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Data Science Certification using R

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    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