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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 for Analysts (or people interested in Analytics)

Kunal Jain
Kunal Jain Last Updated : 26 Aug, 2021
4 min read

One of the ways I continue my learning is reading. I read for 30 minutes before hitting the bed every day. This not only makes sure that I learn some thing daily, but also ends my day in a fulfilling manner. Over the years, I have read a variety of books on various subjects. In this article, I will share a list of 7 must read books, which I think should be present in every Analyst’s bookshelf.

I believe that each and every book listed below has helped me learn about Analytics and expect that they will immensely help people who want to learn about this field.

These books are chosen, not because they have best technical subject matter, but because they help you understand the impact of analytics. While none of these books will tell you how to build the best logistic regression model, they will definitely help you appreciate the need and impact of building a model.

books on analytics

So, if you wish to learn about analytics, do read these books and see how analytics is transforming the world around you:

[stextbox id=”section”]1. When Genius failed:  The Rise and fall of Long-Term Capital Management[/stextbox]

This book is one of my all time favorite. It starts from a period when no one had heard of analysts being employed on Wall Street. A fast paced thriller, which is difficult to put down after reading first 50 pages. This book not only brings out how you can compete on data based decisions, but also why you need to keep human behavior in mind while taking decisions on data.

Here is a brief description about the story:

When it was founded in 1993, Long-Term was hailed as the most impressive hedge fund in history. But after four years in which the firm dazzled Wall Street as a $100 billion moneymaking juggernaut, it suddenly suffered catastrophic losses that jeopardized not only the biggest banks on Wall Street but the stability of the financial system itself.

[stextbox id=”section”]2. Scoring Points:  How Tesco Continues to Win Customer Loyalty[/stextbox]

Written by Clive Humby, one of the founders of Dunnhumby (company which does Analytics for Tesco), this book brings out some of the practical challenges faced by Tesco and how they overcame them to create one of the biggest success story of customer loyalty.

A must read for any one dealing with customer data to study their behavior and then turning them in business insights.

[stextbox id=”section”]3. The Signal and the Noise: The Art and Science of Prediction[/stextbox]

In his recently published book, the political forecaster Nate Silver, who accurately predicted the results of every single state in the 2012 US election, reveals how we can all develop better foresight in an uncertain world. From the stock market to the poker table, from earthquakes to the economy, he takes us on an enthralling insider’s tour of the high-stakes world of forecasting, showing how we can use information in a smarter way amid a noise of data – and make better predictions in our own lives.

The book tells not only about the errors made by analysts, but also how to avoid them.

[stextbox id=”section”]4. Web Analytics 2.0: The Art of Online Accountability & Science of Customer Centricity[/stextbox]

Another recent addition by Avinash Kaushik, author of the leading web analytics and research blog Occam’s Razor. This book is a must read for any one working in Online industry or wants to learn web analytics. It provides specific recommendations for creating an actionable strategy, applying analytical techniques correctly, solving challenges such as measuring social media and multichannel campaigns and employing tactics for truly listening to your customers.

[stextbox id=”section”]5. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die[/stextbox]

What could be better than learning about Predictive Analytics from one of the most influential personalities in this domain. In this rich, entertaining primer, Siegel reveals the power and perils of prediction by including case studies from across the globe.

Aimed towards a common man, the book explains predictive modeling and its basics in lay man terms.

[stextbox id=”section”]6. Moneyball[/stextbox]

Another one of my favorites, this book is based on application of statistics to baseball. Inspiring story of a baseball team manager with low budget to run the team, who uses statistics to identify undervalued players and carves out a winning team out of them. The book has also been converted into a movie, but I prefer the book over the movie.

[stextbox id=”section”]7. Freakonomics[/stextbox]

This one is a classic, probably the first book where I read on how data and analysis can be used to reveal unknown insights.  While analytics has evolved significantly since the time this book had come out, this book is still worth a read for the analysis it presents and delivers from the tools of previous era.

[stextbox id=”section”]These ones just missed…[/stextbox]

Apart from the ones mentioned, there were a few other books which I had considered in creating this list and are worthy of mention. The Intelligent Investor by Benjamin Graham, Competing on Analytics by Thomas Devenport, The Black Swan by Nassim Nicholas Taleb fall in this category. All of these are good reads if you are interested in the subject.

This is my list of must read books for every analyst. In case you have any other recommendation or want to add more on any of the books mentioned, please feel free to add through comments.

If you like what you just read & want to continue your analytics learning, you can 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

Rajiv Jain
Rajiv Jain

Hi Kunal How can a professional with BE(Mech)+ Ex-PGDM(marketing) and more than 5 years of exp. in metals and mining sector will be able to prove its ability to handle analytics to be hired by any analytics company in domains like manufacturing, metals and mining, Oil and gas. Although he has a strong analytics aptitude and little exposure to some analytics software too.

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Kunal Jain
Kunal Jain

Hi Rajiv, There are multiple options which you can explore depending on resources and availability:

    * Making a shift internally, if your company has analytics setup. If you have a good performance record and you can align your manager, this should be the way to go. * Alternately, you can look at doing some certifications as mentioned in some of my previous articles here and here * Another way could be to start working with analytics companies as an intern and then apply for full time roles, once you have proven yourself.
Hope this answers your question.

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sridhar
sridhar

I have around 7.5 years of experience in Embedded Testing and currently not working any where. Recently after reading a lot on Analytics, planning to pursue a course and then take up a job. So what do you suggest for people who are into a different domain altogether trying to look for a career shift with almost 8 years of experience. I have seen some of your articles on training from various IIMS and reputed institutes. But I am not sure they will offer placment. Please guide me - I am now planning to take a course in this areas, but courses would not really give us that real experience as they are really short term and all you get is a exposure. In such cases what would be the right approach ?

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Kunal Jain
Kunal Jain

Hi Sridhar, As with any career shift, employers will find limited value in your past experience. So, be prepared to take a more hands on role in initial days. The certification courses mentioned in my articles do not provide any placement services and would also be running for longer duration (than your need). There are 2 options which I can think of: * Do a certification from software providers. I know about certification courses from SAS adn Qlikview and can recommend a boot camp course. These courses run for a couple of weeks or months, give you all the knowledge and some hands on experience. These can then be followed by examination and certification. These certificates are well regarded in industry and can provide you a selling point on your CV. Some providers can also help you with placements. * Alternatively, one more option could be to do a course from Jigsaw Academy. Their course will run for 3 - 4 months and they provide placement support. I don't know people who have undergone this course, so I can not comment on the course content and placement service, but you can explore it for yourself. Hope this answers your question.

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Ganesh
Ganesh

Hi Sridhar, Have you joined any course. If yes, can you share with me the details. Thanks in advance. Ganesh

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Girish Malik
Girish Malik

Hi Kunal, Warm Greetings! I have read this book and, going by your collection, I believe you will like it for the sheer practicality of it. Cheers, Girish Malik

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