Data Science-ForecastingTime series Using XLMiner,RTableau

Data Science-Forecasting/Time series Using XLMiner,R&Tableau, available at $49.99, has an average rating of 4.55, with 33 lectures, 1 quizzes, based on 90 reviews, and has 1354 subscribers.

You will learn about 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 This course is ideal for individuals who are 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. It is particularly useful 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.

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

Summary

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

Price: $49.99

Average Rating: 4.55

Number of Lectures: 33

Number of Quizzes: 1

Number of Published Lectures: 33

Number of Published Quizzes: 1

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 34

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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 Curriculum

Chapter 1: Forecasting Introduction

Lecture 1: Forecasting Introduction and Agenda for Introduction

Chapter 2: Forecasting Using R and XL Miner

Lecture 1: Why Forecasting, types of Forecasts

Lecture 2: Who Forecasts ?

Lecture 3: Forecasting Strategy-Defining goal

Lecture 4: Forecasting-Data Collection and Various components

Lecture 5: Forecasting Seasonal, Trend, Random components

Lecture 6: Forecasting-Data Exploration & Visualization

Lecture 7: Forecasting-Data Visualization Principles

Lecture 8: Forecasting-Error measures

Lecture 9: Exploratory Data Analysis Using Walmart Footfalls Example Part-1

Lecture 10: Exploratory Data Analysis Using Walmart Footfalls Example Part-2

Lecture 11: Evaluating Predictive Accuracy

Lecture 12: Forecasting Different Methods

Chapter 3: Forecasting Model Based Approaches

Lecture 1: Forecasting Methods-Linear Model

Lecture 2: Forecasting Methods-Exponential, Quadratic and Additive Seasonality Models

Lecture 3: Forecasting Methods- Additive seasonality with trend,Multiplicative seasonality

Lecture 4: Forecasting-Irregular Components.

Lecture 5: Recap Understanding Forecasting

Chapter 4: Forecasting Model Based Approaches Using R

Lecture 1: Forecasting Model Based Approaches Using R-Part 1

Lecture 2: Forecasting Model Based Approaches Using R-Part 2

Chapter 5: Forecasting Data Driven Approaches

Lecture 1: Forecasting Autocorrelation Model

Lecture 2: Forecasting-Model Based Approach VS Data Driven Approach

Lecture 3: Forecast Methods based on Smoothing

Lecture 4: Forecast Methods Exponential Smoothing

Lecture 5: Forecast Data Driven- Holts and Winter Method

Lecture 6: Forecast Data Driven-Seasonal Indexes

Lecture 7: Forecast Seasonal Indexes,Centered Moving Average Hands On

Lecture 8: Forecasting -Logistic Regression using XLminar

Chapter 6: Forecasting Data Driven Approach Using R

Lecture 1: Run Package and Load Data

Lecture 2: Using R Part 1

Lecture 3: Using R Part 2

Chapter 7: Forecasting using Tableau

Lecture 1: Forecasting using Tableau

Lecture 2: Whats Next..?

Instructors

ExcelR Solutions
Pioneer in professional management trainings & consulting

Rating Distribution

1 stars: 6 votes

2 stars: 5 votes

3 stars: 11 votes

4 stars: 18 votes

5 stars: 50 votes

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