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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

9 Books to Start Your Business Analytics Journey

Abhiraj Suresh
Abhiraj Suresh Last Updated : 19 Jul, 2020
7 min read

Introduction

The global spread of the internet has made the availability of knowledge easy. Every information is at the grasp of our palms. As a result, we are seeing a global decline in the purchase of books and visits to the library. We will not be completely wrong in saying that the internet and e-books are becoming a substitute for books and libraries.

There is more treasure in books than in all the pirate’s loot on Treasure Island.” – Walt Disney

But it is a myopic mindset that forgets everything old. All the great leaders of the world still read and learn from books. Even the largely available e-books fail when compared with the psychological impact of reading and learning from books. What Walt Disney said passed the test of time. There is no replacement for a book and treasure you find is the key to further treasures of life.

Like any other subject, even business analytics professionals have a collection of books to broaden their minds. In this article, I will be talking about the various books to hone.

If you are more interested in a course to understand and get started with Business Analytics, have a look at our free course-

 

Table of Contents

  1. Microsoft Excel Data Analysis and Business Modeling by Wayne L. Winston
  2. Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
  3. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
  4. SQL in 10 Minutes a Day by Ben Forta
  5. Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic
  6. Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau by Ryan Sleeper
  7. The Hundred-Page Machine Learning Book by Andriy Burkov
  8. The Pyramid Principle: Logic in Writing and Thinking by Barbara Minto
  9. Scoring Points: How Tesco Continues to Win Customer Loyalty by Terry Hunt

1. Microsoft Excel Data Analysis and Business Modeling by Wayne L. Winston

The 21st century has changed the game with respect to data storage and warehousing. Moreover, the plethora of data generated cannot be manually analyzed using statistics. Hence, began the reliance on statistical analysis on computer applications.

For more than 35 years, Excel has been one of the favorites for beginners to perform all types of statistical analysis. Mastering excel serves as a great stepping stone towards your analytics career.

Microsoft Excel Data Analysis and Business Modeling by Wayne L.Winston are one of the best books to learn MS Excel from scratch. The book not only covers the use of excel for basic statistics and probability but also has rich content for practical application of technical topics like multiple regression, time-series forecasting, etc. And the best part is the content extends to great lengths to make you an expert in excel with a lot of practical exercises for each topic.

Here is a course for you if books are not your way of learning – Microsoft Excel: From Beginner to Advanced-2.0

 

2. Naked Statistics: Stripping the Dread from the Data by Charles Wheelan

Business Analytics Professionals consider statistics as their foundational knowledge. It helps them understand the data in hand and find necessary relationships.

Statistics - Business Analytics

“[Wheelan] does something unique here: he makes statistics interesting and fun. His book strips the subject of its complexity to expose the sexy stuff underneath.”
– The Economist

Naked Statistics by Charles Wheelan is considered by many, the best book for beginners in statistics. It is fun and easy to read when it comes to explaining the topics of descriptive, inferential statistics, probability, and regression.

With technical concepts explained in layman terms, this not only serves as a great book for beginners. But for anyone just to revise or brush up the topics of statistics without getting bored.

 

3. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

Python for Data Analysis, 2nd Edition

Have you ever wondered how will you manipulate the data you have? How are you going to explore it in a depth, find relationships between variables and identify and transform various kinds of data types? These tasks can easily be performed with a simple line of code in python. Its most basic libraries not only helps you perform data analysis but easily helps you explore and transform the data too with just a couple of lines of code.

Business analytics professionals often use Python for performing descriptive analytics. They also perform Exploratory Data Analysis to find patterns, trends, and various relationships between the variables to draw insights and aid in better decision making for the business.

This is one of the most used books for business analytics professionals. The book goes deeply through the important libraries like Numpy, pandas, matplotlib, statsmodel, scikit-learn, and also helps one learn python with ease.

Here is a course for you if books are not your way of learning – Introduction to Python

4. SQL in 10 Minutes a Day by Ben Forta

Data Retrieval is one of the most basic skills which people forgo. A lot of business analytics professional does not know how to proceed.

Structured Query Language(SQL) makes the job easy. Just a couple of lines of codes will help you get 1000’s of rows of data. Also, not only does this give examples for syntax in MySQL but in multiple languages such as Oracle, PostgreSQL, etc.

The concepts of database and database management are explained in a beginner-friendly manner with practical examples. But the name suggests the best feature of the book- one has to spend just 10 minutes regularly to become an expert.

Here is a course for you if books are not your way of learning – Structured Query Language (SQL) for Data Science 

 

5. Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic

Storytelling with Data: A Data Visualization Guide for Business Professionals by [Cole Nussbaumer Knaflic]

This book teaches you the foundational knowledge of visualization and how to communicate effectively with data.

The above book gives business analytics professionals pragmatic guidance on how to present data in an understandable manner. Moreover, the book contains an abundance of information, insights, and counsel that can help almost anyone to master the skills needed. In addition, this book demonstrates how to go beyond conventional tools to reach the depth of your data and how to use your data to create an engaging, informative, and compelling story.

 

6. Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau by Ryan Sleeper

Tableau - Business Analytics

Tableau is one of the favorite visualization and dashboarding tools in the industry. The popularity is further confirmed by Gartner’s Magic Quadrant where it has acquired a leadership position for long now. Practical Tableau is a perfect book for beginners to learn and become professional. In addition, author Ryan Sleeper is a qualified Tableau consultant and he has imparted his rich experience in the easiest manner possible.

The book is divided into 5 parts- Fundamentals, chart types, tips and tricks, framework, and storytelling. Each part has an in-depth explanation of concepts along with practical examples to go by. Practical Tableau answers questions like-

  • Why are we creating this chart?
  • How are we going to create this?
  • What if you create this visualization

Here is a course for you if books are not your way of learning – Tableau for Beginners

 

7. The Hundred-Page Machine Learning Book by Andriy Burkov

The Hundred-Page Machine Learning Book by Andriy Burkov is recommended book for beginners in data science. This book is one of the best introduction to machine learning I have ever read. It successfully retains the required amount of mathematical rigour without intimidating the newbies. Thought leader like Peter Norvig has also endorsed this book. Beginner or established, every data scientist should get their hands on this book.

 

8. The Pyramid Principle: Logic in Writing and Thinking by Barbara Minto

Communication

The Pyramid Principle by Barbara Minto is one of the eye-opening books. The book is crystal clear when it comes to explaining communication and structured thinking for people of any career. This book is used in many colleges and also by companies like EY, Delloite, etc to teach them to communicate effectively.

Even after 25+ years of publishing, people regard it as the best book in how-to-write guides for technical and persuasive work in any language. If you were to read one book on how to present an idea, then this is it. Further, it helps you make your presentation concise, understandable, and impactful.

 

9. Scoring Points: How Tesco Continues to Win Customer Loyalty by Terry Hunt

Scoring points is one of the most thought-provoking books. Author Terry Hunt presents a case study where he explains how Tesco became one of the Supermarket Giants in the UK by the use of customer loyalty marketing. The customer loyalty program was created by deeply analyzing the data to reach a business decision.

It is one of the best reads for any business analytics professionals to understand one of the most successful business analytics stories. It is one of the best case studies highlighting the value and challenges of business analytics.

 

 

End Notes

To summarize, in this article, we read about some of the 8 relevant books that will help a business analytics professional hone his/her skills. Meanwhile, if you want to explore your interests in business analytics, we recommend you check out this free course-

Also, in case you are confused between a data scientist and a business analytics professional, I recommend this article-

I hope this article was fruitful. Comment your favorite business analytics books.

 

 

books in analyticsBusiness AnalyticscommunicationExcelproblem solvingpythonstructured thinkingtableau
Abhiraj Suresh
Abhiraj Suresh

My name is Abhiraj. I am currently a manager for the Instruction Design team at Analytics Vidhya. My interests include badminton, voracious reading, and meeting new people. On a daily basis I love learning new things and spreading my knowledge.

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Responses From Readers

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Srinivas Adapa
Srinivas Adapa

Nice one, thanks for sharing the resources.

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The writing BI Analyst
The writing BI Analyst

Thanks for sharing resources. This is great!

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