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Advanced Predictive Modelling in R Certification Training

Advanced Predictive Modelling in R Certification Training

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R offers a free and open source environment that is perfect for both learning and deploying predictive modelling solutions. This Certification Training is intended for a broad audience as both, an introduction to predictive models as well as a guide to applying them, covering topics such as Ordinary Least Square Regression, Advanced Regression, Imputation, Dimensionality Reduction etc. Readers will also be able to learn basics of Statistics, such as Correlation and Linear Regression Analysis.

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Online self paced classes

Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You'll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.

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Curriculum

Basic Statistics in R

Learning Objectives: In this module you will get a brief introduction to statistics and will conduct best test and exploratory analysis.

Topics:

Covariance & Correlation
Central Limit Theorem
Z Score
Normal Distributions
Hypothesis

Hands On: Calculating statistical parameters such as mean, median, mode and making custom visualizations for developing intuition of data with respect to statistical parameters.

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Ordinary Least Square Regression 1

Learning Objectives: In this module you will get a brief introduction of basic regression and multiple regression and will learn how to present the same 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.

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Ordinary Least Square Regression 2

Learning Objectives: The goal of this module is to dive you 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:

Model fitting using Linear Regression
Performing Over Fitting & Under Fitting
Collinearity
What is Heteroscedasticity?

Hands On: Perform exploratory data analysis and check for heteroscedasticity, perform remedial steps and transform the data and implement linear regression model.

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Logistic Regression

Learning Objectives: In this module, you will understand the problems related with Linear Probability Model, will be introduced to logistic regression and various uses of the same and its industry usage.

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.

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Advanced Regression

Learning Objectives: In this module, you will dig deeper into logistic regression and learn about more varied usage of logistic regression on various dataset.

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 Hurdel regression model.

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Imputation

Learning Objectives: In this module, you will learn about addressing missing values and how to impute it using various processes.

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.

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Forecasting 1

Learning Objectives: The goal of this module is to give an introduction on forecasting and time series data.

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.

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Forecasting 2

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

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Dimensionality Reduction

Learning Objectives: In this module, you will get a complete knowledge on Dimensionality Reduction and will discuss and apply few of the important algorithms associated with Dimensionality Reduction.

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

Hands On: Implement Principal component analysis and Boosting(ADAboost).

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Survival Analysis

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

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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Course Description

About the course

This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. Predictive modelling is emerging as a competitive strategy across many business sectors and can set apart high performing companies. Models such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks are frequently used in solving predictive analytics problems. Regression models help us understand the relationships among these variables and how their relationships can be exploited to make decisions

What are the objectives of this course ?

After the completion of this training, you will be able to:

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 Advance Predictive Modeling using R?

This course will introduce you to some of the most widely used predictive modelling techniques and their core principles which is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed in this course are applied throughout all functional areas within business organizations such as accounting, finance, human resource management, marketing, operations, strategic planning etc.

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 pre-requisites for this course?

Basic Understanding of R will be necessary in order to take up this course

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Certification

Edureka’s Programmer using Advanced Predictive Modelling in R Certificate Holders work at 1000s of companies like

Advanced Predictive Modelling in R Certification Training Certificate

John Doe

Programmer using Advanced Predictive Modelling in R

with Grade X

XYZ1234 31st Jul 2024

The Certificate ID can be verified at www.edureka.co/verify to check the authenticity of this certificate

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Testimonial Reviews

Manish DunaniBig Data Enthusiast

It is a very good experience to learn from edureka!.Faculty seems excellent in sharing real world ex ... Read More

Souvik KunduLearning to code the web, big data & cloud computing. Aspiring Developer

I'm currently enrolled in a lot of courses offered at Edureka, so I'm attending live classes and usi ... Read More

Rajendran GunasekarOffshore Delivery Manager at CSC, Chennai, India

Knowledgeable Presenters, Professional Materials, Excellent Customer Support what else can a person ... Read More

Abhishek MishraExperience in Hadoop and Big Data Analytic, Java developer.

Awesome faculty. Awesome explanation on topics. I really appreciate Edureka Support team. They are r ... Read More

Suman RajaIT Analyst at Tata Consultancy Services Greater Philadelphia Area Information Technology and Services

Definitely there is no doubt in saying that all the instructors at Edureka are industry experienced ... Read More

Pramod KunjuData Warehouse Guru, Greater Los Angeles Area

I found the big data course from Edureka to be comprehensive, and practical. Course instructor was v ... Read More

Video Reviews

Our learner Balasubramaniam shares his Edureka learning experience and how our training helped him stay updated with evolving technologies.

Vinayak shares his Edureka learning experience and how our Big Data training helped him achieve his dream career path.

Sriram speaks about his learning experience with Edureka and how our Hadoop training helped him execute his Big Data project efficiently.

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Features

You will undergo self-paced learning where you will get an in-depth knowledge of various concepts that will be covered in the course.

Towards the end of the course, you will be working on a project where you are expected to implement the techniques learnt during the course.

Each module will contain case study, which can be completed before going to next module.

You will get lifetime access to all the videos,discussion forum and other learning contents inside the Learning Management System.

edureka certifies you as an expert in Advanced Predictive Modeling based on the project reviewed by our expert panel.

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.

FAQ's

What if I miss a class?

You will never miss a lecture at Edureka! You can choose either of the two options:

View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.

Can I attend a demo session before enrollment?

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how are the classes conducted, quality of instructors and the level of interaction in a class.

Who are the instructors?

All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.

What if I have more queries?

Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co

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