Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 44 Minute(s)
Language
English
Taught by
ExcelR EdTech
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
2 views
Course Overview
Forecasting Techniques-Linear,Exponential,Quadratic Seasonality models, Autoregression, Smooting, Holts, Winters Method
Forecasting using XLminar,Tableau,R is designed to cover majority of the capabilities from Analytics & Data Science perspective, which includes the following
- Learn about scatter diagram, autocorrelation function, confidence interval, which are all required for understanding forecasting models
- Learn about the usage of XLminar,R,Tableau for building Forecasting models
- Learn about the science behind forecasting,forecasting strategy & accomplish the same using XLminar,R
- Learn about Forecasting models including AR, MA, ES, ARMA, ARIMA, etc., and how to accomplish the same using best tools
- Learn about Logistic Regression & how to accomplish the same using XLminar
- Learn about Forecasting Techniques-Linear,Exponential,Quadratic Seasonality models,Linear Regression,Autoregression,Smootings Method,seasonal Indexes,Moving Average etc,...
Course Content
- 7 section(s)
- 33 lecture(s)
- Section 1 Forecasting Introduction
- Section 2 Forecasting Using R and XL Miner
- Section 3 Forecasting Model Based Approaches
- Section 4 Forecasting Model Based Approaches Using R
- Section 5 Forecasting Data Driven Approaches
- Section 6 Forecasting Data Driven Approach Using R
- Section 7 Forecasting using Tableau
What You’ll Learn
- Learn about different types of approaches using XLminer, R and Tableau
- Learn about the Forecasting Importance ,Forecasting Strategy which includes Defining goal, Data Collection, Exploratory Data Analysis, Partition Series, Pre-process Data, Forecast Methods, using various Plots.
- Learn about scatter diagram, correlation coefficient, confidence interval, which are all required for implementing forecasting techniques
- Learn about the various error measures such as ME, MAD, MSE, RMSE, MPE, MAPE, MASE
- Learn about Model based Forecasting Techniques such as Linear, Exponential, Quadratic, Additive Seasonality, Multiplicative Seasonality, etc.
- Learn about Auto Regressive Models for using errors to further strengthen the forecasting model used & also learn about Random walk & how to identify the same
- Learn about Data Driven approaches such as Moving Average, Simple Exponential Smoothing, Double Exponential Smoothing / Holts, Winters / HoltWinters
Skills covered in this course
Reviews
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AAVINASH PRADHAN
the concepts are very clear but has not used good data to work on i will recommend this course for better understanding of basic concepts
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YYhasreen Abrahim
overall a comprehensive curriculum exercises solutions on excel miner was very good. this course needs more explanation on time series forecast in R and Tableau. Little time spend on them. especially for R where the video skipped ahead.
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RRobert Samohyl
Really inadequate english from the professor, and therefore the automatic subtitles are often wrong. More time should be spent on free software, like R, Libre office, and google sheets. Why not have a last page for each lesson with a few references, more than just the help pages from R. Overall, the course is a good introduction to forecasting.
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AAustin Somlo
I learned a lot about time series, and the content was deep. The instructor responded to my questions in a timely manner and uploaded missing files. Thanks for the lessons.
