TIME SERIES ANALYSIS with R Course Bundle - 19 Courses in 1

Objectives

The training program is a well-defined structure and aims at catering up the requirements of the industry and the individuals who are looking for learning something new and building up their career according to the industry demand. There are a variety of skills that are going to be covered under this training program like predictive analytics, R language, data science, business analytics, and many others gives that are sufficient enough to security job as a data analyst, analyst, data scientist, business analyst, R programmer, machine learning analyst and many other opportunities that will open for individual once they will complete this training program successfully.

Course Highlights

The training program covers a great variety of skills that are going to be taught and covered under this training program. Some of the core skills that are covered are as follows:

Under the module of data science with R, a participant would learn about introduction to data science with the use of R language, understanding data science and its models, different tools, statistical computing, the purpose of R tool, a module on data visualization, creating pie charts, greeting bar charts, creating histograms, methods of using scatter plots, basic data visualization, vector values, regression, understanding linear regression, logistic regression, logistic regression, learning normal distribution curve, binomial distribution, analysis of covariance, time series analysis, least square method, random forest method, etc.

under this module of business analytics using the R language, one will learn about reference, introduction, course curriculum, discriminant analysis, introduction to analytics, the evolution of business analytics, ordinal data, business analytics lifecycle, understanding our language, manipulation in statistics basics, statistics probability and distribution, understanding visualization, etc.

Under this module of machine learning with R language what would learn about introduction to machine language, basic data manipulation in R language, linear regression, square, standard errors, multiple linear regression, generalized linear model and generalized least square, k nearest neighbor model, decision trees classifier and learning decision tree, random forest, k means clustering, naive Bayes classifier, neural networks, time series analysis, market basket analysis, gradient boosting machines, etc.

Project Highlights

There are many real-time and practical projects that a part of this training program that would help individuals to understand the concept in a better way. Some of the core projects that are part of this training program are as follows:

card purchase prediction with the use of R language would help an individual to understand the process behind our language and card purchase prediction.

A project on a predictive model for term deposit investment using R language which includes, problem statement, explanation, model development, model parameters, model improvement, model validation in deployment using R language.

A project on attrition predicting using random forest technique of employees that includes an introduction to the project, deciding the parameters, deployment of the project, validation of the project using R language.

A project on term deposit prediction using logistic regression CART algorithm includes an introduction to term deposit investment predictions, an overview of the project, development of the project.

a project on telecom customer churn prediction using R language includes an introduction to the project, loading of data set, developing model, validating the model.

A project on machine learning based on a prediction model using R studio includes an introduction, types of data, transformation and model, reference files, etc.

a project on forecasting using R that includes different principles of R, forecasting tools and techniques.

Project on logistic regression using R language.

A project on decision tree modeling using, market basket analysis using the R language, hypothesis testing using the R language, HR attrition analytics using the R language, etc.

With the help of these real-time projects and the case studies, one would be available to prepare himself for the real industry scenarios.