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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How to create compelling analytical stories using infographics?

Kunal Jain
Kunal Jain Last Updated : 27 Aug, 2021
4 min read

Let me go back a few years:

After spending slightly more than a year in my previous role, I moved into a new role in my Organization. This new role was my first experience in Customer Management analytics. The amount of data was more than I had handled ever before. The impact I could have on my employers’ profitability was huge.

I worked hard to deliver good quality analysis but the impact from these analysis was lower than what I could have created.

This is a common situation many analysts face during their career.

The transition from being a number cruncher to a story teller (of course, based on numbers) is more difficult than it sounds. Thankfully, Infographic came to my rescue. Although, the purpose of infographics is to share content and increase visibility over the web, they can be used to create stories in effective manner.

storytelling wordle

You can find various tips on how to create good infographics over web, however the purpose of this article is to tell “How you can use Infographics to create compelling stories from your analysis?”

[stextbox id=”section”]What is an infographic?[/stextbox]

As per Wikipedia:

Information graphics or infographics are graphic visual representations of information, data or knowledge intended to present complex information quickly and clearly. They can improve  cognition by utilizing graphics to enhance the human visual system’s ability to see patterns and trends. The process of creating infographics can be referred to as data visualization, information design, or information architecture.

Simply put, you need to answer the following question:

If you had only one page to tell your story, how would you tell it?

[stextbox id=”section”]Elements of an infographic:[/stextbox]

Infographics are important because they change the way people find and experience stories – especially now, when more and more infographics are being used to create editorial content on the web. Infographics create a new way of seeing the world of data, and they help communicate complex ideas in a clear, crisp and beautiful manner.

A good infographic must:

  • present complex information quickly and clearly
  • integrate words and graphics to reveal information, patterns or trends
  • be easier to understand than words alone
  • be beautiful and engaging

.

[stextbox id=”section”]How can infographic help you create a story?[/stextbox]

You might almost think that what does an infographic have to do with creating and presenting a story? Well, it does! As a tool, it forces you to create and present your story on a single page. It ensures that you remove all the clutter around the story (in your mind and on paper as well) and present it in simple & graphical manner.

Doesn’t that sound like a story?

One of my mentors used to advice “If you can not present a project in 10 slides, you are not clear about what you have done.” Once I was comfortable creating story in 10 slides, the milestone moved to 3 slides!

With use of infographic, the challenge moves to a single page.

Let us take an example, Economist recently produced an interactive infographic on how Google Chrome has become most widely used browser. This is how it looks:

google_chrome3

Now imagine the number of slides you would have required to tell this story?

[stextbox id=”section”]So, how do create a story with an infographic?[/stextbox]

Just follow following steps:

infographic process flow

  1. Create headline and wire frames on a paper (preferably A3): The headline should summarize the project and wire frames should capture the story of the analysis.
  2. Populate message in each wire frame: In each of the frames, write the message you want to convey through the chart / graphic. Don’t draw the charts yet! Just see if the story is complete from customer / audience point of view.
  3. Remove / Combine unnecessary wire frames: Next, ask this question for each and every wire frame: “Can I remove this wire frame without impacting the story?” or “Can I narrate my story without this graphic?”. If the answer is “Yes”, then remove them. A few tips to make this easy for you:
    1. If you have performed an analysis, but it does not fit in the story, remove it.
    2. There is no rule on minimum number of wire frames. Lower the better! If one chart can tell the entire story, just use one.
    3. If you can combine multiple charts or frames, combine them. Since you will emphasize on one or two key charts in an infographic, they can be relatively complex. However, remember that complexity should be avoided to the extent possible.
  4. Represent using best charts: For each of the remaining frames, think hard about what is the best chart / graphic to represent this view. Some of the general guidelines like maximize data-ink ratio, selecting the right chart should be followed during this step. If need be, feel free to use variety of charts like geocharts, Venn diagram, Area chart etc.
  5. Provide priority wise space: Next, rank order each wire frame in order of importance. Once done, make sure that space allocated to the wire frame is in proportion of its importance. Most important chart in the story deserves the highest area on the paper.
  6. Narrate story in less than a minute: Speak out the story on this page in less than a minute. If it takes longer or is hard to comprehend, SIMPLIFY and re-structure until you achieve this.
  7. Create the infographic: Once happy with the story, as an optional step you can actually go ahead and create an infographic. There are multiple tools on the web to create the same.

Using this process, you can use infographics to create stories out of your analytical project. To start with, can you create an infographic summarizing this post?

If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our facebook page

 

Image 1 credit: residentsvoices.net

Business Analyticsinfographicsvisualization
Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Ram
Ram

Hi Kunal, This is great article. Thanks for sharing your thoughts. Thanks Ram

123

Deepak Rai
Deepak Rai

Hi Kunal, Its awesome!!!! Thanks for sharing. Thanks, Deepak Rai

123

sunil ray
sunil ray

Thanks Kunal...awesome...best content on story writing..

123

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