Predictive Modelling in R Online Training | R Certification Course | Edureka

Advanced Predictive Modelling in R Certification Training

Learn to develop Predictive Models in R using Advanced Analytics concepts i.e. Logistic and Linear Regression, Neural network , Forecasting and Churn Analysis.


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Why this course ?

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Online self - paced learning

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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Course Duration

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

Real-life Case Studies

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.

Assignments

Each module will contain practical assignments, which can be completed before going to next module.

Lifetime Access

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

Certification

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

Forum

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

Edureka's Advanced Predictive Modeling in R course will cover the Advanced Statistical and Analytical techniques. This course focuses on case study approach for learning various Analytical techniques and there will be a project to be done at the end of the course.

After the completion of Advanced Predictive Modeling in R course at Edureka, you will be able to: 

1. Understand the need for Statistical Predictive Modeling 

2. Work on Logistic and Linear Regression

3. Do Forecasting with Time series data and decomposition

4. Implement ARIMA models

5. Understand Survival Analysis and Neural Networks

Advanced Predictive Modeling in R will allow one to gain an edge over other Data analysts and present the data in a much better and insightful manner. 

This would help the learner to immediately implement these technique and create analysis and support decision making in the most scientific manner. 

This Advanced Analytics course is a must for anyone who aspires to get into Data Analytics and Decision science. The following professionals can go for this course : 

1. Developers who want to step-up as 'Data Scientists'

2. Analytics Consultants 

3. R / SAS / SPSS Professionals 

4. Data Analysts 

5. Information Architects and Data Engineers

6. Statisticians

Pre-requisites for learning Advanced Predictive Modeling is knowledge on R and exposure to basics of statistics.

For your practical work, we will help you setup Edureka's Virtual Machine in your System. This will be a local access for you. The required installation guide is present in LMS.

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.

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.

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

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.

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.

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.

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). 

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).

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.

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.

. Call a Course Adviser for discussing Curriculum Details . 1844 230 6361
As soon as you enrol into the course, your LMS (The Learning Management System) access will be functional. You will immediately get access to our course content in the form of a complete set of Videos, PPTs, PDFs and Assignments. You can start learning right away.
We do provide placement assistance by routing relevant job opportunities to you as and when they come up. To get notified on relevant opportunities, it is important that you fill out your profile details.

It is important to attend classes and complete assignments. Course completion is an important criterion based on which we screen profiles of learners interested in a particular job. Also, before your profile is shared with prospective employers, you will have to go through an internal assessment by edureka. So it is important to be well versed with the course concepts to become eligible for placement opportunities.
You can pay by Credit Card, Debit Card or NetBanking from all the leading banks. We use a CCAvenue Payment Gateway. For USD payment, you can pay by Paypal. We also have EMI options available.
You can Call us at +91 90660 20867 /1844 230 6362 ( US Tollfree ) OR Email us at sales@edureka.co . We shall be glad to assist you. 

  • Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka’s Advanced Predictive Modeling expert certificate.
  • edureka certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc. Please be assured.

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Advanced Predictive Modelling in R Certification Training