Advanced Predictive Modeling in R Certification Training

Access Duration - 365 Days
Awarded By  Edureka Awarded By Edureka
Course Materialr Course Material
4.5( 2 REVIEWS )

What Will I Learn?

Understand the basics of statistics using R
Learn about binary response variable
Understand the algorithms related to dimension reduction
Learn the core principles of predictive modeling
Learn predictive modeling techniques


The Advanced Predictive Modeling in R Certification Training is a great course to get started on if you wish to learn about predictive modeling techniques. It is designed to provide learners with the skills and knowledge to become experts in big data analytics. You will understand the benefits of this technique, and how it can be applied in functional areas within a business organisation. This technique can be used in multiple sectors from finance to human resource, strategic planning and operations.

Advanced Predictive Modeling in R Certification Training will provide a wealth of knowledge on the core principles of predictive modeling techniques. Predictive modeling is at the forefront as a competitive strategy in the business sector and is a solution for high-performing companies.

Advanced Predictive Modeling in R Certification Training will also introduce learners to the concept of how to use data to make effective decisions. Qualifying in this course will set you in the right direction, giving you the opportunity to open the door to new and exciting job roles in data and analytics.

Why You Should Consider Taking this Course at Global Edulink?

Global Edulink is a leading online provider for several accrediting bodies, and provides learners the opportunity to take this exclusive course awarded by Edureka. At Global Edulink, we give our fullest attention to our learners’ needs and ensure they have the necessary information required to proceed with the Course.  Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships and their favourite restaurants.

  • Access Duration
  • Who is this Course for?
  • Entry Requirement
  • Method of Assessment
  • Certification
  • Awarding Body
  • Career Path & Progression

The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course.  The course is self-paced and you can complete it in stages, revisiting the lessons at any time.

This course is recommended for individuals, who want to have a flourishing career in Data Analytics,
  • Data Scientists
  • Analytics Managers
  • Data Business Scientists
  • Learners should be over the age of 16, and have a basic understanding of English, ICT and numeracy.
  • A sound educational background is recommended
  • Learners should have a basic understanding of R

In order to qualify in the course ‘Advanced Predictive Modeling in R Certification Training’ successfully, learners will take an online test with each module being rounded off with multiple-choice questions. This online test is marked automatically, so you will receive an instant grade and know whether you have passed the course.

Upon the successful completion of the course, you will be awarded the Advanced Predictive Modeling in R Certification Training by Edureka.

Edureka is a pioneer in online learning and an agency that has frequently and consistently collaborated with educational and academic institutes. It guarantees expert commitment and is dedicated to learners taking their courses. Edureka is internationally recognised and takes learners a step closer to a leading career in the IT industry.

The Advanced Predictive Modeling in R Certification Training will improve your candidature for a number of jobs in data analytics.  You can study further courses in the same field and enhance your academic expertise, and add this as a valuable skillset on your resume. Your skills will be recognised by leading organisations that give you the opportunity to land a well-paying job in a business organisation. Given below are job titles you can compete for, along with the average UK salary per annum according to

  • Data Scientist - £75,000 (Approximately)
  • Analytics Manager - £80,000 (Approximately)
  • Data Analyst - £50,000 (Approximately)

Key Features

Gain an Accredited UK Qualification
Access to Excellent Quality Study Materials
Personalised Learning Experience
Support by Phone, Live Chat, and Email
Eligible for TOTUM Discount Card
UK Register of Learning Providers Reg No : 10053842

Course Curriculum

1: Basic Statistics in R
Covariance & Correlation
Central Limit Theorem
Z Score
Normal Distributions
2: Ordinary Least Square Regression 1
Bivariate Data
Quantifying Association
The Best Line: Least Squares Method
The Regressions
Simple Linear Regression
Deletion Diagnostics and Influential Observations
3: Ordinary Least Square Regression 2
Model fitting using Linear Regression
Performing Over Fitting & Under Fitting
What is Heteroscedasticity?
4: Logistic Regression
Binary Response Regression Model
Linear regression as Linear Probability Model
Problems with Linear Probability Model
Logistic Function
Logistic Curve
Goodness of fit matrix
All Interactions Logistic Regression
Multinomial Logit
Ordered Categorical Variable
5: Advanced Regression
Poisson Regression
Model Fit Test
Offset Regression
Poisson Model with Offset
Negative Binomial
Dual Models
Hurdle Models
Zero-Inflated Poisson Models
Variables used in the Analysis
Poisson Regression Parameter Estimates
Zero-Inflated Negative Binomial
6: Imputation
Missing Values are Common
Types of Missing Values
Why is Missing Data a Problem?
No Treatment Option: Complete Case Method
No Treatment Option: Available Case Method
Problems with Pairwise Deletion
Mean Substitution Method
Regression Substitution Method
K-Nearest Neighbour Approach
Maximum Likelihood Estimation
EM Algorithm
Single and Multiple Imputation
Little’s Test for MCAR
7: Forecasting 1
Need for Forecasting
Types of Forecast
Forecasting Steps
Time Series Components
Variations in Time Series
Forecast Error
Mean Error (ME)
MPE and MAPE—Unit free measure
Additive v/s Multiplicative Seasonality
Curve Fitting
Simple Exponential Smoothing (SES)
Decomposition with R
Generating Forecasts
Explicit Modeling
Modeling of Trend
Seasonal Components
Smoothing Methods
ARIMA Model-building
8: Forecasting 2
Analysis of Log-transformed Data
How to Formulate the Model
Partial Regression Plot
Normal Probability Plot
Tests for Normality
Box-Cox Transformation
Box-Tidwell Transformation
Growth Curves
Logistic Regression: Binary
Neural Network
Network Architectures
Neural Network Mathematics
9: Dimensionality Reduction
Factor Analysis
Principal Component Analysis
Mechanism of finding PCA
Linear Discriminant Analysis (LDA)
Determining the maximum separable line using LDA
Implement Dimensionality Reduction algorithm in R
10: Survival Analysis
Time-to-Event Data
Survival Analysis
Types of Censoring
Survival Analysis Techniques
Elastic Net

Students feedback


Average rating (2)
5 Star
4 Star
3 Star
2 Star
1 Star
    N L

    Nevaeh Lowe

    January 06, 2021
    Future career prospects

    I firmly believe this course will help in my future career prospects and I’m able to demonstrate my skills and knowledge on this subject matter.

    M S

    Maggie Shaw

    December 01, 2020
    Effective training

    The course focused completely on modeling techniques and core principles. I learned the basics of statistics using R, exploring Regression and understand forecasting.

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