Learn about the basic statistics required to get started with Tableau. As part of this learn about the very first question which any management person would ask pertaining to business. Learn about the measures of central tendency. 

Learn about the basic statistics required to get started with Tableau. As part of this learn about the very second question which any management person would ask pertaining to business. Learn about the second moment business decision. 

Learn about the science behind using Bar plot & Histogram. One should know about the applications & uses of each visualisation before accomplishing a specific chart.

Learn about the various components of Box Plot & how to identify the outliers using this plot. Also learn about the quartiles, IQR, median, etc., of Box Plot. 

Learn about how to accomplish a simple visualisation using Bar plot & learn on how to make the visualisation tell different stories using a Bar plot by simply dragging the various dimensions & measures around on a worksheet.

Learn about the steps involved in generating histograms. Also learn about what kind of inferences can be drawn using histogram and explore the opportunities of skewness & thereby long tail effect.

Learn about steps involved in generating histogram by changing the number of bins. Experimenting by changing the bins in histogram, to derive meaningful insights, is pivotal to the success of drawing business inferences. 

Learn about the famous 80/20 rule, which says that 80% of problems are because of 20% of causes. Learn about how to generate a Pareto chart & how to draw reference lines. 

Learn about the famous 80/20 rule, which says that 80% of problems are because of 20% of causes. Learn about how to generate a Pareto chart & how to plot a curved line graph on top of the bar plot.

Learn about the steps involved in generating Bar Plots by having two axis instead of one. This is accomplished using a concept called as dual axis. 

Learn about the steps involved in creating a calculated field & using this to generate a Bar Plot for comparison. 

Learn about the steps involved in accomplishing a Box Plot. Interesting part is to learn about the various components of Box Plot and draw meaningful business insights. 

Learn about the concepts behind scatter diagrams, which are used to explain about the direction of relationship, strength of relationship & linearity of relationship between 2 variables. Also learn about how to objective way of evaluating strength of relationship using correlation coefficient value. 

Learn about the concepts of Population & Sample, how to determine sample size. Also learn about calculating the confidence interval using a real world case-study.

Learn about the various ways of generating scatter plot & how to draw inferences from the scatter plot in Tableau.

Learn using R on how to generate the Z value & how to manually calculate the confidence intervals using Z-distribution & t-distribution. 

Learn about simple linear regression concepts and various components which one needs to understand before building prediction models. Also learn about a real world case-study from Life Sciences Health Care industry to understand about building prediction model.

Learn about the use of R in building prediction model to understand on how to build an equation for prediction. Also learn on how to gage the strength of the model along with the possible transformations to improve the accuracy of the model. 

Learn on how to build simple linear regression using Tableau Analytics pane. Also learn about the log transformation option of Tableau to see whether the accuracy of the model increases. 

Learn about exponential & polynomial regression using Tableau. Learn about how to bring back the log of output variable to the original variable by taking exponential. Also learn about how will changing the degrees impact the polynomial regression. 

Learn about the data visualisation principles which are pivotal for successful reports generation. In order to gain the attention of the stakeholders one has to ensure that visualisation is done based on Tufte's principles, Lie Factor, etc.

Learn about the data visualization principles including consistent scales, standardized monetary values, presenting data in context, data-ink ratio, chart junks, Moire effect & Necker illusion. 

Learn about how to connect to a Microsoft Access data source using Tableau. Also learn about establishing connection between R and Tableau in a Windows machine. 

Learn on how to establish a connection between R and Tableau on a Macintosh Machine. Also learn about how to accomplish K-means clustering using Tableau by having a connection established with R.

Learn about what is clustering and why K-means clustering is such an important technique within Data Mining Unsupervised learning. 

Learn about K-Means clustering using R code and understand the output in finer details. 

Learn about the new feature of Tableau 9.3, which will make the life of most of the analysts simpler by identifying the discrepancies in a dataset and highlighting the auto corrections which have been made by Tableau automatically. 

Learn about how to change the data types within Tableau from dimensions to measures. Also learn about customising the different views along with Split function of Tableau. 

Learn about how to use clipboard features in Tableau, how hierarchies are created & changed. Also learn about how to save the metadata and how to share it. 

Learn about how to extract data from various data sources and how to refresh the changes, either incrementally or fully. 

Learn about how to work with multiple data sources at one go. Also learn about how to use different fields from the various data sources to accomplish a visualisation to solve business problems. 

Learn about the integration between Cloudera Hadoop and Tableau. Also learn about how to extract the data from Twitter using Tableau.

Learn about when to use trend line chart as part of time series analysis. Also learn about the visualisations pertaining to line chart which will help the business answer a wide variety of questions. 

Learn about the various mark types in maps using case studies. Learn about creating pie charts on maps and ranking states by sales using maps. 

Learn about generating a basic line chart and from there on learn about the advanced line chart using multiple panes in Tableau. Also learn about using calculated field while using a line chart.

Learn about two basic yet powerful default charts used in Tableau. Text table is to visualise the data in textual format. Also learn about the 2 variants of Area chart including discrete & continuous. 

Learn about the Highlight table, Heat Maps, how to change the colors, filtering and Circle plots, Tree maps & how to draw interesting insights. 

Learn about the Pie Chart and the use of this in various scenarios. Dos and Don'ts of the Pie chart are extremely imperative because this chart should be sparingly used in real world scenarios. 

Learn about the use of Bubble chart. Learn on how to generate this chart using the default options of Tableau and also learn on how to manually generate the same. 

Learn about the Bullet chart and how to set the various target values within the chart. Learn about the various shades of colors within this chart and its implications in drawing inferences. 

Learn how to make waterfall or Mario chart using a simple calculated field using the example of profit measure of a firm.

Learn about the various options within Analytics pane of Tableau, including reference lines, references bands & reference distributions to visualise the various business parameters.

Learn about how to generate word cloud, also called as tag cloud, manually using Tableau. There is no default chart for word cloud within Tableau. 

Learn about the science behind generating word clouds & sentiment analysis, including positive & negative word clouds. Also learn about Term Frequency & Term Frequency Inverse Document Frequency, the two key concepts used to generate the word clouds. 

What is forecasting understanding with respective to a time series data, Introduction about the trainer, course agenda for forecasting

Learn on why forecasting is so important and a few case studies of forecasting failures. Also learn about the various types of forecasts such as point, interval and density forecasts. 

Learn on why forecasting is so important and a few case studies of forecasting failures. Also learn about the various types of forecasts such as point, interval and density 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.

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.

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. 

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

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

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

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

Types of forecasts; Naïve forecast, distribution of error identifying which is more desirable, Old Lecture 142: Evaluating Predictive AccuracyTypes of errors its strengths and weaknesses, challenges with data quality, forecasting prediction interval for normal and non–normal data.

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

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

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

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

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

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

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

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

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

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  

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

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.

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.

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

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  

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

Learn about the 'include' & 'exclude' functionalities of filtering. Learn about relative date, range of dates, starting date, ending date, special & various filtering options on a date dimension. 

Learn about the filter options when we filter on dimensions & measures. Also learn about general, wildcard, condition, top. Also learn on how to apply a filter to all worksheets or only a few. 

Learn about setting a quick filter and also learn about the various options available to represent the quick filter, including single, multiple value list, dropdown & sliders.

Learn about creating a parameter and how to show parameter control to perform what-if analysis.

Learn about creating a parameter and how to use it in the calculated field, which can be used to show case the use of Tableau to perform what-if analysis in solving business problems pertaining to setting goals. 

Learn about filter action, highlight action & URL action through demonstration of the options using Tableau. Also learn about all the various options available in Filter actions.

Learn about the Highlight action & the various options are explored thoroughly in this tutorial. Also learn about URL action to navigate to a url in web browser upon clicking a specific entry on the map. 

Learn on how to create groups within dimensions by directly exploring the 'Create Group' option. Also learn about creating groups using visual grouping by selecting a specific dimensions or all dimensions. 

Learn on how to create sets to narrow the dimensions entries and also learn about how to combine multiple sets to generate a visualisation to solve complex business questions. 

Learn about Ad hoc calculations, which are also called as on-the-fly calculations. Also learn about simple numeric calculation & various components of calculation editor. 

Learn about the various Numeric calculations, String calculation including converting the customer name into a proper case, date calculations including creating a default date dimension. 

Learn about creating logic constructs including IF/ELSEIF/ELSE/END, CASE/WHEN/THEN/ELSE/END, IF/ELSE/END, IF/THEN/ELSE/END, etc.

Learn about aggregation within aggregation using LOD expressions. Also learn about the basic syntax of LOD expressions using FIXED scope keyword and Table scoped LOD expression.

Learn about the LOD expression scope keywords for aggregation granularity including FIXED LOD expression, INCLUDE & EXCLUDE LOD expressions using business scenarios. 

Learn about the table calculations such as Grand Totals for rows, columns, Percentage of totals. Also learn about Table (Across), Table (Down), Table (Across then Down), Table (Down then Across).

Learn on how to calculate 'Year over Year Growth', 'YTD Total', 'YTD Growth' & 'Compound Growth Rate' using Tableau and also learn on how to calculate the same using spreadsheet.