R TIME SERIES ANALYSIS
Learning Path | 19 Course Series
This Time Series Analysis and Forecasting with R includes 19 courses with 73+ hours of video tutorials and One year access. In this course, we are going to learn about one of the most interesting aspects of technology, which is to predict something that can be certain or uncertain. We will be focusing on all sorts of time related data and its analysis using the R programming language with the help of machine learning and predictive analytics.
Offer ends in:
What you'll get
- 73+ Hours
- 19 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Courses: You get access to all 19 courses in the Projects bundle. You do not need to purchase each course separately.
- Hours: 73++ Video Hours
- Core Coverage: The main aim of this course is to learn how to use R in real forecasting and time series analysis.
- Course Validity: One year access
- Eligibility: Anyone serious about learning Time Series Analysis and Forecasting with R
- Pre-Requisites: Basic knowledge about R programming, statistics
- What do you get? Certificate of Completion for each of the 19 courses, Projects
- Certification Type: Course Completion Certificates
- Verifiable Certificates? Yes, you get verifiable certificates for each course with a unique link. These links can be included in your resume/LinkedIn profile to showcase your enhanced skills
- Type of Training: Video Course – Self-Paced Learning
Content
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MODULE 1: Statistics Essentials Training
Courses No. of Hours Certificates Details Data Science with R 6h 2m ✔ Business Analytics using R - Hands-on! 16h 21m ✔ Machine Learning with R 20h 25m ✔ -
MODULE 2: Projects based Learning
Courses No. of Hours Certificates Details Predictive Analytics Model for Term Deposit Investment with R Studio 3h 2m ✔ Project on R - Card Purchase Prediction 2h 28m ✔ Random Forest Techniques and R - Employee Attrition Prediction 1h 6m ✔ Predictive Analytics Model for Term Deposit Investment using CART Algorithm 1h 38m ✔ R Practical - Telecom Customer Churn Prediction 1h 27m ✔ Project on ML - Churn Prediction Model using R Studio 1h 22m ✔ Decision Trees - Bank Loan Default Prediction using R 1h 47m ✔ Time Series Analysis and Forecasting using R 4h 34m ✔ -
MODULE 3: Learning from Practicals & Case Studies
Courses No. of Hours Certificates Details Logistic Regression & Supervised Machine Learning with R 4h 14m ✔ Decision Trees Modeling using R 1h 4m ✔ Project - Market Basket Analysis in R 37m ✔ Project - Hypothesis Testing using R 3h 6m ✔ Project - Exploratory Data Analysis EDA using ggplot2, R and Linear Regression 2h 07m ✔ Project on R - HR Attrition and Analytics 2h 08m ✔ Machine Learning Project using Caret in R 1h 58m ✔ Cluster Analysis and Unsupervised Machine Learning - K-Means Clustering using R 43m ✔
Description
Time series analysis and forecasting can be defined as the approach to predict certainty based on the time sequence. It can also be considered the method required to determine the possibilities that could occur based on the data occupied through the last occurrence. R programming language can be used to implement Time Series Analysis and Forecasting in the application level which will eventually help to implement the concepts of data science. As the name suggests, the program has to be written in a manner that allows it to have ample power to determine the possibilities based on the past performance of any event or the data gathered from the event that has already occurred. It is very simple to understand, and folks with a good understanding of the R programming language will be able to write the logic to implement the concepts easily.
Sample Certificate
Requirements
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- All the topics that are not directly but indirectly concerned with R and that endorse the implementation of Time Series Analysis and Forecasting have been considered prerequisites for this course. You should be aware of certain technologies to facilitate your learning in this course.
- Data science is the first thing the trainees should be aware of before taking this course. As some of the concepts are based on data science, it will be helpful for the trainees to understand this.
- Machine learning is the next important thing. Folks with a good understanding of ML will find it easy to cover the units and projects solely based on it.
If you have a good understanding of these technologies, it will be very easy for you to complete this course. If you are new to these concepts, don’t worry—we have them covered in this course.
Target Audience
- Anyone willing to master the required approach to learn the implementation of Time Series Analysis and Forecasting can be the best target audience for this course.
- All the professionals, students, and trainers can be the best target audience for this course.
- Developers who are working in another programming language and want to learn about the complex aspects of the R programming language can be the best target audience for this course. They will learn to implement Time Series Analysis and Forecasting in R.
- The students interested in diving deep into R can also be the best target audience for this course and the best target audience for this course. They will be learning everything from a beginner’s point of view.
- The educators training folks in R programming language can also be the best target audience for this course. After completing this course, they can implement the concepts from scratch.
Course Ratings
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This tutorial was really helpful in understanding forecasting using R. The explanation was really easy to understand and the examples were really useful. The coverage of topics was good starting with the basics then going deep into the topics. they have covered simple forecasting methods, transformations, and adjustments, time series regressions and arima models
SHUSHANTH T
The course covers a wide range of areas into linear and multiple linear regression and decision tress. It also covers both theory and application using R neural network , time series analysis and gradient boosting machines. I have enjoyed learning in this course very much and found it useful in my work.
Tsui Man Kit
Overall it was a good. All the basics of R is being covered. Even the statistics is also explained. Things go parallel in this course. Beside R, I got hand on experience on stats too. I have to practice more in order to make it more handy. Moreover some extra case study is required in this program to make the things more practical. Overall satisfactory experience.
AVINASH KUMAR
The course offers a very good concept on basic statistics and probability, and how to use methods of each using R studio. I have found the course very useful in this sense. The course covers more advanced concepts such as how to utilize R to make a Least Square Estimation or a Regression analysis in general.
Aaron Bernal Lee