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Reading list

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

Must read books on data visualization

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

It is not a co-incidence that all highly successful analyst have excellent data visualization skills. As a matter of fact, I think data visualization and creative story telling differentiates best of analysts from good analysts.

Be it design of a dashboard or a presentation after building an earth-shattering predictive model, visualization ends up being the face of all the hard work you have put in. Hence, it makes sense to spend time thinking about what is the best way of visualizing the data or insights from the data.

Sadly, not many analysts spend time on improving and thinking about visualization.

Look at it from this perspective, we sweat ourselves to get that extra percent of lift through our models or to make sure all the data flowing into the dashboards is right to the second and third decimal place. But, we usually spend a fraction of that time thinking about how this outcome would be presented and the impact it can create.

In this article, I am listing down the books, which have helped me improve immensely in data visualization. Further, some of these are resources for lifetime as I still continue to refer them, when I am stressing my grey cells over how to create that extra impact through my work.

must_read_data_visualization

For beginners:

[stextbox id=”section”]1. The Visual Display of Quantitative Information by Edward R. Tufte:[/stextbox]If there is one book you should definitely read on visualization, it is this book! First published in 1983, a classic book on charts, tables and various practices in design of data graphics. The book contains 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Some of the well-known concepts like data-ink ratio and sparklines were outlined by Tufte in this book. The book also mentions various sources of graphical deception. A definite must read for every analyst!

[stextbox id=”section”]2. Visualize This: The FlowingData Guide to Design, Visualization, and Statistics by Nathan Yau:[/stextbox]This book from the creator of flowingdata.com provides approaches to tell stories with data and offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design. It also provides details on tools that can be used to visualize data-native graphics for the Web and tools to design graphics for print.

[stextbox id=”section”]3. The Wall Street Journal Guide to Information Graphics: The Dos & Don’ts of Presenting Data, Facts, & Figures by Dona M. Wong: [/stextbox]A super practical guide to effective communication through graphs and charts. Concise, well written and easy to navigate, this book is a must read for people who have just started making presentations

[stextbox id=”section”]4. Show me the Numbers: Designing Tables and Graphs to Enlighten by Stephen Few:[/stextbox]A clear, concise and comprehensive book on how to design tables and graphs. It gives you the tools to create effective tables and charts, and the understanding on how and why these tools work. The book has a lot of practical advice which can be applied with Excel and hence can be put in practice straight away.

[stextbox id=”section”]5. Now You See It: Simple Visualization Techniques for Quantitative Analysis by Stephen Few:[/stextbox] This book starts where the previous one ends. Few talks about principles of visualization and their applications. Again, most of the learnings can be applied on Excel.

[stextbox id=”section”]6. Information Dashboard Design: The Effective Visual Communication of Data by Stephen Few:[/stextbox] A must read book for anyone who designs and creates dashboards for a living or wants to make a career in BI. This book will teach you the visual design skills you need to create dashboards that communicate clearly, rapidly, and compellingly. Key take-aways from the book are common dashboard design mistakes & ways to minimize distractions and confusions.

For advanced readers:

[stextbox id=”section”]7. Envisioning Information by Edward R. Tufte: [/stextbox] If you love the subject of data visualization, you will love this book. Tufte takes on a high-dimensional complex data and plots them on maps, charts, scientific presentations and courtroom exhibits. Topics in the book include layering and separation, micro/macro design, applying color and witing narratives. Some of the examples include sources as diverse as Gallileo’s observations of Saturn, a 3D map of a Japanese shrine, a visual “proof” of Pythagoras’ theorem, color studies by the artist Joseph Albers, and a New York train schedule.

[stextbox id=”section”]8. Visual Explanations: Images and Quantities, Evidence and Narrative by Edward R. Tufte:[/stextbox] Tufte starts discussing more advanced topics on arrangement of images, words and numbers in space and time. Visual Explanations centers on dynamic data–information that changes over time. This book includes an engaging narrative narrative of the 1854 Cholera Epidemic and a study on the Challenger space-shuttle tragedy.

[stextbox id=”section”]Special mentions:[/stextbox] Cartographies of time: A history of the timeline by Daniel Rosenberg & Semiology of Graphics: Diagrams, networks, maps by Jaques Bertin – Both these books are classic and give you a good perspective on how the field has evolved. You might still find some nuggets of gold here!

must_read_data2 

In my list of books to be read: Visual Thinking by Colin Ware. I have read some great reviews about the book, but have not been able to read it till now. It’s on my reading list.

If you have not read these books, please find them and read! On the other hand, if you have any other books which are not there in the list, please suggest them in the comments below.

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

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

Sheeba
Sheeba

These books seem great! Alas, I see they are expensive. Does anyone know if they are available in some library in Delhi?

123

Kunal Jain
Kunal Jain

Some of these are available in IIT Delhi library, if you have access to it. Thanks, Kunal

123
Sheeba
Sheeba

Oh dear! I suppose Central Library @ IIT-Delhi is accessible only for current and ex-students. I'll call them and confirm, nevertheless. Any idea if the British Council keeps books on Analytics?

123

Anjali Samani
Anjali Samani

Very useful indeed! Thanks!

123

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