Our Predictive Modelling in R Training course helps you master R Programming for Data Science through tutorials on advanced analytics concepts; regression, forecasting,

1. Unlock your Learning Potential !
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Course details:
Course Code : MYT1644
Course Name: Predictive Modelling in R
Course duration: Fast track – 4 weeks
Regular weekdays – 6 weeks
Week End – 8 weeks
Training mode:
instructor led class training | Live virtual training
Contact: +91 90191 91856
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2. Basic Statistics in R
 Learning Objectives - In this module, you will get an introduction to statistics and
conduct best test and exploratory analysis.
 Topics- Basic Statistics, Hypothesis Analysis, Correlation, Covariance, Matrix,
Basic Charts.
Ordinary Least Square Regression 1
 Learning Objectives - In this module, you will be introduced to basic regression
and multiple regression, and will learn how to present the same graphically.
 Topics- Exporting Data and Connecting Sheets, Making Basic Visualization in
Tableau, Making Sense out of the Visuals and Interpreting the same.
myTectra Learning Solutions private Limited
Bangalore-BTM Layout/
+91 90191 91856/ info@mytectra.com / www.mytectra.com

3. Ordinary Least Square Regression 2
 Learning Objectives - In this module, you will dive into linear regression and make
the model a better fit, make necessary transformation check for over fitting and
under fitting and outliers identification and treatment.
 Topics- Residual Plots, AV plots, deletion diagnostics, partial correlation, subset
selection, influential observations, transformations, Hetroscadasticity, VIFs, Multi
co-linearity, auto-correlations, tests, dummy variables, seasonality, DW tests, Box-
Cox transformation, interaction variables
Logistic Regression
 Learning Objectives - In this module, you will be introduced to logistic regression
and various uses of the same and also its industry usage.
 Topics- Basic Logistic Regression, Uses, Drawbacks of OLS, Tests.
myTectra Learning Solutions private Limited
Bangalore-BTM Layout/
+91 90191 91856/ info@mytectra.com / www.mytectra.com

4. Advanced Regression
 Learning Objectives - In this module, you will dive into logistic regression, learn
about more varied usage of logistic regression on various dataset.
 Topics- Poisson Regression, Multinomial, ordinal Regression: Business Case &
Zero-inflated regression, Negative binomial, Panel data.
Imputation
 Learning Objectives - In this module, you will learn about addressing missing
values and how to impute it using various process
 .Topics- Imputations using various methods like regression, mode/mean
substitutions.
myTectra Learning Solutions private Limited
Bangalore-BTM Layout/
+91 90191 91856/ info@mytectra.com / www.mytectra.com

5. Forecasting 1
 Learning Objectives - In this module, you will get an introduction to forecasting
and time series data.
 Topics- Techniques, Time series data, Decomposition, ARIMA/ ARMA, ACF and
PACF plots, Seasonality and Smoothing (exponential).
Forecasting 2
 Learning Objectives - In this module, you will learn about Seasonality, Trend
Analysis and decaying the factors over the time.
 Topics- Holt_winter smoothing, Growth Models, binary data, Neural Networks,
ARCH / GARCH, trend lines (exponential trend lines).
myTectra Learning Solutions private Limited
Bangalore-BTM Layout/
+91 90191 91856/ info@mytectra.com / www.mytectra.com

6. Survival Analysis
 Learning Objectives - In this module you will learn about Churn analysis and
Regression on time series data with time component.
 Topics- Survival Analysis, CoxPH analysis, Plots, tests.
Project
 Learning Objectives - In this module, you will work on a dataset of your choice
after approval from the trainer. The project needs to cover all concepts discussed
in the class. The scope of project should enable you to perform various
regressions (including logistic regression), forecasting and survival analysis. You
are encouraged to take up a dataset that has missing value and logically impute
the same before performing any predictive modeling. You can further develop
various models under each section (logistic, forecasting and survival) and then
suggest the best one using any technique of his choice. You also need to perform
EDA and various aggregation and transformation before jumping into model
making and implementing the entire concept on a free dataset
 Topics- Project Discussion.
myTectra Learning Solutions private Limited
Bangalore-BTM Layout/
+91 90191 91856/ info@mytectra.com / www.mytectra.com