Course Overview

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This course will help candidates to gain expertise in Machine Learning Algorithms like K-Means Clustering, Regression, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics and mathematical algorithms used in Machine Learning. Throughout this Data Science Course, the candidate will implement real-life use-cases.

 

Learning Outcome

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On successful completion of the course, a candidate will be able to:

 

1. Understand characteristics of a dataset and do exploratory data analysis.

2. Decide on an appropriate machine learning algorithm to solve a business problem.

3. Train and build a predictive model using R for aiding business decisions.

4. Test accuracy of model and do predictions on future data using the predictive model.

 

 

Course Duration

36 Hours

 

 

                                               Course Curriculum

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1.  Introduction to Data Analytics – and R

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• Introduction to Artificial Intelligence and Data Analytics

• Factors driving surge in popularity of Data Analytics.

• Data Analytics Use Cases from multiple industries.

• Data Ecosystem of an Organisation.

• Difference between Business Analytics and Data Analytics

• Skill set for Data Analytics professional.

• Reasons for growing popularity of R.

• Basics of R language

• Introduction to R Ecosystem and Community

• R in comparison to other analytics tools

 

2.  Installation of R Studio and Packages

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• Installation of R Studio

• Installation of packages in R

• Basic operations using R Studio.

 

3.  R programming – Basics I

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• Data types in R – vectors, matrix, arrays, lists, data frames.

• Basic Data manipulation

 

4.  R programming – Basics II

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• Use of built in functions in R like, seq(), cbind(), rbind(), merge()

• Summarise and interpreting data using functions like, str(), class(), length(), nrow(), ncol()

• Inspecting data frames with functions like, head(), tail()

 

5.  R Programming – Basics III

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• For and While Loops in R

 

6.  Data Manipulation

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• Steps in Data Cleaning

• Data Inspection

• Tackling the issues arising during data cleaning.

• Use of string manipulation functions like grepl(), grep(), sub()

• Data coercion

• Use of apply() family of functions.

 

7.  Exploratory Data Analysis and Data Visualisation

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• Exploratory Data Analysis (EDA) – Concepts

• Performing EDA on datasets

• Co-relation function cor() in R

• Use of packages for EDA in R

• Use of Plot function

• Creating various graphs like, boxplot, density plot and histogram.

 

8.  Advance Data Visualisation

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• Lattice package for Visualisation

• GGplot2 package for visualisation

• Decision making through plots

 

9.  Machine Learning - I: Segmentation

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• K-Means Clustering

• Hierarchical Clustering

 

10. Machine Learning - II: Regression

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• Simple Linear Regression

• Multiple Linear Regression

• Decision Tree – Regression

• Random Forest - Regression

 

11.  Machine Learning - III: Classification

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• Logistic Regression

• K-Nearest Neighbour

• Naïve Bayes

• Support Vector Machine

• Decision Tree – Classification

• Random Forest - Classification

 

12.  Machine Learning - IV: Recommendation System

 

• Association Mining Rule (Market Basket Analysis)