Advance Predictive Modelling in R CourseOverview


This course will expose you to the most used advanced predictive modelling in R approaches and their fundamental ideas. In many commercial areas, predictive modelling is developing as a competitive approach that may distinguish high-performing organisations. Frequently used predictive analytics problem-solving models include multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks. Regression models enable us to comprehend the correlations between these variables and how those relationships might be used for decision making.

  • This course is meant for anybody interested in gaining insights from data and making better business choices.
  • The concepts covered in this course are applicable to all functional areas of business organisations, including accounting, finance, human resource management, marketing, operations, and strategic planning, among others.
  • KVCH is a renowned R programming training facility that provides the most effective R programming instruction to students seeking employment in MNCs and corporate giants.
  • R users are increasing by around 40%, and enterprises are integrating it into their daily operations.
  • Google, Bing, Facebook, Uber, Twitter, and Ford are all employers.
  • Utilised for tasks such as data cleansing, forecasting, and the creation of mind-blowing graphics.
  • R is a highly sought-after data analytics talent.
  • Obtain hands-on R programming instruction and achieve professional certification.

Our advanced predictive modelling in R Programming Training at KVCH focuses mostly on developing R language expertise. So, why are you still waiting? Enrol in the top R Programming certification course and secure a job with less investment.

Advanced Predictive Modelling in R

After completing this programme, you will have the ability to:

  • Learn Statistics Fundamentals with R
  • Describe Recession
  • Learn about Simple, Multiple, Advanced, and Logistic Regression
  • Apply Linear Regression to Model Fitting.
  • Explain What is the meaning of heteroscedasticity?
  • Acquire Knowledge of the Binary Response Variable and Linear Probability Model
  • Explain Imputation
  • Comprehend Forecasting
  • Understand Neural Networks
  • Description of Dimensionality Reduction
  • Comprehends the methods involved in Dimensionality Reduction
  • Appreciate Survival Analysis

Following the completion of your R Programming training course, your career will flourish. With enticing compensation packages and titles, your career will surge, and you will advance by standing out from the crowd.

  • You will get a deeper understanding of programming and its implementation.
  • Enhanced understanding of the web development framework to rapidly create dynamic websites.
  • Increased opportunities to work for prominent software businesses such as Infosys, Wipro, Amazon, TCS, and IBM, among others.
  • Learn how to design, build, test, and deploy desktop, mobile, and bespoke web apps.
  • Design and enhance testing and maintenance methods and operations

R is one of the fastest-growing programming languages, and its adoption has been successful for over 25 years. This result also demonstrates the programming language r's tremendous future potential.

  • Data Science has a median base income of $110000 and 4,524 job vacancies, according to Glassdoor.
  • It is the fastest-growing technology job sector at now.
  • Data Scientist, Analytics Manager, Database Administrator, Data Engineer, etc. are examples of job titles.
  • Your income will rise as your expertise and experience develop.

R finally proved to be a cost-effective, customizable, and expandable statistical analysis platform. There are various packages/libraries in R that include numerous useful functions that you may utilise.

  • R is regarded as an excess of individuals entering the area of analytics.
  • Nearly forty percent of those who completed this specialty began a new job.
  • Nearly 20% received a raise or promotion.
  • The statistical community's preferred computer environment is now R.
  • The number of users using R increases by 40%

Our advanced predictive modelling in R training course is recommended for beginners and experts interested in working in the analytics business. R is not just the most popular open-source analytical tool, but also the most popular analytics tool in the world.

  • There are usually sufficient employment opportunities for R programmers.
  • Infosys, Spectra, IBM, TCS, QA Infotech, and many more, are among the top industries recruiting in R programming.
  • R is appropriate for all IT workers, including those in Big Data analytics, business analytics, and scientific research.
  • Inadequate analytics resources exist for professionals with R programming expertise.

You will obtain a Certificate that you can share with potential employers and your professional network upon completion of all courses and the practical project.

  • Our certification is globally recognised.
  • Increases the value of your CV, allowing you to compete for top positions.
  • Obtain immediate employment by gaining the attention of prospective employers.
  • Additionally, our specialists provide interview advice.
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Advanced Predictive Modelling in R Curriculum


Topics:
  • Covariance & Correlation
  • Central Limit Theorem
  • Z Score
  • Normal Distributions
  • Hypothesis
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Topics:
  • Bivariate Data
  • Quantifying Association
  • The Best Line: Least Squares Method
  • The Regressions
  • Simple Linear Regression
  • Deletion Diagnostics and Influential Observations
  • Regularization
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Topics:
  • Model fitting using Linear Regression
  • Performing Over Fitting & Under Fitting
  • Collinearity
  • What is Heteroscedasticity?
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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
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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
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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
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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
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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

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

Topics:
  • Time-to-Event Data
  • Censoring
  • Survival Analysis
  • Types of Censoring
  • Survival Analysis Techniques
  • PreProcessing
  • Elastic Net

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Advanced Predictive Modelling in R Features

Instructor-led Live SessionsInstructor-led Live Sessions

KVCH experts with in-depth knowledge create a focused learning environment by presenting learners with real-world industry problems and focusing on solutions.

Live Training SessionsLive Training Sessions

During our certified training, seasoned instructors and industry experts conduct remote sessions to share their extensive knowledge with the learners.

Flexible Curriculum Flexible Curriculum

Professionals can obtain in-depth knowledge of cutting-edge Advanced predictive modelling in R training by taking advantage of the availability of specialised certificates.

 Expert Support Expert Support

Through a ticketing system that operates around the clock, our technical support staff is available to answer any questions you may have.

Certification Certification

Upon finishing the course and the assigned tasks, you will be awarded a certificate from KVCH, recognising your accomplishment as an Advanced predictive modelling in R expert.

Assignments Assignments

There is a quiz at the end of each lesson that must be completed before the next lesson begins to test your understanding.

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Advanced Predictive Modelling in R Certification

Earn your certificate

On successful completion of the training, you are awarded with a Certificate in Advanced predictive modelling in R. The certificate is recognised by top companies and helps in career growth.

KVCH Advanced Predictive Modelling in R Certificate holders work at various companies like (TCS, Accenture, Infosys) etc.

Share your achievement

Once you get your certificate, you can share it on your online profiles like LinkedIn. Sharing your certification with your connections will help you acquire your dream job.

Advanced Predictive Modelling in R FAQs

Can you describe the course briefly ?

This course will expose you to the most used predictive modelling approaches and their fundamental ideas. In many commercial areas, predictive modelling is developing as a competitive approach that may distinguish high-performing organisations. Frequently used predictive analytics problem-solving models include multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks. Regression models enable us to comprehend the correlations between these variables and how those relationships might be used for decision-making.

Why Should You Learn Advanced Predictive Modeling with R ?

This course is meant for anybody interested in gaining insights from data and making better business choices. The concepts covered in this course are applicable to all functional areas of business organisations, including accounting, finance, human resource management, marketing, operations, and strategic planning, among others.

Who should enrol in this class ?

These professions can enrol in this course:

1. Developers who want to become "Data Scientists"
2. Managers of Analytics who oversee a group of analysts.
3. R specialists interested in capturing and analysing Big Data
4. Business Analysts interested in Machine Learning (ML) Techniques.

What prerequisites does this course require ?

A fundamental understanding of R is required to enrol in this course.

Does the employment programme guarantee me a job ?

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  • Completely tailor-made curriculum.
  • Post training support and query management.
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