Data Analysis, Dashboards and Visualization - How to create powerful visualizations like a Zen Master

In today’s data economy and disruptive business environment, data is the new oil and data analysis with data visualization is vital for professionals and companies to stay competitive. Data Analysis and developing useful and interactive visualizations which provide insights may seem complex for a non -data professional. That should not be the case, thanks to various BI & data visualization tools. Tableau is one of the most popular one and widely used in various industries by individual users to enterprise roll out.

In this hands-on training session you will learn to turn your data into interactive dashboards, how to create stories with data and share these dashboards with your internal/external stakeholders. We will begin with practices for creating charts and storytelling utilizing best visual practices. Whether your goal is to explain an insight or let your audience explore data insights, using Tableau’s simple drag-and-drop user interface makes the task easy and enjoyable.

You will learn to use functionalities like Table Calculations, Sets, Filters, Parameters and predictive analytics using forecast functions . You will also learn mapping and other visual functionalities. We will demo few charts such as Waterfall charts, Pareto charts, Gantt Charts, Control Charts and Box and Whisker’s plot.

We will focus on data Visualization workflows and best practices using zen master techniques.


Outline/Structure of the Workshop

In this workshop, we’ll cover intermediate and advanced tableau functionality:

  1. Perform data analyses and create graphs from a real-world data set, using Tableau Public (free to use)
  2. Deeper Analysis – Trends, Clustering, Distributions, and Forecasting
  3. Table Calculations, Sets, Filters, Parameters and Pages
  4. Right and Wrong way to build Dashboards and Best Practices
  5. Examples of Tableau Stories and Dashboards best practices + Tips
    1. Choose Five or Fewer Colors for Your Dashboards
    2. Common Charts to use
    3. Include Comparisons for data – time series (annual, quarterly, etc)
    4. Use Segmentation for visuals
    5. Design Tips for Enhancing Your Visualizations
    6. Creating Efficient Workbooks
    7. Make Beautiful Charts in and Advanced ones - Waterfall, Pareto, etc

Learning Outcome

After this training session, you will gain skills to confidently analyze and visualize complex data sets with ease .You will be guided using data set to build a compelling and convincing dashboards and story. You will build those during the session with best visual practices. This session is for anyone who works with data and is interested in building dashboards and communicate insights about data with stories.

  • Create the most important visuals used in business analysis and transform data
  • Design business dashboards
  • Tell stories with data
  • Deliver compelling business analysis
  • Be fully prepared to examine, and present data for any purpose - scientific data or forecasts about profits/sales,hr,finance

Target Audience

Data Analysts, Business Analysts, Data Scientists, Intermediate to Advanced Users of Data, Professional from any background looking to analyze their own data and create meaningful insights and to communicate to their stakeholders

Prerequisites for Attendees

Software you will need on your laptop for the workshop:

1) Tableau Public: It's free to install, Go to this link and install Tableau Public

2) Tableau Reader: It's free too and can be used to open Tableau worksheets created by others.



schedule Submitted 3 years ago

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    • 480 Mins

      Modern statistics has become almost synonymous with machine learning, a collection of techniques that utilize today's incredible computing power. This two-part course focuses on the available methods for implementing machine learning algorithms in R, and will examine some of the underlying theory behind the curtain. We start with the foundation of it all, the linear model. We look how to assess model quality with traditional measures and cross-validation and visualize models with coefficient plots. Next we turn to penalized regression with the Elastic Net. After that we turn to Boosted Decision Trees utilizing xgboost. Along the way we learn modern techniques for preprocessing data.

    • jyotsna mehta

      jyotsna mehta - Creating alerts for asthma patients using a machine learning model

      jyotsna mehta
      jyotsna mehta
      Keva Health
      schedule 3 years ago
      Sold Out!
      45 Mins

      Digital health platforms help to create personalized care experiences for patients with chronic diseases. Patient apps can be created as a customizable mobile application with an AI enabled user interface that keeps patients engaged. It provides an intelligent decision support engine that helps patients follow physician recommended guidelines. The platform provides cloud based data driven machine learning models, powerful data analytics, real time insights via dashboards to optimize remote patient monitoring.

      Use cases and examples will be shown to the audience for chronic diseases such as asthma. First, dummy data for Asthma app mobile users will be shown. Next, use of external data from other sources will be explained and described. Finally, use of Machine learning approaches will be explained in Predicting risk of asthma attack.

      Each example will highlite different datasets and variables used, analytic approaches considered and its pros and cons, and how machine learning can predict and help in reducing visits to the emergency room or hospital for severe asthma patients.

      Data collection:

      Data includes patients peakflow, zones for asthma (yellow, green or red), symptoms, medications, symptom severity and answers to a 6 question survey (including number of hospital visits, medication change reason etc) all entered by the patient. External data includes air quality data , geographical location and pollen data.


      Machine learning methods work by uncovering hidden relationships between the target and features that classify or predict a particular outcome. In the context of telemonitoring via an app, supervised classification algorithms can be used to yield a classifier that distinguishes between a stable disease state and disease trajectory that it is indicative of incipient exacerbation on the basis of patient characteristics collected during a predefined time frame.

      Thus, from a machine learning prospective, telemonitoring data collected on a daily basis may be considered as features and each corresponding day's disposition with regard to exacerbation status (yes or no) can be considered as an outcome for predictive modeling. Within this framework, an initial predictive model can be continuously improved with increased numbers of cases.

      Examples of classification algorithms for building classification models include: adaptive Bayesian network, naive Bayesian classifier, and support vector machines. The naive Bayesian classifier looks at historical data and calculates conditional probabilities for the class values by observing the frequency of attribute values and of combinations of attribute values. The second algorithm used, adaptive Bayesian network, was based on Bayesian networks, which use a directed acyclic graph consisting of nodes, where each node represents an attribute. Corresponding to each node are instances with conditional probabilities. The conditional probability of an instance is calculated by the relative frequencies of the associated attributes in the training data. The third algorithm we used is a support vector machine algorithm which uses a subset of training data as support vectors. The support vectors are the closest instances to the maximum margin hyperplane, which provides the greatest separation between the classes. The support vectors are determined by constrained quadratic optimization.

      A receiver operating characteristic (ROC) will be shown to characterize comparative performance of classifying algorithms for asthma exacerbation prediction resulting from different training data sets . Our study demonstrates significant potential of machine learning approaches using telemonitoring data for early prediction of acute exacerbations of chronic health conditions.