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Data Science Certification Training - Simplilearn

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RM 3866.00
  1. 24 Hours
  1. HRDF SBL Claimable
  2. Certificate of Attendance available
  3. 180 days of access from date of purchase
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Data Science Certification Training - Simplilearn

WHAT YOU WILL LEARN

This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc.

AUDIENCE

There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We recommend this Data Science training particularly for the following professionals:

• IT professionals looking for a career switch into data science and analytics

• Software developers looking for a career switch into data science and analytics

• Professionals working in data and business analytics

• Graduates looking to build a career in analytics and data science

• Anyone with a genuine interest in the data science field

• Experienced professionals who would like to harness data science in their fields


METHODOLOGY

This program will be conducted with interactive lectures, PowerPoint presentation, discussion and practical exercise.

COURSE OBJECTIVES

The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies.

• Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.

• Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.


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Modules

Module 1: Course Introduction
• Course Introduction
Module 2: Introduction to Business Analytics

• Overview

• Business Decisions and Analytics

• Types of Business Analytics

• Applications of Business Analytics

• Data Science Overview

• Conclusion

• Knowledge Check


Module 3: 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

• Knowledge Check


Module 4: Data Structures

• Overview

• Identifying Data Structures

• Demo Identifying Data Structures

• Assigning Values to Data Structures

• Data Manipulation

• Demo Assigning values and applying functions

• Conclusion

• Knowledge Check


Module 5: Data Visualization

• Overview

• Introduction to Data Visualization

• Data Visualization using Graphics in R

• ggplot2

• File Formats of Graphic Outputs

• Conclusion

• Knowledge Check


Module 6: Statistics for Data Science-I

• Overview

• Introduction to Hypothesis

• Types of Hypothesis

• Data Sampling

• Confidence and Significance Levels

• Conclusion

• Knowledge Check


Module 7: Statistics for Data Science-II

• Overview

• Hypothesis Test

• Parametric Test

• Non-Parametric Test

• Hypothesis Tests about Population Means

• Hypothesis Tests about Population Variance

• Hypothesis Tests about Population Proportions

• Conclusion

• Knowledge Check


Module 8: 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

• Knowledge Check


Module 9: Classification

• Overview

• Classification and Its Types

• Logistic Regression

• Support Vector Machines

• Demo 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

• Knowledge Check


Module 10: Clustering

• Overview

• Introduction to Clustering

• Clustering Methods

• Demo K-means Clustering

• Demo Hierarchical Clustering

• Conclusion

• Knowledge Check


Module 11: Association

• Overview

• Association Rule

• Apriori Algorithm

• Demo Apriori Algorithm

• Conclusion

• Knowledge Check


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