Advanced Predictive Modeling in R Certification Training
Access Duration - 365 Days
Awarded By Edureka
Course Material
4.5( 2 REVIEWS )
129 STUDENTS
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
Overview
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 https://www.glassdoor.com.
- 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 : 10053842Course Curriculum
| 1: Basic Statistics in R | |||
| Covariance & Correlation | |||
| Central Limit Theorem | |||
| Z Score | |||
| Normal Distributions | |||
| Hypothesis | |||
| 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 | |||
| Regularization | |||
| 3: Ordinary Least Square Regression 2 | |||
| Model fitting using Linear Regression | |||
| Performing Over Fitting & Under Fitting | |||
| Collinearity | |||
| 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 | |||
| Interpretation | |||
| 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 | |||
| Imputation | |||
| 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 | |||
| 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 | |||
| 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 | |||
| Censoring | |||
| Survival Analysis | |||
| Types of Censoring | |||
| Survival Analysis Techniques | |||
| PreProcessing | |||
| Elastic Net | |||
Nevaeh Lowe
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.
Maggie Shaw
The course focused completely on modeling techniques and core principles. I learned the basics of statistics using R, exploring Regression and understand forecasting.