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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 (and blogs) on Web Analytics

Kunal Jain
Kunal Jain Last Updated : 24 Sep, 2015
4 min read

I love reading!

By reading something every day before sleeping, I not only continue my learning, but also end my day on a fulfilling note. This is third and final post in series of must read books (click here for Business Analysts and visualization).

The demand for web analytics professionals is set to increase. Here are some of the reasons why I expect so:

  • Increasing internet penetration across the globe
  • Growing e-commerce industry (especially in developing countries)
  • User data available at a very granular level (starting from where user clicked to how much time he spent reading about a product)
  • Integration across mobile devices and interfaces

All these reasons are enough to ensure that application of Web Analytics grows by leaps and bounds. With increased application, the world would need more professionals in this industry.

So, if you plan a career in Analytics and are not up to pace with Web Analytics, start learning it today. It is a dynamic field where things change frequently. Keeping that in mind, this list contains both books and blogs, as I think they need to be used simultaneously for effective learning.

There is a plethora of reading material on this subject. However, I have shortlisted only the best resources which would definitely add value to your web analytics learning. All these books and blogs are tremendous source of knowledge and have helped me increase my understanding of web Analytics:

must read books web analytics

 [stextbox id=”section”] Books and blog by Avinash Kaushik [/stextbox]

Meet Avinash Kaushik, Digital Marketing Evangelist at Google, author of some of the best books on web analytics, blogger – Occam’s Razor (one of the most popular blog on web Analytics) and co-founder of Market Motive Inc.

No list of resources on Web Analytics can be complete without mentioning work of Avinash Kaushik. Whether you are new to web analytics or a pro in this field, you will still find tons of practical advice on his blog or in his books.

Here is a brief description of the books he has written:

Web Analytics: An Hour a Day

Through this step-by-step guide on how to implement a successful Web analytics strategy, Kaushik leads you on a path to gaining actionable insights from your analytics efforts. Discover how to move beyond clickstream analysis, why qualitative data should be your focus, and more insights and techniques that will help you develop a customer-centric mind-set without sacrificing your company’s bottom line.

Web Analytics 2.0

This book starts where the first one ended (though you can read them independently). It provides specific recommendations for creating an actionable strategy, applying analytical techniques correctly, solving challenges such as measuring social media and multi-channel campaigns, achieving optimal success by leveraging experimentation, and employing tactics for truly listening to your customers. The book will help you become a super analysis ninja!

 [stextbox id=”section”] Advanced Web Metrics with Google Analytics by Brian Clifton [/stextbox]

This book can be easily called out as Bible of Google Analytics. Brian formerly led the Google Web Analytics team for Europe, the Middle East, and Africa, and he initiated and helped launch the online learning center for the Google Analytics Individual Qualification (GAIQ).

This book will teach you each and every aspect of Google Analytics, starting from basic reporting to creating segmentation, tracking social and mobile visitors, use of new multichannel funnel reporting features and much more. If there is only one book you want to read on Google Analytics, this is your book.

 [stextbox id=”section”]How to Measure Social Media: A Step-By-Step Guide to Developing and Assessing Social Media ROI by Nichole Kelly [/stextbox]

If you face any of these questions and do not have a concrete answer, this book will provide you the answers:

  • “What is the ROI on social media campaigns you have run and invested in?”
  • Do they make repeat purchases more often than other customers?
  • How much revenue did our activities on social media platforms generate this month?
  • Are social media prospects more likely to convert to customers?
  • Which status update delivered the highest conversion rate?
  • How long do we retain new social media customers?
  • Do they spend more or less than customers from other channels?

This book will give you simple step-by-step techniques for creating measurable strategies and getting the data to prove they deliver. You’ll also get helpful hands-on exercise worksheets.

[stextbox id=”section”]List of blogs to follow:[/stextbox]

1. Occam’s Razor by Avinash Kaushik

2. Analytics Talk by James Cutroni

3. Official Google Analytics blog

4. Adobe digital Marketing blog (formerly known as Omniture Industry Insights)

5. Since I have not included any text on search, Search Engine Watch becomes a must follow to be up to speed with development on Search

[stextbox id=”section”]Special mention (book):[/stextbox]

Search Analytics for Your Site: Conversations with Your Customers by Louis Rosenfeld was a very useful book when I read it. However, given the recent rise in “Not Available” keywords on search, I have strayed away from recommending it as must read.

On my reading list:

Web Analytics Action Hero: Using Analysis to Gain Insight and Optimize Your Business by Brent Dykes

What do you think about the list? Are there any other recommendations you would want to share on the subject? Please add them in the comments below.

You might also like:

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

Shaifali
Shaifali

Hey Kunal Thanks for rudiment suggetions. But for a bignner I think before putting hand in web analytics one must know basics of data analytics; Please can you suggest some books for it.

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Kunal Jain
Kunal Jain

Shaifali, Look at the follwing article Kunal

123
Janani R
Janani R

Hi Kunal, I have made entry into analytics field recently after 3 yrs of experience in Market Research. Initially I started with learning statistical techniques through SPSS and SAS, later moved on to SQL.Moving on, Since I am interested in marketing analytics , Can you please suggest me' 1)Main statistical techniques or modelling techniques that I should learn (like segmentation,clustering,time series modelling) that would suit marketing analytics' common needs 2)Tools that I could learn in Marketing Analytics (Web/Social media Analytics tools such as Google analytics, Omniture, Core Metrics). Which would be the best to learn? 3)Would it be good to learn big data technologies like R or Hadoop which find greater use in web crawling,sentiment analytics and marketing analytics than learning marketing analytics specific tools like Omniture or Sysomos? It would be really helpful if you could help me in identifying them. -Janani

123

Kunal Jain
Kunal Jain

Janani, Welcome to this wonderful world! Here are your answers: 1. Segmentation and clustering are the most common used techniques in marketing. Other popular techniques is predictive modeling (mostly regression) for Cross-sell and up-sell. If your employer sells products online / has a big online presence, web analytics is another are to lookout for. 2. Start with GA and then try one of the two - Omniture / Coremetric 3. I wouldn't trade-off one over other. You should learn depending on the need and the role you are currently performing / plan to take up. If it involves web, go for basic web analytics first and big data later on. If not, do it other way. Thanks, Kunal

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abhi
abhi

Hi Kunal, First of all I thank you for the great guidance that you and your team is providing to poeple in analytics and people who want to enter in analytics. I need some guidance from you. Currently I am working in Web analytics & digital marketing domain. I have 6 months of experience. I have also undergone training in business analytics with sas (and I love it ). I want to make a switch to business analytics, but since I dont have any experience in business analytics, so i am facing problems in switching. I asked some people regarding this issue, they suggested me to stick with web analytics domain as it is bound to achieve great heights especially in india. But if I compare salaries of people in web analytics domain with people in business analytics domain (from payscale.com) there is a significant difference, with people in business analytics earning great packages. Further I have read on some blog that salaries in web analytics saturate around 10-12 LPA. Whats your opinion on this? is this correct ? and What would you suggest, should continue my efforts of switching to business analytics or stick with web analytics?

123

Kunal Jain
Kunal Jain

Abhi, You are absolutely right about the difference in the salary and its saturation. I think you should continue to look out for BA role. Where are you based out of? Can you mail me your CV? I might be aware of an opening, which might be relevant to you. Thanks, Kunal

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