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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

Boon from big data or loss of privacy?

Kunal Jain
Kunal Jain Last Updated : 24 Apr, 2015
5 min read

Today’s post is going to be different.

There is no technical subject matter I am going to talk about. But the article is far more thought provoking than any of the article I have written till date.

[stextbox id = “section”] A real life incident:[/stextbox]

Let me start with a real life example to get your thinking process started:

About 6 months back, I bought a top end Android smartphone. After using it for a month or so, I accidently started Google Now on the phone. The interface looked very simple on first look (nothing more than a search bar and weather update). So I moved back from the application and started living my usual life.

I would have almost forgot this instance like multiple other applications which come with the phone and I don’t use. However, Google had something else in store. A week after I opened the application for first time, I got a notification on my home screen, suggesting that I am 15 minutes away from Home and the traffic on route is normal!

The notification took me by surprise. I never told my phone where my home is! Over the next few days, the application identified my Office, commute place, friends place, the websites I visit frequently. It now integrates my searches across devices. So if I search a restaurant on my laptop, my phone shows me the route to same restaurant!

big-data-eye

The incident above is like a dream come true for a lot of analysts and a scary incident leading to loss of privacy to a lot of customers.

As an analyst and some one who specializes in predictive modeling, I am usually a proponent of big data and the changes it is bringing to our day to day life. However, I have to admit that Google took me by surprise and has made me think and reflect a lot more on how life is changing. It has ensued a debate between 2 sides of my personality.

[stextbox id = “section”]Two sides of debate:[/stextbox]

My first personality is that of a common man. I want my privacy, specially during some personal moments. These moments could be the time I spend with my family or when I am reading or may be talking to a friend. I don’t want interruptions or suggestions from any third party during this period. I want to relish the moment as it is. After going through the experience mentioned above and many more like that, I am not sure whether these moments will remain as pristine and unadulterated as I would want them. Would my reading experience be marred by suggestions about different things I might like? Would the phones pop up notifications about my friend when I am talking to them? or may be when I am talking about them to my wife? The possibilities are limitless!

The other side of my personality is a big proponent of technology and Analytics. I remain excited about how technology can be used to solve day to day problems. I come out with innovative ways of using data to create value (for customers as well as Organizations). I continuously think how behavioural modeling can help customers in breezing through day to day chores? How can I predict something before it actually happens.

[stextbox id = “section”]How do we resolve this?[/stextbox]

After a lot of thought and internal debate, I have come to a conclusion that we are standing at that juncture of time when both these personalities need to change. The first personality needs to be aware about the data trails I am creating and actively cut them out if I don’t want them. So, I have stopped carrying my smartphone when I play around with my daughter. I need to be aware that nothing gets erased from this new world we are living in, be it a comment on Facebook, a status update, my geo-location map (through the mobile device I am carrying). It is only a matter of next version of Android roll out before my calls get searched for what I am looking out for and I get app suggestions based on that.

The second personality needs to be cognizant about the presence of first personality and take actions which are in sync with values of first personality. Here are some rules I have come out with, which every analyst needs to keep in mind while designing a product or working on his next big data project:

big data balance

[stextbox id = “section”]1. Transparency:[/stextbox]

This is the biggest takeaway. The bare minimum an analyst needs to make sure is that the customer is aware about what data is being collected and how can this be used. This needs to come out clearly. This is similar to apps (on smartphones) asking permissions before installing them. If you are collecting data with out asking customer explicitly, you are headed for disaster.

So, instead of using data through a pre-selected tick box (buried somewhere is my phone settings), I would have appreciated if the app reminded me of the data it will use, when I started it for the first time.

[stextbox id = “section”]2. Develop a character of your Organization by keeping customer at the heart:[/stextbox]

Let me try and explain. Years before Google started collecting information about usage from Android phones, Microsoft started this for MS Office. They asked me whether I would want to share my usage patterns with Microsoft, which will help them improve user experience further. I almost always declined. When Google asks me same thing, I am more open to sharing information.

It might be a personal choice. However, the reality is that I am more open to sharing data with Google because I can relate to the benefits they have provided me by using this information. I have benefited by sharing some of this information with Google.

The message is that if you don’t provide the benefit of this information back to the consumer, they will stop sharing this information.

[stextbox id = “section”]3. Make change in subtle manner:[/stextbox]

Big changes in user interface or the way new product gets rolled out can take customer by surprise. You have to build in these changes in subtle manner. In a way such that the customer still feels as much at home as possible. I think Google does a nice job at it. Here are some best practices:

  • Provide an option to user to switch back to old proposition, if it is not working for him
  • Roll out changes slowly and in steps. Recently Google organized Gmail in different tabs, a move which benefited me by making sure I spend maximum time on relevant mails. But they also started to show ads in one of the tabs. I thought it was a smart way to test. If customers feel distracted by these ads, they will pull them out. If customers get used to them, they can roll them out to different tabs.
  • Try and keep as much user interface unchanged as possible.

[stextbox id = “section”]4. Test and roll-out:[/stextbox]

Irrespective of how good an idea is, you should avoid making complete roll-outs without testing. There are multiple benefits from this:

  • You actually act based on how customer feels about the product
  • You can size the benefit / loss you have seen by moving to a new product.

I think until and unless Organizations and analysts adhere to these rules, it might only be a question of time before they face a bunch of disgruntled customers.

What do you think about these rules? Are there any other rules, you think Organizations should follow? Or which you follow while rolling out your big data projects. Do let me know your thoughts through comments below.

Image 1 credit: tnooz.com

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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

Ravi T
Ravi T

Thought provoking

123

Abhishek Chauhan
Abhishek Chauhan

Interesting!!! I will be more comfortable with sharing my personal data (on the condition it will boomerang with Google Now kinda advantage) as a company and as a person, if I can control the data security. Google Now today has its own advantages...but what troubles people most is that the data resides in a remote US server which one cannot control. What if tomorrow Google fiddles with data security guidelines (just like Facebook does every 3 months :P), gets a few Google trusted partner, shares data with them..to offer more services....and then it whispers around.... Contrary to this, imagine the good old days when Japan used to market their Intelligent Robots (much like today's intelligent apps) ... helping people in their daily chores....You wouldn't mind sharing personal information. I always liked that concept. An approach where data remains off the internet/cloud could be a trade-off some people are comfortable with. Although I don't see many treading the path.

123

Data-fyed
Data-fyed

Think the user should understand their own limitations before using some of these applications. Many are there to get personal information and then predict, if you are not comfortable sharing then you can stay away. I think a user has more power than we give them credit for. Data-fyed

123

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