Data Science-Forecasting&Time series Using XLMinerR&Tableau

Course Feature

Cost

Paid
Provider

Udemy
Certificate

Paid Certification
Language

English
Start Date

2018-03-01
Learners

No Information
Duration

No Information
Instructor

ExcelR Solutions

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4.2

1,345 Ratings

This course on Forecasting using XLminar, Tableau, and R is designed to cover the majority of capabilities from an Analytics & Data Science perspective. Learn about scatter diagrams, autocorrelation functions, confidence intervals, and more, all required for understanding forecasting models. Discover the usage of XLminar, R, and Tableau for building forecasting models, as well as the science behind forecasting and forecasting strategies. Plus, learn about forecasting models such as AR, MA, ES, ARMA, ARIMA, and more, and how to accomplish the same using the best tools. Finally, explore Logistic Regression and Forecasting Techniques such as Linear, Exponential, Quadratic Seasonality models, Linear Regression, Autoregression, Smoothing Methods, Seasonal Indexes, and Moving Average.

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Data Science-Forecasting&Time series Using XLMinerR&Tableau

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Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 25th, 2023]

This course on Forecasting and Time Series Using XLMinerR and Tableau is designed to provide students with a comprehensive overview of the capabilities of analytics and data science. Students will learn about scatter diagrams, autocorrelation functions, and confidence intervals, which are all essential for understanding forecasting models. Additionally, students will gain an understanding of the usage of XLminar, R, and Tableau for building forecasting models, as well as the science behind forecasting, forecasting strategies, and how to accomplish the same using XLminar and R. Furthermore, students will learn about forecasting models such as AR, MA, ES, ARMA, ARIMA, and how to use the best tools to accomplish them. Additionally, students will learn about logistic regression and how to accomplish the same using XLminar. Finally, students will gain an understanding of forecasting techniques such as linear, exponential, quadratic seasonality models, linear regression, autoregression, smoothing methods, seasonal indexes, and moving averages.

Course Syllabus

Forecasting Introduction

Forecasting Using R and XL Miner

Forecasting Model Based Approaches

Forecasting Model Based Approaches Using R

Forecasting Data Driven Approaches

Forecasting Data Driven Approach Using R

Forecasting using Tableau

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