Course Description

Produced Exclusively for DASCA by John Wiley, USA

Produced exclusively for DASCA by John Wiley, USA under the DASCA Data Science Knowledgeware project. This resource is an integral part of the DASCA certification exam preparation kit provided to all individuals who have formally registered for the DASCA ABDE™ certification program. The courses included in this program are solely for assisting and complementing learning and comprehension on some important topics included in the DASCA certification exam preparation kit provided to learners. The access to these course-modules is restricted to only those individuals who are formally registered in the DASCA ABDE™ certification program.

Course curriculum

  • 1

    Introduction to Business Analytics

    • Overview

    • Business Decisions and Analytics

    • Types of Business Analytics

    • Applications of Business Analytics

    • Data Science Overview

    • Conclusion

  • 2

    Introduction to R Programming

    • Overview

    • Importance of R

    • Data Types and Variables In R

    • Operators in R

    • Conditional Statements in R

    • Loops in R

    • R Script

    • Functions in R

    • Conclusion

  • 3

    Data Structures

    • Overview

    • Identifying Data Structures

    • Demo Identifying Data Structures

    • Assigning Value to Data Structures

    • Data Manipulation

    • Demo Assigning Values and Applying Functions

    • Conclusion

  • 4

    Data Visualization

    • Overview

    • Introduction to Data Visualization

    • Data Visualization Using Graphics in R

    • GGPlot 2

    • File Formats of Graphic Outputs

    • Conclusion

  • 5

    Statistics for Data Science- 1

    • Overview

    • Introduction to Hypothesis

    • Types of Hypothesis

    • Data Sampling

    • Confidence and Significance Levels

    • Conclusion

  • 6

    Statistics for Data Science- 2

    • Overview

    • Hypothesis Test

    • Parametric Test

    • Non-Parametric Test

    • Hypothesis test about Population

    • Hypothesis about Population Variance

    • Hypothesis about Population Proportions

    • Conclusion

  • 7

    Regression Analysis

    • Overview

    • Introduction to Regression Analysis

    • Types of Regression Analysis Models

    • Linear Regression

    • Demo Simple Linear Regression

    • Non-Linear Regression

    • Demo Regression Analysis with Multiple Variables

    • Cross Validation

    • Non-Linear to Linear models

    • Principal Component Analysis

    • Factor Analysis

    • Conclusion

  • 8

    Classification

    • Overview

    • Classification and its types

    • Logistic Regression

    • Support Vector Machines

    • K-Nearest Neighbours

    • Naive Bayes Classifier

    • Demo Naive Bayes Classifier

    • Decision Tree Classification

    • Demo Decision Tree Classification

    • Random Forest Classification

    • Evaluating Classifier Models

    • Demo K-Fold Cross Validation

    • Conclusion

  • 9

    Clustering

    • Overview

    • Introduction to Clustering

    • Clustering Methods

    • Demo K-Means Clustering

    • Demo Hierarchical Clustering

    • Conclusion

  • 10

    Association

    • Overview

    • Association Rule

    • Apriori Algorithm

    • Demo Apriori Algorithm

    • Conclusion