Time Series Analysis & Forecasting in R Programming Language
What Will You Learn?
- Upon completing these modules, participants will be able to perform comprehensive time series analysis in R, including identifying and modeling trends, seasonality, and stationarity.
- They will also gain proficiency in forecasting future values using both ARIMA and SARIMA models.
- This series also aim to equip learners with the skills to leverage R Markdown for creating professional and visually appealing code presentations.
- Furthermore, the series will cover techniques for enhancing the aesthetic appeal of R Markdown outputs, including image insertion, clickable links,
- alongside efficient handling of code execution, tables, and images.
Course Content
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Files For Hands-On Learning
MODULE 1 OF 3: FOUNDATIONAL COURSE REVISION (Click to see module content)
A foundational course in R programming for data analytics typically covers the essential skills needed to manipulate, analyze, and visualize data using R.
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Introduction: Why You Should Learn R Programming Language
00:00 -
Lesson 1, Practice 1: R and R Studio Explained with Practice
24:53 -
Lesson 2: Let’s Get Started with Some Installations
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Lesson 3, Practice 2: Let Us Code Together! It is Easier than You Imagine.
16:43 -
Lesson 4, Practice 3 with Note: Syntax- Rules of Writing Code in R Programming Language
01:02:28 -
Lesson 5: Best Practice in R Syntax
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Lesson 6, Practice 4: Assignment Solution in Syntax
15:19 -
Lesson 7, Practice 5: Data Structure in R- Vector, Matrices, List and Data Frame
01:17:16 -
Lesson 8, QUIZ 1: Syntax and Data Structure True or False Quizzes with Explanations
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Lesson 9, Practice 6: Assignment Solution in Data Structure
32:34 -
Lesson 10, Multiple Choice Questions on Syntax and Data Structure
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Lesson 11, Practice 7 with Note: Data Importation into R Studio
42:23 -
Lesson 12, Practice 8: Uploading files into R studio
03:53 -
Lesson 13: Work smart in R Studio Cloud
19:52 -
Lesson 14: Practice 9 with Note: Data Exploration
41:45 -
Lesson 15, Practice 10: Assignment Solution in Data Exploration
12:35 -
Lesson 16: Quiz on Data Exploration
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Lesson 17, Practical Quiz for Application
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Lesson 18, Practical Quiz for Application
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Lesson 19, Practice Field 1: 37 Question & Answer Test of Knowledge
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Lesson 20, Practice Field 2: 20 Questions of Application
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Lesson 22, Practice Field 4: 100 Pure Application Questions & Answers
MODULE 2 OF 3: R MARKDOWN- ADDING BEAUTY TO YOUR CODE PRESENTATION (Click to see module content)
This series aims to equip users with the skills to leverage R Markdown for creating professional and visually appealing code presentations. Objectives include mastering file management and knitting processes to generate HTML, Word, and PDF documents. Furthermore, the series will cover techniques for enhancing the aesthetic appeal of R Markdown outputs, including image insertion, clickable links, and basic website development principles, alongside efficient handling of code execution, tables, and graphs.
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File and Folder Creation in R Markdown
20:00 -
How to Knit into HTML and Word Document
20:00 -
How to Knit into PDF Document
23:18 -
Adding Aestheticism to HTML File in R
18:01 -
How to Create a Dashboard in R Markdown
13:05 -
Creating Clickable Link in R Markdown
10:52 -
Image Insertion in R Markdown
09:49 -
Running Code in R Markdown
00:00 -
Working with Table in R Markdown
14:42 -
R-Markdown Test of Knowledge 1
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R-Markdown Test of Knowledge 2
MODULE 3 OF 3: TIME SERIES ANALYSIS AND FORECASTING (Click to see module content)
Upon completing these modules, participants will be able to perform comprehensive time series analysis in R, including identifying and modeling trends, seasonality, and stationarity. They will also gain proficiency in forecasting future values using both ARIMA and SARIMA models.
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Time Series Analysis- General Overview
27:04 -
Time Series Analysis- Trend
27:04 -
Seasonality 1- Subseries
15:27 -
Seasonality 2- Weekly
12:09 -
Seasonality 3- Autocorrelation
10:17 -
Time Series Analysis- Stationarity
08:55 -
Forecasting with ARIMA Model
17:18 -
Forecasting with SARIMA Model
14:10 -
60 Practical-Based Quiz Questions on Time Series Analysis and Forecasting in R
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