Data Science-Forecasting/Time series Using XLMiner,R&Tableau
4.1 (86 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,318 students enrolled

Data Science-Forecasting/Time series Using XLMiner,R&Tableau

Forecasting Techniques-Linear,Exponential,Quadratic Seasonality models, Autoregression, Smooting, Holts, Winters Method
4.1 (86 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,318 students enrolled
Created by ExcelR Solutions
Last updated 3/2018
English [Auto]
Current price: $34.99 Original price: $49.99 Discount: 30% off
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This course includes
  • 6.5 hours on-demand video
  • 1 article
  • 4 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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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
Course content
Expand all 33 lectures 06:44:27
+ Forecasting Introduction
1 lecture 10:29

Learn about the trainer introduction & course introduction, which briefly explains about the concepts to be discussed through out the program. 

Preview 10:29
+ Forecasting Using R and XL Miner
12 lectures 02:25:34

Learn about course introduction, which briefly explains about the concepts to be discussed through out the program. 

Why Forecasting, types of Forecasts

Learn about who does the forecasting and about the dataset to be used for forecasting. Also learn about the various notations which are pivotal for strong foundation.

Who Forecasts ?

Learn about the 8 steps in brief for successful forecasting. Also deep dive on the first step which is Defining Goal, which is the single most important step of the 8-step forecasting strategy.

Forecasting Strategy-Defining goal

 Learn about step-2, Data collection in the 8-step forecasting strategy. Also you would gain in-depth knowledge on exploring the data series, which happens to be the 3rd step of the 8-step process. 

Forecasting-Data Collection and Various components

Learn about the various components of the forecasting data series including Seasonal, Trend & Random components. 

Forecasting Seasonal, Trend, Random components

Learn the rudimentary visualization used in forecasting; Scatter plot. Time Plot, Lag plot. Understand the correlation function, Standard error for lagged plot    

Forecasting-Data Exploration & Visualization

Understanding the ill effects of incorrect visualizations by reduced granulation or transformation. Why we need plots and their uses

Forecasting-Data Visualization Principles

Understanding forecast error through various plots, various measure of forecast errors and its dichotomies

Forecasting-Error measures

Preliminaries to forecasting; pre–processing, incorporating additional variables, data Partitioning in XL Miner

Exploratory Data Analysis Using Walmart Footfalls Example Part-1

Types of forecasts; Naïve forecast, distribution of error identifying which is more desirable.   

Exploratory Data Analysis Using Walmart Footfalls Example Part-2

Types of errors its strengths and weaknesses, challenges with data quality, forecasting prediction interval for normal and non–normal data,

Evaluating Predictive Accuracy

Introduction to two main methods of forecasting, Choice of the model based on Data Volatility

Forecasting Different Methods
14 questions
+ Forecasting Model Based Approaches
5 lectures 01:06:00

Various model based approaches to forecasting, Performing a linear Model forecast using XL Miner, Walk through the various steps involved in Linear Model.

Forecasting Methods-Linear Model

Walk through the steps in performing exponential forecast, Quadratic forecasting, Additive seasonality forecast using XL Miner

Forecasting Methods-Exponential, Quadratic and Additive Seasonality Models

Walk through the steps in performing Additive seasonality with trend forecast and multiplicative seasonality; Configuring visually the best model and ratifying with the least error model; Combining the training and Validation data to run the final model

Forecasting Methods- Additive seasonality with trend,Multiplicative seasonality

Addressing irregular components, building econometric models based on External information and domain knowledge

Forecasting-Irregular Components.

A complete recap of all the topics related to forecasting covered so far

Recap Understanding Forecasting
+ Forecasting Model Based Approaches Using R
2 lectures 26:28

Packages in R for reading file; creating a file access pop up using R script; create a data frame with dummy variable for seasonality; creating & assigning new variable to a data frame as a part of pre-processing; evaluating various prediction models for forecasting namely Linear Model, Exponential Model, Quadratic Model (Polynomial trend with two degrees); performing the predict function on the test data for each of the models and evaluating the RMSE(Root Mean Square Error)

Forecasting Model Based Approaches Using R-Part 1

Various other model based prediction strategies viz, Additive seasonality, Additive seasonality with Quadratic trend, Multiplicative seasonality; and evaluating for the model with the least RMSE; Running the model with least error using the complete data base for making the prediction; performing an ACF plot on the residuals from the model; Auto regressive model AR(1) on the residuals; check for significant information left over in the residuals of residuals

Forecasting Model Based Approaches Using R-Part 2
+ Forecasting Data Driven Approaches
8 lectures 01:45:22

Features of autoregressive models, Detailed study of residuals and its leftover information, Forecasting errors by AR model using the principle of Parcimony, iterative study of ‘residuals of residuals’ till no further information can be gleaned, Building the final model by plugging in the error factor

Forecasting Autocorrelation Model

The disadvantages and limitations of Model Based approaches, the underlying principle of model based and data based approach to forecasting

Forecasting-Model Based Approach VS Data Driven Approach

Naïve technique, Moving average approach with adequate ‘window width’ for smoothing the data, Key takeaways from the moving average approach; two types of calculations for moving averages and the math behind it

Forecast Methods based on Smoothing

Types of Exponential smoothing and their facets, the formula for Simple exponential smoothing considering decreasing weights for older data, understanding under smoothing and over smoothing

Forecast Methods Exponential Smoothing

Hands on with the Holt’s method with level and trend, hands on with Winter’s method with level trend and seasonality, importance of period for encompassing the seasonality in the Winter’s method, Forecasting using the model with the least error

Forecast Data Driven- Holts and Winter Method

De-seasoning the data using seasonal indexes and re–seasoning it using the same

Forecast Data Driven-Seasonal Indexes

Computing the Moving average even window width and walk thru of the steps in computing the seasonal indexes, Normalizing the seasonal indexes and drawing inferences for these, computing the de seasoned data using the respective seasonal indexes  

Forecast Seasonal Indexes,Centered Moving Average Hands On

Using the Sine and cosine component predictors for capturing the seasonality for daily data, logit regression using odds, Walk through the steps of using XL Miner for performing the Logistic regression

Forecasting -Logistic Regression using XLminar
+ Forecasting Data Driven Approach Using R
3 lectures 33:44

The various packages needed for forecasting; Loading the data on to R and converting the data to time series data; Splitting the data to training and validation data; Visualization of the time series data for analysis

Run Package and Load Data

Performing simple moving average using ARIMA; Selecting the appropriate MA value based on the Seasonality; performing the forecast() function and the accuracy function to check errors for training and test data; Performing simple exponential smoothing using only the level component of the time series data; Building Double exponential smoothing model using the level and trend component of the time series data; performing Holts winters model using level, trend and seasonality;

Using R Part 1

Performing the various exponential smoothing models using the HoltsWinters() command, so as to determine the optimum alpha, beta and gamma values corresponding the Level, Trend and seasonality; Performing the various smoothing techniques using commands line ses() for Simple exponential smoothing, holt() for Holts and hw() for holt Winters technique; Determining the best model based on the MAPE (Mean absolute percent error); Running the best model to from the list on the total data and forecasting one full cycle of seasonality

Using R Part 2
+ Forecasting using Tableau
2 lectures 16:50

Forecasting in Tableau using exponential smoothing; choosing date dimension in the column shelf and value dimension in the rows self; Selecting the analytics pane to choose the forecast option for obtaining the forecasts; Understanding the various options within the forecast; Viewing the errors of the forecast; forecasting for non time series numerical data; The eight types of forecasts models;

Forecasting using Tableau
Whats Next.....?
  • Download XLminer, R , RStudio before starting this tutorial
  • Download datasets folder in zip file which is uploaded in Session 1

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,...

Who this course is for:
  • All the IT professionals, whose experience ranges from '0' onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.