Tableau Course Content
Introduction to Data Visualization and Power of Tableau
What is data visualization, Comparision and benefits against reading raw numbers, Real usage examples from various business domains, Some quick powerful examples using Tableau without going into the technical details of Tableau
Architecture of Tableau
Installation of Tableau Desktop, Architecture of Tableau, Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane etc), How to start with Tableau, Ways to share and exporting the work done in TableauHands-on Exercise – Play with the tableau desktop, interface to learn its user interface, Share an existing work, Export an existing work
Working with Metadata & Data Blending
Connection to Excels, PDFs and Cubes, Managing Metadata and Extracts, Data Preparation and dealing with NULL values, Data Joins (Inner, Left, Right, Outer) and Union, Cross Database joining, Data BlendingHands-on Exercise – Connect to an excel sheet and import data, Use metadata and extracts, Handle NULL values, Clean up the data before the actual use, Perform various join techniques, Perform data blending from more than one sources
Creation of sets
Marks, Highlighting, Sort and Group, Working with Sets (Creation of sets, Editing sets, IN/OUT, Sets in Hierarchies)Hands-on Exercise – Create and edit sets using Marks, Highlight desired items, Make groups, Applying sorting on result, Make hierachies in the created set
Working with Filters
Filters (Addition and Removal), Filtering continuous dates, dimensions, measures, Interactive FiltersHands-on Exercise – Add Filter on data set by date/dimensions/measures, Use interactive filter to views, Remove some filters to see the result
Organizing Data and Visual Analytics
Formatting Data (Labels, Annotations, Tooltips, Edit axes), Formatting Pane (Menu, Settings, Font, Alignment, Copy-Paste), Trend and Reference Lines, Forecasting, k-means Cluster Analysis in TableauHands-on Exercise – Apply labels, annotations, tooltips to graphs, Edit the attributes of axes, Set a reference line, Do k-means cluster analysis on a dataset
Working with Mapping
Coordinate points, Plotting Longitude and Latitude, Editing Unrecognized Locations, Custom Geocoding, Polygon Maps, WMS: Web Mapping Services, Background Image (Add Image, Plot Points on Image, Generate coordinates from Image)Hands-on Exercise – Plot latitude and longitude on geo map, Edit locations on the map, Create custom geocoding, Use images of a map and plot points on it, find coordinates in the image, Create a polygon map, Use WMS
Working with Calculations & Expressions
Calculation Syntax and Functions in Tableau, Types of Calculations (Table, String, Logic, Date, Number, Aggregate), LOD Expressions (concept and syntax), Aggregation and Replication with LOD Expressions, Nested LOD Expressions
Working with Parameters
Create Parameters, Parameters in Calculations, Using Parameters with Filters, Column Selection Parameters, Chart Selection ParametersHands-on Exercise – Create new parameters to apply on a filter, Pass parameters to filters to selet columns, Pass parameters to filters to select charts
Charts and Graphs
Dual Axes Graphs, Histogram (Single and Dual Axes), Box Plot, Pareto Chart, Motion Chart, Funnel Chart, Waterfall Chart, Tree Map, Heat Map, Market Basket analysisHands-on Exercise – Plot a histogram, heat map, tree map, funnel chart and others using the same data set, Do market basket analysis on a given dataset
Dashboards and Stories
Build and Format a Dashboard (Size, Views, Objects, Legends and Filters), Best Practices for Creative and Interactive Dashboards using Actions, Create Stories (Intro of Story Points, Creating and Updating Story Points, Adding Visuals in Stories, Annotations with Description)Hands-on Exercise – Create a dashboard view, Include objects, legends and filters, Make the dashboard interactive, Create and edit a story with visual effects, annotation, description
Integration of Tableau with R and Hadoop
Introduction to R Language, Applications and Use Cases of R, Deploying R on Tableau Platform, Learning R functions in Tableau, Integration with HadoopHands-on Exercise – Deploy R on tableau, Create a line graph using R interface, Connect tableau with Hadoop and extract data
R Programming Course Content
Introduction to R
R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.
R Functions, code compilation and data in well-defined format called R-Packages, learn about R-Package structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.
R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.
Matrices and Vectors
Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.
Reading data from external files
Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.
Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.
Analysis of Variance (ANOVA)
Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.
K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.
Association Rule Mining
Introduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.
Regression in R
Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.
Analyzing Relationship with Regression
Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit
Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.
Logistic Regression Mean, Logistic Regression in R.
Advance Logistic Regression
Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.
Receiver Operating Characteristic (ROC)
Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.
Kolmogorov Smirnov Chart
Data analysis with R, understanding the WALD test, MC Fadden’s pseudo R-squared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.
Database connectivity with R
Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.
Integrating R with Hadoop
Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.
R Case Studies
Logistic Regression Case Study In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.Multiple Regression Case Study You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.Receiver Operating Characteristic (ROC) case studyYou will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.
Project 1 – Tableau Interactive DashboardData Set – SalesObjective – This project is involved with working on a Tableau dashboard for sales data. You will gain in-depth experience in working with dashboard objects, learn about visualizing data, highlight action, and dashboard shortcuts. With a few clicks you will be able to combine multiple data sources, add filters and drill down specific information. You will be proficient in creating real time visualizations that are interactive within minutes.Upon completion of this project you will understand how to create a single point of access for all your sales data, ways of dissecting and analyzing sales from multiple angles, coming up with a sales strategy for improved business revenues.
Project 2Domain –
Crime Statistics (Public Domain)Objective –
The Project aims to show the types of crimes and their frequency that happen in the District of Columbia. Also to provide the details of the crimes like, the area/location and day of the week the crime has happenedProblem statement :
Police departments are often called upon to put more “feet on the street” to prevent crime and keep order. But with limited resources, it’s impossible to be everywhere at once. This visualization shows where crimes take place by type and which day of the week. This kind of information gives local police more guidance on where they should deploy their crime prevention efforts.
- Map should be plotted at Block site address level
- Show the Offense, Location and Date of Crime occurrence.
- Show the Number of incidents and frequency in percentage for each type of crime happened(Offense)
- Show each incident happened every month by week and weekday and by offense type
- The dashboard should have Crime type and District filters which will be applicable to all three sheets in the dashboard
- An action from Map should filter out the other two sheets accordingly
- An action from tree map and bar chart should highlight the remaining two sheets according to the selection
Project 3Domain –
Visual Mapping between Vaccination rate and Measles outbreakProblem statement :
Lab Environment :
- Plot measles outbreaks depending on the coverage of population
- Plot measles infection cases before 1st dose, between 1st and 2nd dose and after the 2nd dose of measles vaccination
- Plot the correlation between immunity when vaccination coverage is high within schools
- Plot correlation between poor urban areas which were not vaccinated at high rate and other areas which were vaccinated properly
Tableau Desktop (Better to use latest version which is 10.3 or any version later 10.0 will also have the impact)
R Programming Projects
Project 1Domain – Restaurant Revenue PredictionData set – SalesProject Description – This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real time industry experience on handling multiple use cases and derive the solution. This project gives insights about feature engineering and selection.
Project 2Domain – Data AnalyticsObjective – To predict about the class of a flower using its petal’s dimensions
Project 3Domain – FinanceObjective – The project aims to find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factors
Project 4Domain – Stock MarketObjective – This project focuses on Machine Learning by creating predictive data model to predict future stock prices