Tableau 10 and Tableau 9.3 Desktop, Server & Data Science

Course Curriculum

Chapter 1: Tableau Introduction

Lecture 1: Introduction about the trainer and overall Agenda

Lecture 2: Introduction – Why Tableau ?

Lecture 3: Tableau, Analytics – Ice breaker

Lecture 4: Tableau Products & Introduction to Architecture – Part 1

Lecture 5: Tableau Architecture Part 2

Lecture 6: Tableau Architecture Part 3

Lecture 7: How to download and install tableau

Lecture 8: An Introduction to Tableau Desktop and History of Tableau

Lecture 9: Explore Tableau "Start Page"

Lecture 10: Location Based Analytics using Tableau

Lecture 11: Tableau User Interface

Lecture 12: Basic Tableau Visualization

Chapter 2: Statistics for Tableau including Linear Regression, K-Means Clustering & R

Lecture 1: First Moment Business Decision

Lecture 2: Measures of Dispersion

Lecture 3: Barplot & Histogram

Lecture 4: Box Plot

Lecture 5: Tableau-Barplot, Color encoded, Nested, Stacked

Lecture 6: Histograms

Lecture 7: Histogram-Calculated Field

Lecture 8: Pareto Chart

Lecture 9: Overlaying Pareto Chart

Lecture 10: Barplot Dual Axis

Lecture 11: Barplot-Calculated Field

Lecture 12: Box Plot

Lecture 13: Scatter Diagram, Correlation Coefficient

Lecture 14: Confidence Interval-Part 1

Lecture 15: Scatter Plot using Tableau

Lecture 16: Confidence Interval-Part 2

Lecture 17: Simple Linear Regression

Lecture 18: Simple Linear Regression R

Lecture 19: Linear Regression Tableau-Part 1

Lecture 20: Linear Regression Tableau-Part 2

Lecture 21: Data Visualization Principles-Part 1

Lecture 22: Data Visualization Principles-Part 2

Lecture 23: Connecting to Data R

Lecture 24: Tableau – R connection

Lecture 25: K-means Clustering -Part 1

Lecture 26: K-means Clustering -Part 2

Chapter 3: Working with Data using Tableau including Data Extraction & Blending

Lecture 1: Data Interpreter-Pivot

Lecture 2: Customizing Views, Data Types, Fields

Lecture 3: Using Clipboard, Hierarchies, Saving -Sharing Metadata

Lecture 4: Data Extraction

Lecture 5: Data Blending

Lecture 6: Tableau Hadoop Integration & Twitter data Extraction using Tableau

Chapter 4: Various Charts in Tableau including Text Mining

Lecture 1: Trend Line Chart

Lecture 2: Tableau_Maps_Mark Types

Lecture 3: Line Chart Variants

Lecture 4: Text Table – Area Chart

Lecture 5: Various Charts-Part 1

Lecture 6: Pie Chart

Lecture 7: Bubble Chart

Lecture 8: Bullet Chart

Lecture 9: Waterfall Chart

Lecture 10: Reference Lines, Bands, Distributions

Lecture 11: Word Cloud

Chapter 5: Bonus Section…

Lecture 1: Word Cloud_Text Mining

Chapter 6: Forecasting/Time series/Trend lines in Tableau

Lecture 1: Forecasting using Tableau

Lecture 2: Forecasting-Why Forecasting & types of Forecasts

Lecture 3: Forecasting – Who Forecasts?

Lecture 4: Forecasting Strategy-Defining goal

Lecture 5: Forecasting-Data Collection,Various components

Lecture 6: Forecasting Seasonal, Trend, Random components

Lecture 7: Forecasting-Data Exploration & Visualization

Lecture 8: Forecasting-Data Visualization Principles

Lecture 9: Forecasting-Error measures

Lecture 10: Exploratory Data Analysis Using Walmart Footfalls Example Part-1

Lecture 11: Exploratory Data Analysis Using Walmart Footfalls Example Part-2

Lecture 12: Evaluating Predictive Accuracy

Lecture 13: Forecasting Different Methods

Lecture 14: Recap Forecasting Part-1

Chapter 7: 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: Forecasting – Linear Regression and Auto Regression

Lecture 6: Forecasting Autocorrelation Model

Lecture 7: Forecasting-Model Based Approach VS Data Driven Approach

Lecture 8: Forecasting-Understanding Moving Average

Lecture 9: Forecast Methods based on Smoothing

Lecture 10: Forecast Methods Exponential Smoothing

Chapter 8: Forecast Data Driven Approach

Lecture 1: Forecast Data Driven- Holts and Winter Method

Lecture 2: Forecast Data Driven-Seasonal Indexes

Lecture 3: Forecast Seasonal Indexes,Centered Moving Average Hands On

Lecture 4: Forecasting -Logistic Regression using XLminar

Chapter 9: Tableau Filtering

Lecture 1: Tableau Filtering_Part 1

Lecture 2: Tableau Filtering_Part 2