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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

Running scalable Data Science on Cloud with R & Python

Kunal Jain
Kunal Jain Last Updated : 11 Dec, 2015
7 min read

Introduction

The complexity in data science is increasing by the day. This complexity is driven by three fundamental factors:

  1. Increased Data Generation – Look around, how many data emitting instruments can you see around? The laptop you might be reading this article on, the smart phone (and the apps in it) you might be carrying around, the fitness band you might be wearing in your hand, the car you drive (in some cases) – are all emitting data continuously. Now imagine things in five two years from now – the refrigerator you use, your home temperature maintenance unit, the clothes you wear, the pen you carry, the water bottle you drink water with all will be carrying sensors and emitting data for the data scientists (and the databots) to analyze.
  2. Low cost of data storage – Let’s do ground reality check. Take a guess – How much would be the cost of storing the entire universe of music created ever? What did you guess? My estimate is that this number would be significantly less than a grand!
  3. Cheap computational power – Check out this power packed configuration of a laptop released recently. A Xeon processor packed with Quadro GPU and containing 64 GB RAM. This machine is expected to come out for less than $2000 and weighs ~2.5 kg. Do I need to say more?

Lenovo P series

So, in summary, we are generating far more data (and you are becoming a data point as you read this article!), we can store it at a low cost and can run computations and simulations on this data at a low cost!

 

Why run Data Science on Cloud?

So, why do we even need to run data science on cloud? You might raise this question that if a laptop can pack 64 GB RAM, do we even need cloud for data science? And the answer is a big YES for a variety of reasons. Here are a few of them:

  • Need to run scalable data science: Let’s  dial back a few years. In 2010, I joined a multi-national Insurance company to set up a data science unit. Among several things, we bought a server which had 16 GB RAM. Since this was a new setup, we had bought more than what we thought we would need in next 3 – 5 years. What seemed a stellar combination at the start of the journey did not scale when we hired more people in team. Not only the number of people in the team increased, the amount of data increased exponentially. With a physical server, we were stuck! Either we buy a new, more powerful server or load this one to maximum (which would have again ran out of juice). The last thing you want is that your data scientists are staring at the screen waiting for data to be processed! A machine on cloud can scale on a click of the button, with out much hassle. So your current scripts and models can easily run when the data behind the model grows multiple times.
  • Cost: While scalability is one benefit, cost is another. Let us say you need to work on a problem, which is one-off in nature, but needs a higher computational infrastructure. This could be any thing – you want to mine social media data for an annual event you have sponsored, but you want the insights in real time. You can’t really buy a new machine for that. The costs will be prohibitive. Simple, rent out a higher configuration for a few hours or days and you have solution to your problem at a fraction of the cost.
  • Collaboration: What if you want to work simultaneously along with several data scientists? You don’t want every one to create a copy of the data and code in their local machines.
  • Sharing: What if you want to share your piece of Python / R code with your team? The libraries you might have used may not be there or might be of the older version. How do you make sure that the code is transferable to a different machine?
  • Larger ecosystem for machine learning system deployments: A few cloud services like AWS, Azure provide complete ecosystem to collect data, run your models and then deploy them. In case of physical machine, you will need to set this up yourself.
  • Use for building quick prototypes: A number of times, you get ideas while you are on the move or when you are discussing some thing with your friends. In these scenarios, it is much easier to use the out of the box services on the cloud. You can quickly build prototypes with out worrying about versions and scalability. Once you have proven the concept, you can always build a production stack later.

You can also read about components of cloud computing here.

Now that you understand the need of cloud computing for data science. Let us look at various options to run R and Python on the cloud.

 

Options to run Data Science in cloud:

Amazon Web Services (AWS)

Amazon is the king of cloud computing space. They have the largest market share, very good documentation, hassle free environment which can scale up quickly. You can launch a machine with R or RStudio as mentioned in this article. If you float a Linux server, it will come with Python pre-installed. You can install the additional libraries and modules you need.

amazon_ml
You can use AWS machine learning, set up a machine by yourself or even use DataScienceToolBox, which provides all the softwares out of the box. The platform not only offers the services, but also a few large datasets to use in case you want to play around with Big Data.

Because it is the most popular choice, it has a ecosystem and it is easier (than other alternatives) to find resources with right experience. On the flip side, Amazon is usually more expensive compared to some other options. It also does not provide. Also, the machine learning service is not available for Asia Pacific for some reason. So, if you are from these regions, you should select a server based in North America or launch and create your virtual machine on the cloud.

 

Azure Machine Learning

If AWS is the champion, Azure is the challenger. Microsoft has definitely upped its efforts in providing an interface to execute end to end data science and machine learning workflows. You can set up machine learning workflow with their studio, float JuPyTer notebooks on the cloud or use their ML APIs directly.

azure_ml

Microsoft has provided a free e-book and a MOOC on virtual academy to get you started.

 

IBM BlueMix:

If Amazon and Microsoft have grown organically in their cloud presence, IBM has some different ideas. IBM acquired BlueMix and has started marketing its services aggressively lately. The offering is not as straight forward as AWS and Azure, but can still be used by setting up notebooks on the cloud.

It will also be interesting how the data science community uses the APIs provided by Watson.

 

Sense.io:

If all what I have written above sounds too complicated, you should check out Sense. Sense projects can be deployed on click of a button. They offer services based on R, Python, Spark, Julia and Impala, flexibility to collaborate with teams and share analysis. Check out this video to have a first hand view of the offering:

 

Domino DataLabs

Domino is based in San Francisco and provides a secure environment with support of languages like R, Python, Julia and Matlab. The platform provides version control and features to make collaboration and sharing across teams a seamless process.

DataJoy looks like a stripped version of Sense and DominoDataLab currently, but it would be interesting how it will pan out in future. For now, if you want to run R or Python on the cloud, you can look at DataJoy as well.
datajoy

 

PythonAnywhere

If you are building a web application and need to build the website along with the data science stack, PythonAnywhere looks to be the perfect option. As the name suggests, the option is available on Python, but it provides a single window to hosting, website building and running data science.

Challenges in running data science on cloud:

While cloud computing comes with its own benefits, there are a few challenges as well. I don’t think these would stop increased usage of cloud in long term, but they can act as hurdles at times.

  • Reluctance to share data with a third party: I have faced this challenge repeatedly. Irrespective of how much you try to explain a few people about security on cloud, there is a reluctance to share data outside of the company. This is at times driven by regulatory requirements or legal guidance, but at times the reasons are irrational as well. For example, quite a few banks are not comfortable uploading their data on cloud for purpose of analysis.
  • Need to upload / download huge amount of data: In case you have huge amount of data in your data center – a one time upload of this data might be a huge challenge, if your internet infrastructure is not robust.

 

End Notes

Cloud computing is set to gain more penetration for the benefits it offers and it is only a matter of time when some of these services become a norm (if they are not already). Hope you find these services useful and they come in handy, when you need them.

 

If you like what you just read & want to learn more on Big Data, subscribe to our emails, follow us on twitter or like our facebook page.

AWSAzureCloud ComputingdataData SamplingpythonRSense
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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Raja
Raja

DataJoy is shutting down We're sorry to announce that DataJoy shut down on 2nd Jan 2017. This was originally announced on August 3rd 2016. If you have any questions please contact us here In 2015 we started DataJoy with the aim of making data analysis with Python and R easier to learn, more accessible and collaborative. We're pleased that we've achieved some of these goals, but there are some fundamental reasons why we've decided not to continue DataJoy as a product.

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Getting started with Cloud Computing using R Programming
Getting started with Cloud Computing using R Programming
Benefits Of Data Science In AWS
Benefits Of Data Science In AWS

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