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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

Top 7 Data Science Books To Transform: Novice to Intermediate

Gargeya
Gargeya Last Updated : 01 Jul, 2021
6 min read

This article was published as a part of the Data Science Blogathon.

What you can’t find in someone’s voice, you might find in someone’s writing.

I was always more inclined to following and referring video tutorials/lectures whenever it comes down to studying something on my own from the web. I found it easier ( just like some of you ) to understand and not go through the pain of reading available books. Most of the time, I felt the same unless recently I discovered those writers or publishers who eliminated the element of ‘bore’ from subject books and made them so… much interesting.

This started when one of my really smart friends told me to start reading books because they contain more content and adds to a really important skill for any person, that is reading and understanding. Initially, I was not keen on doing that unless he also mentioned a publisher whose books are really fun to read and interactive. This got me thinking: “does something like this really exists?” so to confirm my doubt I gave it a try and unraveled this whole new dimension of amazing books that I could read for hours.

Today, I am making myself that well-wishers of yours and sharing with you those books, those publishers whose books will make you think twice before turning your face away from books.

My favorite Publishers:

1. Manning Publications

They are an American-based publishing company founded in 1990, they mostly publish books on computer technology topics and a lot of them are worldwide famous and loved by millions of readers. The most amazing ones are mentioned below along with all my other top data science books.

2. O ‘Reilly Media Inc.

This is another absolutely fantastic American learning organization set up by Tim O’Reilly that distributes books, produces tech gatherings, and gives a web-based learning stage. Their particular image includes a woodcut of a creature on a significant number of their book covers. Their books contain lots and lots of content on the latest and high-tech topic so that their readers can become profound in those areas and excel at their work.

My Favourite Data Science Books:

1. Python Data Science Handbook by Jake VanderPlas published by O ‘Reilly.

data science books python

This book is best for those who just started doing Data Analysis or Data Science and need a go-to book to refer to all the techniques and library functionalities and strengthen their grip on python for data science and letting it work for you. The book covers these topics in great detail and depth: { IPython (Interactive Python), Numpy, Data Manipulation with Pandas, Visualization with matplotlib, Supervised and some Unsupervised Machine learning algorithms with scikit-learn }. The amount and quality of content available on these topics will significantly contribute to harnessing your skills for the first few steps in any data science project cycle.

 

2. Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce & Peter Gedeck published by O ‘Reilly.

data science books statistics

The Second edition of this book is already released and personally speaking, even if you are a starter or a practitioner, reading this book will be beneficial to you, because there are a lot of skills that I gained from this book, few were those that I have forgotten over the period of time and some that I didn’t know already. After reading this book I started feeling more confident and I can say that it was worth the read.

It includes such topics: {EDA, Data and Sampling Distribution, Statistical Experiments and Significance Testing, Regression, Classification, Statistical ML and Unsupervised Learning}. So, if you are a beginner, I would recommend you to read the 1st book and after that directly jump to this book and make yourself familiar with a lot of new skills in data science.

 

3. Introducing Data Science by Davy Cielen et.al published by Manning Publications

data science books DS

I like this book for a special reason and that is, the books contain not only the topics of data science that we see everywhere, it also includes other aspects of Data Science as a field, such as { NoSQL databases, Text mining, Text analysis, First step in Big data, and especially handling large data on a single computer.} Understanding and working with the integration of databases in your data science project is a really helpful and sought-out skill. I highly recommend you to give this a read and more or less get yourself familiar with above mentioned extra skills in your arsenal.

 

4.The Art of Statistics Learning from Data by David Spiegelhalter published by pelican publications

The Art of Statistics Learning

This book was specially recommended to me by my instructor while I was pursuing my course of Applied Data Science on Coursera by the University of Michigan. They significantly drove us into realizing the importance of skills (to be more accurate, Art) of visualization so that your visualization doesn’t say what they are not supposed to and feels self-explanatory to the reader. I highly recommend this book to those who wish to understand the depth of data visualization and master the skill.

 

5. Data Science from Scratch by Joel Grus published by O’Reilly

Data Science from Scratch

The second edition of this book is already released and it has been a popular book due to the fact that it encounters various fundamentals altogether in this single book. Starting from Crash course on Python, Visualizing Data, Linear Algebra and Statistics, Probability, Hypothesis and Inference, Getting and working with data and many more topics relating to data along with Machine learning, Neural Nets, Recommender systems, Network Analysis and whatnot involved as well. It’s a full package deal and you should definitely consider giving it a read.

6. R for Data Science by Hadley Wickham & Garrett Grolemund published by O’ Reilly

R for Data Science

Okay, so I admit that I love working with python and have only mentioned the books based on Python for Data Science. But this book is for people who like or what you give ‘R’ programming language a shot. I have tried this language and it’s nice but most of the work is related to python so I never really considered shifting my programming language to R. This book breaks that bias, I really enjoyed reading and implementing this book while I was learning ‘R’. You should definitely read this book if you are thinking of doing something fun or new in data science like learning a new language for Data science tasks. The books will tell you all about it. Definitely worth checking out.

7. Think Stats by Allen B. Downey published by O’ Reilley

Think Stats by Allen B. Downey

Think Stats is a prologue to Probability and Statistics for Python software engineers and Data Scientists (if you already are not familiar with these topics in detail).

Think Stats underlines straightforward methods that you can use to explore real data sets and answer intriguing problems. The book presents a contextual analysis utilizing data from the National Institutes of Health.

If you have essential skills in Python, you can utilize them to learn ideas in probability and statistics. Think Stats depends on a Python library for probability distributions. Many exercises included uses short programs to run various experiments and help readers develop a strong understanding.
Most of the books don’t cover Bayesian statistics yet Think Stats depends on the possibility that Bayesian techniques are too critical to even consider delaying. By exploiting the PMF and CDF libraries (used for probability distribution), it is feasible for amateurs to gain proficiency with the ideas and take care of testing issues.

That’s it for this article, I hope that these books bring more shine to your skillset. Keep Growing, Keep Reading, Keep Flourishing.

Gargeya Sharma

B.Tech 3rd Year Student
Specialized in Deep Learning and Data Science

For getting more info check out my Github Homepage

LinkedIn           GitHub

Photo by Annelies Geneyn on Unsplash

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

Gargeya
Gargeya

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