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Advanced Predictive Modelling in R Certification Training

R provides a free and open source environment that is ideal for both learning and delivering predictive modeling solutions. This Certification Course is designed for a broad audience as both an introduction to predictive models and a guide to using them, including subjects such as Ordinary Least Square Regression, Advanced Regression, Imputation, Dimensionality Reduction, and so on. Readers will also master the fundamentals of statistics, such as correlation and linear regression analysis.

Why This Course

This course is for anybody who wants to learn how to use data to obtain insights and make better business decisions. Accounting, finance, human resource management, marketing, operations, and strategic planning are all areas where the approaches presented are used in corporate organizations.

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Self Paced Online Class

Advanced Predictive Modelling in R Certification Training

Our Online Self-Learning Courses offer a self-paced approach, enabling participants to start learning at their preferred times. These courses provide structured training and incorporate review exercises to solidify understanding. You'll access instructional videos and PowerPoint presentations (PPTs) and engage in assignments, projects, and various activities aimed at improving learning outcomes, all tailored to your convenience.

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Why Enroll In Course?

Undertaking Advanced Predictive Modeling in R Certification Training can enhance your data modeling, machine learning, and predictive analytics skills, while providing you with hands-on experience with R programming. This training can also improve your career prospects by differentiating you from other candidates and providing you with a competitive edge in the job market. Overall, this certification can provide you with the skills, knowledge, and recognition needed to advance your career in data science and predictive analytics.

Training Features

Live Interactive Learning

  World-Class Instructors

  Expert-Led Mentoring Sessions

  Instant doubt clearing

Lifetime Access

  Course Access Never Expires

  Free Access to Future Updates

  Unlimited Access to Course Content

24x7 Support

  One-On-One Learning Assistance

  Help Desk Support

  Resolve Doubts in Real-time

Hands-On Project Based Learning

  Industry-Relevant Projects

  Course Demo Dataset & Files

  Quizzes & Assignments

Industry Recognized Certification

  CertHippo Training Certificate

  Graded Performance Certificate

  Certificate of Completion

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Course Curriculum

Learning Objectives: This programmer will provide you with a basic introduction to statistics as well as effective test and exploratory analytic techniques.

Topics:

  • Covariance & Correlation

  • Central Limit Theorem

  • Z Score

  • Normal Distributions

  • Hypothesis

Hands On: Calculating statistical metrics such as mean, median, and mode, as well as creating bespoke visuals for building data understanding in relation to statistical parameters.

Learning Objectives: This programmer will provide you with a basic introduction to statistics as well as effective test and exploratory analytic techniques.

Topics:

  • Covariance & Correlation

  • Central Limit Theorem

  • Z Score

  • Normal Distributions

  • Hypothesis

Hands On: Calculating statistical metrics such as mean, median, and mode, as well as creating bespoke visuals for building data understanding in relation to statistical parameters.

Learning Objectives: This session will provide you a quick overview of basic regression and multiple regression, as well as how to illustrate the results graphically

Topics:

  • Bivariate Data

  • Quantifying Association

  • The Best Line: Least Squares Method

  • The Regressions

  • Simple Linear Regression

  • Deletion Diagnostics and Influential Observations

  • Regularization

Hands On: Ridge and Lasso regression implementation.

Learning Objectives:The purpose of this module is to introduce you to linear regression and help you improve the model's fit by performing essential transformations, checking for overfitting and underfitting, and identifying and treating outliers.

Topics:

  • Model fitting using Linear Regression

  • Performing Over Fitting & Under Fitting

  • Collinearity

  • What is Heteroscedasticity?

Learning Objectives: In this session, you will learn about the challenges associated with the Linear Probability Model, as well as logistic regression and its diverse applications in business.
Topics:

  • 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

  • Interpretation

  • Ordered Categorical Variable

Hands On: Build a logistic regression model to classify the data.

Learning Objectives: This lesson will take you further into logistic regression and teach you how to use it on a variety of datasets.
Topics:

  • 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

Hands On: Create ZINB and Hurdle regression model.

Learning Objectives: This lesson will teach you how to address missing values and how to impute them using various procedures.

Topics:

  • 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

  • Imputation

  • Regression Substitution Method

  • K-Nearest Neighbour Approach

  • Maximum Likelihood Estimation

  • EM Algorithm

  • Single and Multiple Imputation

  • Little’s Test for MCAR

Hands On: Implement KNN model and perform single and multiple imputation.

Topics:

  • Need for Forecasting

  • Types of Forecast

  • Forecasting Steps

  • Autocorrelation

  • Correlogram

  • Time Series Components

  • Variations in Time Series

  • Seasonality

  • 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

Hands On: Implement Exponential Smoothing and ARIMA model for time series forecasting.

Learning Objectives: In this module, you will learn about Seasonality, Trend Analysis and decaying the factors over the time. 

Topics:

  • 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

Learning Objectives: In this lesson, you will gain a thorough understanding of Dimensionality Reduction and will study and implement a number of the most essential Dimensionality Reduction techniques.

Topics:

  • 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



Learning Objectives: You will learn about Churn analysis and Regression on time series data with a time component in this subject.

Topics:

  • Time-to-Event Data

  • Censoring

  • Survival Analysis

  • Types of Censoring

  • Survival Analysis Techniques

  • PreProcessing

  • Elastic Net


Hands On: Do PCA preprocessing and implement Elastic Net model.

Learning Objectives: You will learn about Churn analysis and Regression on time series data with a time component in this subject.
Learning Objectives: In this module, you will learn about Churn analysis and Regression on time series data with time components. 


Topics:

  • Time-to-Event Data

  • Censoring

  • Survival Analysis

  • Types of Censoring

  • Survival Analysis Techniques

  • PreProcessing

  • Elastic Net

Hands On: Do PCA preprocessing and implement Elastic Net model.

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Certification

After completing the project successfully (as reviewed by a CertHippo expert), you will be given CertHippo Advanced Predictive Modeling expert credential. CertHippo certification is widely recognized in the industry, and we are the chosen training partner for many multinational corporations, including Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mind Tree, BNYMellon, and others. Please be confident.

Online Training FAQs

With CertHippo, you will never miss a lecture! You can select one of two options:

  • View the recorded session of the class available in your LMS.

  • You can attend the missed session, in any other live batch

With CertHippo, you will never miss a lecture! You can select one of two options:

  • View the recorded session of the class available in your LMS.

  • You can attend the missed session, in any other live batch.

To maintain Quality Standards, we have a restricted number of participants in a live session. As a result, participation in a live class without enrollment is not feasible. But, you may listen to a sample class recording to get a sense of how the lessons are run, the quality of the teachers, and the degree of engagement in a class.

CertHippo instructors are all industry practitioners with at least 10-12 years of relevant IT experience. These are subject matter experts who have been educated by CertHippo to provide participants with an amazing learning experience.

You can give us a CALL at +1 302 956 2015 (US) OR email at info@certhippo.com

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Course Description

About the course

This course will expose you to some of the most extensively used predictive modeling techniques as well as the underlying ideas behind them. Predictive modeling is growing as a competitive approach in many business areas, and it helps distinguish high-performing firms. Predictive analytics issues are typically solved using models such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks. Regression models help us understand the correlations between these variables and how they might be used to make decisions

What are the objectives of this course ?

You will be able to do the following after completing this training:

  • Understand Basics of Statistics using R

  • Explain Regression

  • Understand Simple, Multiple, Advanced and Logistic Regression

  • Perform model fitting using Linear Regression

  • Explain What is Heteroscedasticity?

  • Understand Binary Response Variable and Linear Probability Model

  • Explain Imputation

  • Understand Forecasting

  • Learn Neural Networks

  • Explain Dimensionality Reduction

  • Understands the algorithms associated with Dimensionality Reduction

  • Understand Survival Analysis

Why Learn Advanced Predictive Modeling using R?

This course will introduce you to some of the most often used predictive modeling tools and their underlying concepts. It is intended for anybody interested in utilizing data to obtain insights and make better business decisions. The concepts covered in this course are used in all functional areas of corporate organizations, including accounting, finance, human resource management, marketing, operations, and strategic planning.

Who should go for this course?

The following professionals can take up this course:


  • Developers aspiring to be a 'Data Scientist'

  • Analytics Managers who are leading a team of analysts

  • 'R' professionals who want to capture and analyze Big Data

  • Business Analysts who want to understand Machine Learning (ML) Techniques

What are the prerequisites for this course?

To take this course, you must have a basic understanding of R.

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Selenium Certification

After completing the project successfully (as reviewed by a CertHippo expert), you will be given CertHippo Advanced Predictive Modeling expert credential. CertHippo certification is widely recognized in the industry, and we are the chosen training partner for many multinational corporations, including Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mind Tree, BNYMellon, and others. Please be confident.

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