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Data Science Certification using R

Enrolled: 2345 students
Duration: 60 Hrs
Lectures: 99
Level: Intermediate

Introduction to Data Science

1
What is Data Science?
2
Analytics Landscape
3
Life Cycle of a Data Science Projects
4
Data Science Tools & Technologies

R for Data Science

1
Intro to R Programming
2
R Base Software
3
Understanding CRAN
4
RStudio The IDE
5
Basic Building Blocks in R
6
Understanding Vectors in R
7
Basic Operations Operators and Types
8
Handling Missing Values in R
9
Subsetting Vectors in R
10
Matrices and Data Frames in R
11
Lapply, sapply, vapply and tapply Functions

Data Visualization using R

1
Grammar of Graphics
2
Bar Charts
3
Histograms
4
Pie Charts
5
Scatter Plots
6
Line Plots and Regression
7
Word Clouds
8
Box Plots
9
GGPLOT2

Statistical Learning -1 (Including ANOVA)

1
Measures of Central Tendency in Data
2
Measures of Dispersion
3
Understanding Skewness in Data
4
Probability Theory
5
Bayes Theorem
6
Probability Distributions
7
Hypothesis Testing

Statistical Learning - 2 (Including ANOVA)

1
Analysis of Variance and Covariance
2
One way analysis of variance
3
Assumption of ANOVA
4
Statistics associated with one way analysis of variance
5
Interpreting the ANOVA Results
6
Two way analysis of variance
7
Interpreting the ANOVA Results
8
Analysis of Covariance

Exploratory Data Analysis with R

1
IMerge, Rollup, Transpose and Append
2
Missing Analysis and Treatment
3
Outlier Analysis and Treatment
4
Summarizing and Visualizing the Important Characteristics of Data
5
Univariate, Bivariate Analysis
6
Crosstabs, Correlation

Linear Regression

1
What is Regression Analysis?
2
Limitations of Regression
3
Covariance and Correlation
4
Multivariate Analysis
5
Assumptions of Linearity Hypothesis Testing
6
Limitations of Regression
7
Implementing Simple & Multiple Linear Regression
8
Making sense of result parameters
9
Model validation
10
Handling other issues/assumptions in Linear Regression
11
Handling outliers, categorical variables, autocorrelation, multicollinearity, heteroskedasticity Prediction and Confidence Intervals

Project 1

1
Property Price Prediction using Linear Regression in R

Logistic Regression

1
Implementing Logistic Regression
2
Making sense of result parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-square Test Goodness of fit measures
3
Model validation: Cross Validation, ROC Curve, Confusion Matrix

Project 2

1
Bank Credit Card Default Prediction using Logistic Regression in R

Decision Trees

1
Introduction to Predictive Modeling with Decision Trees
2
Entropy & Information Gain
3
Standard Deviation Reduction (SDR)
4
Overfitting Problem
5
Cross Validation for Overfitting Problem
6
Running as a solution for Overfitting

Project 3

1
Churn Analysis in Telecom Industry (Regression Trees

Random Forest

1
Random Forest
2
Project 4 – Churn Analysis in Telecom Industry (Regression Trees & Classification Trees)

Linear Discriminant Analysis

1
LDA Objective
2
Why Discriminant Analysis?
3
Discriminant Function
4
Assumption of LDA
5
Advantages & Disadvantages of LDA
6
Applications of LDA

Project : Wine classification with Linear Discriminant Analysis

1
Project : Wine classification with Linear Discriminant Analysis

Machine Learning & Linear Regression

1
Machine Learning Modelling Flow
2
How to treat Data in ML
3
Parametric & Non-parametric ML
4
Types of Machine Learning
5
Introduction to Linear Regression
6
Linear Regression using Gradient Descent
7
Linear Regression using OLS
8
Linear Regression using Stochastic Gradient Descent
9
Project : Real Estate Price Prediction using Linear Regression

Model Tuning

1
Performance Measures
2
Bias-Variance Trade-Off
3
Overfitting & Underfitting
4
Optimization Techniques
5
Project : Identifying good and bad customers for granting credit

K Nearest Neighbor

1
K Nearest Neighbor
2
Understanding KNN
3
Voronoi Tessellation
4
Choosing K
5
Distance Metrics – Euclideam, Manhattan, Chebyshev
6
Project : Case Study: Breast Cancer

Resume Building and Interview Prep

1
1:1 Mock Interviews with Industry Veterans to Clear the Technical Round of Interviews to Give You Confidence to Face Real World Scenarios

1:1 Mock Interviews

1
1:1 Mock Interviews with Industry Veterans to Clear the Technical Round of Interviews to Give You Confidence to Face Real World Scenarios
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