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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

How Apple Watch would re-define Apple’s products in next 3 years?

Kunal Jain
Kunal Jain Last Updated : 13 Apr, 2015
5 min read

By the time I publish this post, the internet and blogosphere would be swamped with Apple Watch written all over it.

So, why am I writing another post on this?

For a simple reason – my perspective is different.  I think some of the advancements from Apple haven’t been discussed as much as they should have been. Please note that this is a data engineer’s perspective on Apple Watch.

The purpose of this article is not to review the watch (Sorry, the Apple Watch!) as a product. This article aims to bring out how Apple Watch would re-define Apple’s data warehouses and present a(nother) competitive edge to Apple in years to come.

apple_watch1

Here are a few features of Apple Watch I want to call out as a data scientist:

 

Feature 1: Launch of Apple Watch heralds deep personalization for Apple’s customers:

Think of iPhones 2 years back – there used to be 1 or may be 2 models to choose from. So, every customer with an iPhone 5 was similar – the device you possessed didn’t tell Apple any thing about you till they start mining your behaviour from App usage. Apple had no way to know upfront whether you are a fashion buff? A sports freak? A working professional?

In last 2 years, Apple drove basic personalization in their products – Apple iPhone 5c and 5s launched simultaneously, so did iPhone 6 and 6 Plus. But, this was still 2 new devices at a time and a total of 4 – 8 devices to buy from at any time.

All of this changes with Apple Watch – different models, different kind of bands, different sizes with Prices varying from $350 – $10,000 provides very specific information about the user to Apple. There are 38 different models to choose your design from!

apple_watch3

If that does not drive the point home, overlay the data from your iPhone (Watches would need to be paired with iPhones) and app usage behaviour. We are clearly talking a granular segmentation, probably no other player in the industry has been able to achieve. How would this impact you? Well, in almost every manner! Would you worry about a notification for a transaction of $5 from Credit card if you possess one of the Gold plated Apple Watches? The chances are slim!

If that has got you thinking, the next few features will give you an idea about Apple’s Genius behind this beautiful piece of instrument!

 

Feature 2: You are now constantly on!

There are 2 aspects of this feature worth mentioning:

  1. How many of you always carry your phones? How many of you always wear your watches? The chances are that more people have watches always with them.
  2. The second aspect is about proximity of the device. You watch is always with you – even when you are driving. It is not resting on your car’s dashboard. It is there with you when you are typing your emails (rather than resting on your office table), or when you are trying to put your baby to sleep.

What does this mean? More and more data about your habits and your lifestyle, which was not possible through your phones.

One of the challenges here would be the battery life – 18 hours. If only Apple would have made it in days, they would have just scored a home run in collecting customer data. I expect this to improve dramatically in the next iteration of Watch – something to watch out for!

Apple Watch

 

Feature 3: Heart beat tracking is now a reality:

According to CDC, 1 in every 4 deaths in the United States is because of heart disease. The percentage would be far higher in middle and low income countries. How far are we from the time, before a company releases data about heart rates for a Kaggle competition which makes heart ailments more predictable? It is unlikely that Apple would do this, but Apple already has in house expertise to make use of this data.

Tracking heart beat every second can yield results which are probably difficult to think right now. How far are we from the time when your health app tells you that exercising 5 minutes extra is likely to extend your life by x days? How does that gamification sound?

 

Let’s put these features together:

Now, let me just give a few examples of some data driven products which Apple Watch would enable in future:

  • Depending on your location, your watch tells you the right gym for you – based on your lifestyle and segmentation. How about recommendations of personal trainers? How about gym trainers accessing your activity records and crafting the workouts accordingly?
  • If you are travelling – which hotel suits your requirement and lifestyle? How far is it? How to reach there? How about checking in as soon as you enter? What should you order? How much should you order as well?
  • How about getting notifications about your family on your watch – you get notified for every class your child misses? You get notified at the first irregularity of heart beat of your parents?

Frankly, the possibilities are limitless!

The journey would take some time. Every new product has it’s own growth cycle and so would Apple Watch. Also. Apple still needs to make a few tweaks in their product:

  1. Better battery life, so that you can start capturing further lifestyle details like sleep quality.
  2. Apple hasn’t addressed the security of this data yet – would be Watch be unlocked always? Can any one access the intimate and personal details in this product? This might be an easy fix, if Apple has not addressed it till now – but something definitely worth pondering.

 

So, how do I feel about Apple Watch?

Overall, I am damn excited about the product as a data scientist (Of course, it is beautiful too and tells the time as well, as Tim Cook says it). It might take some tweaks before it becomes the dream of a data scientist. I’ll also closely watch how Apple handles privacy issues with customers and app developers.

Apple has started its journey for deep personalization through data, and I can’t wait to see how they arrive in style! I think that the data asset created by Apple through Watch would re-define their products and experience in years to come.

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Appledata driven productsInternet of thingswatch
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

Abhishek Sinha
Abhishek Sinha

Very good post Kunal. A must read for anyone attached to data. Thanks for writing.

123

Mahesh CR
Mahesh CR

Perhaps one of the few sensible articles on Apple Watch's potential. Whilst its common to wish for big-bang revolutions, Watch makes it possible for small behavioral tweaks to be possible via Glances, powered by some light weight analytics in near real-time. Not grand but practical and available today. Completely concur with your views too.

123

vishram
vishram

It is indeed another game changer from Apple, but from data scientist perspective, I would not appreciate it much. With just one more device (which essentially is an extension of existing iphone/ipad screen) being added to the range of apple devices already owned by user, do you really believe deeper insights would be provided on users' lifestyle, etc? How much additional info on personality can be generated by analyzing the sales data of watch, band, and maybe embossing on bands? Probably a delta on top of the info already available (given that watch works only with existing apple devices), Also, apple has shrewdly maintained the combination of watch editions and the bands to make most money from every deal. Yes, you are always on while wearing this (but do you really want to?) and how many useful sensors (and associated apps) does watch have apart from heart-bit monitor? maybe a Blood pressure and pulse monitor. While it is very very significant to be monitoring your own heart rate, there are cheaper watch like monitors already available in the market. Other features you mentioned are already existing in iphone before watch came. Yes, possibilities are limitless, depending upon sensors on the watch and the apps leveraging those to make life easier, better. Then, consuming the data generated from these additional sensors to drive analytics, will indeed offer little more insights. But all these data would remain with apple, or the app owning company so, how would it excite us as a data scientist, only time will tell. So, I would agree that its a long journey started with a bang, and take couple of years to be able to excite us!! keep exchanging ideas!!

123

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