 Arithmetic Operators 00:21

    3.5 Demo - Perform Arithmetic Operations 00:05

    3.6 Use Arithmetic Operations 02:00

    3.7 Relational Operators 00:16

    3.8 Demo - Use Relational Operators 00:05

    3.9 Use Relational Operators 01:00

    3.10 Logical Operators 00:41

    3.11 Demo - Perform Logical Operations 00:05

    3.12 Use Logical Operators 01:22

    3.13 Assignment Operators 00:13

    3.14 Demo - Use Assignment Operator 00:05

    3.15 Use Assignment Operator 00:32

    3.16 Conditional Statements in R 00:24

    3.17 Conditional Statements in R (contd.) 00:34

    3.18 Conditional Statements in R (contd.) 00:32

    3.19 Ifelse() Function 00:18

    3.20 Demo - Use Conditional Statements 00:06

    3.21 Use Conditional Statements 01:44

    3.22 Switch Function 00:45

    3.23 Demo - Use the Switch Function 00:05

    3.24 Use Switch Function 01:39

    3.25 Loops in R 00:14

    3.26 Loops in R (contd.) 00:33

    3.27 Loops in R (contd.) 00:18

    3.28 Loops in R (contd.) 00:31

    3.29 Break Statement 00:38

    3.30 Next Statement 00:35

    3.31 Demo - Use Loops 00:05

    3.32 Use Loops 02:37

    3.33 Scan() Function 01:04

    3.34 Running an R Script 00:40

    3.35 Running a Batch Script 00:20

    3.36 R Functions 00:33

    3.37 R Functions (contd.) 00:05

    3.38 Demo - Use R Functions 00:06

    3.39 Use Commonly Used Functions 01:37

    3.40 Demo - Use String Functions 00:07

    3.41 Use Commonly-USed String Functions 00:53

    3.42 Quiz

    3.43 Summary 00:39

    3.44 Conclusion 00:11

    Lesson 04 - R Data Structure 26:50

    4.1 Introduction 00:10

    4.2 Objectives 00:16

    4.3 Types of Data Structures in R 00:41

    4.4 Vectors 00:47

    4.5 Demo - Create a Vector 00:05

    4.6 Create a Vector 01:24

    4.7 Scalars 00:12

    4.8 Colon Operator 00:15

    4.9 Accessing Vector Elements 00:44

    4.10 Matrices 00:35

    4.11 Matrices (contd.) 00:18

    4.12 Accessing Matrix Elements 00:23

    4.13 Demo - Create a Matrix 00:05

    4.14 Create a Matrix 01:45

    4.15 Arrays 00:33

    4.16 Accessing Array Elements 00:14

    4.17 Demo - Create an Array 00:05

    4.18 Create an Array 01:31

    4.19 Data Frames 00:57

    4.20 Elements of Data Frames 00:13

    4.21 Demo - Create a Data Frame 00:05

    4.22 Create a Data Frame 01:54

    4.23 Factors 00:41

    4.24 Demo - Create a Factor 00:05

    4.25 Create a Factor 01:49

    4.26 Lists 00:20

    4.27 Demo - Create a List 00:05

    4.28 Create a List 01:14

    4.29 Importing Files in R 00:22

    4.30 Importing an Excel File 00:53

    4.31 Importing a Minitab File 00:20

    4.32 Importing a Table File 00:29

    4.33 Importing a CSV File 00:43

    4.34 Demo - Read Data from a File 00:05

    4.35 Read Data from a File 03:50

    4.36 Exporting Files from R 00:33

    4.37 Exporting Files from R (contd.) 00:37

    4.38 Exporting Files from R (contd.) 00:17

    4.39 Exporting Files from R (contd.) 00:38

    4.40 Quiz

    4.41 Summary 00:27

    4.42 Conclusion 00:10

    Lesson 05 - Apply Functions 28:01

    5.1 Introduction 00:13

    5.2 Objectives 00:15

    5.3 Types of Apply Functions 00:31

    5.4 Apply() Function 00:13

    5.5 Apply() Function (contd.) 00:57

    5.6 Apply() Function (contd.) 00:31

    5.7 Demo - Use Apply() Function 00:05

    5.8 Use Apply Function 01:10

    5.9 Lapply() Function 01:05

    5.10 Demo - Use Lapply() Function 00:05

    5.11 Use Lapply Function 00:54

    5.12 Sapply() Function 00:56

    5.13 Demo - Use Sapply() Function 00:05

    5.14 Use Sapply Function 01:10

    5.15 Tapply() Function 00:28

    5.16 Tapply() Function (contd.) 00:23

    5.17 Tapply() Function (contd.) 00:19

    5.18 Demo - Use Tapply() Function 00:05

    5.19 Use Tapply Function 01:22

    5.20 Vapply() Function 00:47

    5.21 Demo - Use Vapply() Function 00:05

    5.22 Use Vapply Function 01:57

    5.23 Mapply() Function 00:21

    5.24 Mapply() Function (contd.) 00:16

    5.25 Mapply() Function (contd.) 00:34

    5.26 Dplyr Package - An Overview 01:08

    5.27 Dplyr Package - The Five Verbs 00:51

    5.28 Installing the Dplyr Package 00:15

    5.29 Functions of the Dplyr Package 00:20

    5.30 Functions of the Dplyr Package - Select() 00:30

    5.31 Demo - Use the Select() Function 00:06

    5.32 Use the Select Function 01:35

    5.33 Functions of Dplyr-Package - Filter() 00:59

    5.34 Demo - Use the Filter() Function 00:05

    5.35 Use Select Function 01:14

    5.36 Functions of Dplyr Package - Arrange() 00:10

    5.37 Demo - Use the Arrange() Function 00:06

    5.38 Use Arrange Function 01:29

    5.39 Functions of Dplyr Package - Mutate() 00:21

    5.40 Functions of Dply Package - Summarise() 00:53

    5.41 Functions of Dplyr Package - Summarise() (contd.) 00:40

    5.42 Demo - Use the Summarise() Function 00:06

    5.43 Use Summarise Function 01:42

    5.44 Quiz

    5.45 Summary 00:33

    5.46 Conclusion 00:11

    Lesson 06 - Data Visualization 33:48

    6.1 Introduction 00:11

    6.2 Objectives 00:17

    6.3 Graphics in R 00:38

    6.4 Types of Graphics 00:25

    6.5 Bar Charts 00:34

    6.6 Creating Simple Bar Charts 00:33

    6.7 Editing a Simple Bar Chart 00:34

    6.8 Demo - Create a Bar Chart 00:06

    6.9 Create a Bar Chart 01:50

    6.10 Editing a Simple Bar Chart (contd.) 00:39

    6.11 Editing a Simple Bar Chart (contd.) 00:26

    6.12 Demo - Create a Stacked Bar Plot and Grouped Bar Plot 00:07

    6.13 Create a Stacked Bar Plot and Grouped Bar Plot 01:58

    6.14 Pie Charts 00:51

    6.15 Editing a Pie Chart 00:27

    6.16 Editing a Pie Chart (contd.) 00:28

    6.17 Demo - Create a Pie Chart 00:05

    6.18 Create a Pie Chart 03:01

    6.19 Histograms 00:53

    6.20 Creating a Histogram 00:37

    6.21 Kernel Density Plots 00:19

    6.22 Creating a Kernel Density Plot 00:29

    6.23 Demo - Create Histograms and a Density Plot 00:07

    6.24 Create Histograms and a Density Plot 02:23

    6.25 Line Charts 00:30

    6.26 Creating a Line Chart 00:21

    6.27 Box Plots 00:47

    6.28 Creating a Box Plot 00:53

    6.29 Demo - Create Line Graphs and a Box Plot 00:07

    6.30 Create Line Graphs and a Box Plot 01:59

    6.31 Heat Maps 00:48

    6.32 Creating a Heat Map 00:28

    6.33 Demo - Create a Heat Map 00:06

    6.34 Create a Heatmap 01:10

    6.35 Word Clouds 00:28

    6.36 Creating a Word Cloud 00:52

    6.37 Demo - Create a Word Cloud 00:06

    6.38 Create a Word Cloud 01:23

    6.39 File Formats for Graphic Outputs 00:51

    6.40 Saving a Graphic Output as a File 01:02

    6.41 Saving a Graphic Output as a File (contd.) 00:43

    6.42 Demo - Save Graphics to a File 00:06

    6.43 Save Graphics to a File 00:49

    6.44 Exporting Graphs in RStudio 00:27

    6.45 Exporting Graphs as PDFs in RStudio 00:17

    6.46 Demo - Save Graphics Using RStudio 00:06

    6.47 Save Graphics Using RStudio 00:53

    6.48 Quiz

    6.49 Summary 00:27

    6.50 Conclusion 00:11

    Lesson 07 - Introduction to Statistics 33:59

    7.1 Introduction 00:10

    7.2 Objectives 00:21

    7.3 Basics of Statistics 02:03

    7.4 Types of Data 01:20

    7.5 Qualitative vs. Quantitative Analysis 00:52

    7.6 Types of Measurements in Order 00:35

    7.7 Nominal Measurement 00:46

    7.8 Ordinal Measurement 00:43

    7.9 Interval Measurement 00:49

    7.10 Ratio Measurement 00:59

    7.11 Statistical Investigation 00:13

    7.12 Statistical Investigation Steps 01:03

    7.13 Normal Distribution 00:58

    7.14 Normal Distribution (contd.) 00:36

    7.15 Example of Normal Distribution 00:08

    7.16 Importance of Normal Distribution in Statistics 00:34

    7.17 Use of the Symmetry Property of Normal Distribution 00:52

    7.18 Standard Normal Distribution 00:33

    7.19 Demo - Use Probability Distribution Functions 00:07

    7.20 Use Probability Distribution Functions 06:52

    7.21 Distance Measures 00:42

    7.22 Distance Measures - A Comparison 00:26

    7.23 Euclidean Distance 00:24

    7.24 Example of Euclidean Distance 00:37

    7.25 Manhattan Distance 00:31

    7.26 Minkowski Distance 00:15

    7.27 Mahalanobis Distance 00:27

    7.28 Cosine Similarity 00:26

    7.29 Correlation 00:43

    7.30 Correlation Measures Explained 01:10

    7.31 Pearson Product Moment Correlation (PPMC) 00:41

    7.32 Pearson Product Moment Correlation (PPMC) (contd.) 00:35

    7.33 Pearson Correlation - Case Study 00:35

    7.34 Dist() Function in R 00:40

    7.35 Demo - Perform the Distance Matrix Computations 00:08

    7.36 Perform the Distance Matrix Computations 03:44

    7.37 Quiz

    7.38 Summary 00:35

    7.39 Summary (contd.) 00:35

    7.40 Conclusion 00:11

    Lesson 08 - Hypothesis Testing I 19:29

    8.1 Introduction 00:11

    8.2 Objectives 00:22

    8.3 Hypothesis 02:01

    8.4 Need of Hypothesis Testing in Businesses 00:52

    8.5 Null Hypothesis 00:26

    8.6 Null Hypothesis (contd.) 00:34

    8.7 Alternate Hypothesis 00:37

    8.8 Null vs. Alternate Hypothesis 00:33

    8.9 Chances of Errors in Sampling 00:30

    8.10 Types of Errors 00:57

    8.11 Contingency Table 01:15

    8.12 Decision Making 00:24

    8.13 Critical Region 00:42

    8.14 Level of Significance 00:51

    8.15 Confidence Coefficient 00:49

    8.16 Bita Risk 00:26

    8.17 Power of Test 00:28

    8.18 Factors Affecting the Power of Test 00:23

    8.19 Types of Statistical Hypothesis Tests 01:05

    8.20 An Example of Statistical Hypothesis Tests 00:31

    8.21 An Example of Statistical Hypothesis Tests (contd.) 00:17

    8.22 An Example of Statistical Hypothesis Tests (contd.) 00:19

    8.23 An Example of Statistical Hypothesis Tests (contd.) 00:23

    8.24 Upper Tail Test 00:30

    8.25 Upper Tail Test (contd.) 00:27

    8.26 Upper Tail Test (contd.) 00:19

    8.27 Test Statistic 00:47

    8.28 Factors Affecting Test Statistic 00:12

    8.29 Factors Affecting Test Statistic (contd.) 00:39

    8.30 Factors Affecting Test Statistic (contd.) 00:09

    8.31 Critical Value Using Normal Probability Table 00:17

    8.32 Quiz

    8.33 Summary 01:02

    8.34 Conclusion 00:11

    Lesson 09 - Hypothesis Testing II 39:45

    9.1 Introduction 00:11

    9.2 Objectives 00:15

    9.3 Parametric Tests 00:35

    9.4 Z-Test 00:23

    9.5 Z-Test in R - Case Study 00:50

    9.6 T-Test 00:30

    9.7 T-Test in R - Case Study 00:35

    9.8 Demo - Use Normal and Student Probability Distribution Functions 00:08

    9.9 Use Normal and Student Probability Distribution Functions 01:32

    9.10 Testing Null Hypothesis 00:50

    9.11 Testing Null Hypothesis 00:08

    9.12 Testing Null Hypothesis 00:09

    9.13 Testing Null Hypothesis 00:20

    9.14 Testing Null Hypothesis 00:14

    9.15 Testing Null Hypothesis 01:00

    9.16 Objectives of Null Hypothesis Test 00:58

    9.17 Three Types of Hypothesis Tests 00:17

    9.18 Hypothesis Tests About Population Means 00:42

    9.19 Hypothesis Tests About Population Means (contd.) 00:50

    9.20 Hypothesis Tests About Population Means (contd.) 00:27

    9.21 Decision Rules 01:21

    9.22 Hypothesis Tests About Population Means - Case Study 1 01:30

    9.23 Hypothesis Tests About Population Means - Case Study 2 01:21

    9.24 Hypothesis Tests About Population Means - Case Study 2 (contd.) 00:22

    9.25 Hypothesis Tests About Population Proportions 00:28

    9.26 Hypothesis Tests About Population Proportions (contd.) 00:29

    9.27 Hypothesis Tests About Population Proportions (contd.) 01:03

    9.28 Hypothesis Tests About Population Proportions - Case Study 1 00:22

    9.29 Hypothesis Tests About Population Proportions - Case Study 1 (contd.) 00:55

    9.30 Chi-Square Test 00:28

    9.31 Steps of Chi-Square Test 00:38

    9.32 Steps of Chi-Square Test (contd.) 00:30

    9.33 Important Points of Chi-Square Test (contd.) 00:31

    9.34 Degree of Freedom 00:35

    9.35 Chi-Square Test for Independence 00:51

    9.36 Chi-Square Test for Goodness of Fit 00:42

    9.37 Chi-Square Test for Independence - Case Study 00:28

    9.38 Chi-Squar Test for Independence - Case Study (contd.) 00:26

    9.39 Chi-Square Test in R - Case Study 00:38

    9.40 Chi-Square Test in R - Case Study (contd.) 00:31

    9.41 Demo - Use Chi-Squared Test Statistics 00:10

    9.42 Use Chi-Squared Test Statistics 02:35

    9.43 Introduction to ANOVA Test 01:03

    9.44 One-Way ANOVA Test 01:10

    9.45 The F-Distribution and F-Ratio 01:22

    9.46 F-Ratio Test 00:37

    9.47 F-Ratio Test in R - Example 00:22

    9.48 One-Way ANOVA Test - Case Study 00:20

    9.49 One-Way ANOVA Test - Case Study (contd.) 00:45

    9.50 One-Way ANOVA Test in R - Case Study 00:49

    9.51 One-Way ANOVA Test in R - Case Study (contd.) 00:29

    9.52 One-Way ANOVA Test in R - Case Study (contd.) 00:35

    9.53 Demo - Perform ANOVA 00:07

    9.54 Perform ANOVA 02:55

    9.55 Quiz

    9.56 Summary 01:12

    9.57 Conclusion 00:11

    Lesson 10 - Regression Analysis 20:48

    10.1 Introduction 00:11

    10.2 Objectives 00:14

    10.3 Introduction to Regression Analysis 00:53

    10.4 Use of Regression Analysis - Examples 00:24

    10.5 Use of Regression Analysis - Examples (contd.) 00:23

    10.6 Types Regression Analysis 00:39

    10.7 Simple Regression Analysis 00:27

    10.8 Multiple Regression Models 00:25

    10.9 Simple Linear Regression Model 00:37

    10.10 Simple Linear Regression Model Explained 00:29

    10.11 Demo - Perform Simple Linear Regression 00:06

    10.12 Perform Simple Linear Regression 02:13

    10.13 Correlation 00:20

    10.14 Correlation Between X and Y 00:27

    10.15 Correlation Between X and Y (contd.) 00:24

    10.16 Demo - Find Correlation 00:06

    10.17 Find Correlation 01:23

    10.18 Method of Least Squares Regression Model 01:02

    10.19 Coefficient of Multiple Determination Regression Model 00:29

    10.20 Standard Error of the Estimate Regression Model 00:44

    10.21 Dummy Variable Regression Model 01:07

    10.22 Interaction Regression Model 00:23

    10.23 Non-Linear Regression 00:29

    10.24 Non-Linear Regression Models 01:24

    10.25 Non-Linear Regression Models (contd.) 01:03

    10.26 Non-Linear Regression Models (contd.) 00:23

    10.27 Demo - Perform Regression Analysis with Multiple Variables 00:07

    10.28 Perform Regression Analysis with Multiple Variables 01:46

    10.29 Non-Linear Models to Linear Models 00:13

    10.30 Algorithms for Complex Non-Linear Models 00:53

    10.31 Quiz

    10.32 Summary 00:26

    10.33 Summary (contd.) 00:28

    10.34 Conclusion 00:10

    Lesson 11 - Classification 33:43

    11.1 Introduction 00:10

    11.2 Objectives 00:17

    11.3 Introduction to Classification 00:40

    11.4 Examples of Classification 00:23

    11.5 Classification vs. Prediction 00:45

    11.6 Classification System 00:10

    11.7 Classification Process 00:54

    11.8 Classification Process - Model Construction 01:03

    11.9 Classification Process - Model Usage in Prediction 00:22

    11.10 Issues Regarding Classification and Prediction 00:15

    11.11 Data Preparation Issues 01:06

    11.12 Evaluating Classification Methods Issues 00:34

    11.13 Decision Tree 00:51

    11.14 Decision Tree - Dataset 00:14

    11.15 Decision Tree - Dataset (contd.) 00:15

    11.16 Classification Rules of Trees 00:34

    11.17 Overfitting in Classification 01:13

    11.18 Tips to Find the Final Tree Size 01:13

    11.19 Basic Algorithm for a Decision Tree 00:42

    11.20 Statistical Measure - Information Gain 01:16

    11.21 Calculating Information Gain - Example 00:08

    11.22 Calculating Information Gain - Example (contd.) 00:05

    11.23 Calculating Information Gain for Continuous-Value Attributes 01:44

    11.24 Enhancing a Basic Tree 00:32

    11.25 Decision Trees in Data Mining 00:18

    11.26 Demo - Model a Decision Tree 00:05

    11.27 Model a Decision Tree 02:06

    11.28 Naive Bayes Classifier Model 01:02

    11.29 Features of Naive Bayes Classifier Model 00:41

    11.30 Bayesian Theorem 00:40

    11.31 Bayesian Theorem (contd.) 00:14

    11.32 Naive Bayes Classifier 00:29

    11.33 Applying Naive Bayes Classifier - Example 00:14

    11.34 Applying Naive Bayes Classifier - Example (contd.) 00:25

    11.35 Naive Bayes Classifier - Advantages and Disadvantages 00:28

    11.36 Demo - Perform Classification Using the Naive Bayes Method 00:07

    11.37 Perform Classification Using the Naive Bayes Method 02:31

    11.38 Nearest Neighbor Classifiers 01:05

    11.39 Nearest Neighbor Classifiers (contd.) 00:20

    11.40 Nearest Neighbor Classifiers (contd.) 00:12

    11.41 Computing Distance and Determining Class 00:34

    11.42 Choosing the Value of K 00:21

    11.43 Scaling Issues in Nearest Neighbor Classification 00:35

    11.44 Support Vector Machines 01:19

    11.45 Advantages of Support Vector Machines 00:29

    11.46 Geometric Margin in SVMs 00:47

    11.47 Linear SVMs 00:08

    11.48 Non-Linear SVMs 00:26

    11.49 Demo - Support a Vector Machine 00:05

    11.50 Support a Vector Machine 01:51

    11.51 Quiz

    11.52 Summary 00:36

    11.53 Conclusion 00:09

    Lesson 12 - Clustering 25:12

    12.1 Introduction 00:11

    12.2 Objectives 00:10

    12.3 Introduction to Clustering 00:42

    12.4 Clustering vs. Classification 00:58

    12.5 Use Cases of Clustering 00:33

    12.6 Clustering Models 01:47

    12.7 K-means Clustering 01:29

    12.8 K-means Clustering Algorithm 00:57

    12.9 Pseudo Code of K-means 00:33

    12.10 K-means Clustering Using R 00:40

    12.11 K-means Clustering - Case Study 00:26

    12.12 K-means Clustering - Case Study (contd.) 00:44

    12.13 K-means Clustering - Case Study (contd.) 01:23

    12.14 Demo - Perform Clustering Using K-means 00:05

    12.15 Perform Clustering Using Kmeans 01:38

    12.16 Hierarchical Clustering 01:12

    12.17 Hierarchical Clustering Algorithms 00:36

    12.18 Requirements of Hierarchical Clustering Algorithms 01:15

    12.19 Agglomerative Clustering Process 00:37

    12.20 Hierarchical Clustering - Case Study 00:37

    12.21 Hierarchical Clustering - Case Study (contd.) 00:10

    12.22 Hierarchical Clustering - Case Study (contd.) 00:22

    12.23 Demo - Perform Hierarchical Clustering 00:05

    12.24 Perform Hierarchical Clustering 01:24

    12.25 DBSCAN Clustering 01:01

    12.26 Concepts of DBSCAN 00:54

    12.27 Concepts of DBSCAN (contd.) 00:51


Course Fee:
USD 699

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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