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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 Amazon re-invented Data Science at Amazon AWS re:Invent 2015?

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

Introduction

The ways of analyzing and visualizing data is changing, we must embrace this change.

Let me begin with a 8 line (quick) story.

King Horik, hired a civil engineer to build a city center in his kingdom. He specifically instructed the engineer for the urgency of this construction. After knowing all the details and specifications, the civil engineer promised to do so, but said, ‘It will take a year where I need to bring all my men for this work. But, it would cost you more’.

King realized, with all engineer’s men, the price to build this city center would cost him a fortune. He had already spent enough on storing food for his citizens. He got worried. He called his son, the wisest of all men in his kingdom. He overtook the responsibility of building city center.

He hired a consultant, and described their condition. The consultant offered him a price. He (consultant) said, ‘I would build the city center at my cost. But you’ll have pay for every citizen entering my city center. And, the deal was made.

Learning from the story: City Center is Data Science Applications. The consultant is Amazon. Rest is history.

AWS re:invent 2015, data science. analytics

 

Amazon re:Invent 2015

Amazon celebrated its 4th Annual re:Invent 2015 from 6th – 9 Oct’ at Las Vegas, USA. Had I been around Vegas at this time of the year, I would have definitely grabbed a seat at this festival.

Let me tell you what happened at this festival (in brief).

At re:Invent 2015, Amazon AWS showcased its years of hard work and dedication spent in building good incredible products. You name a technology /service, and amazon had a product to offer.

Be it Internet of Things, Analytics, Databases, Storage and Content Delivery, Security and Identity, Compute, Mobile Services, Management tools, amazon totally captured world’s attention by its unique offer pricing and services.

Amazon AWS has become the fastest growing Multi-Billion Dollar Enterprise IT Company in this world. The guy presenting is Andy Jassy, Senior Vice President, AWS (Proof: Image below). Screen-Shot-2015-10-07-at-11.35.38-AM

 

Why am I telling you all this ?

Amazon has built great products and it has offered these products at incredible prices. But, why are you telling me all this? I bet you thought this for a moment! 😉

Well….Amazon has been the king of cloud computing and when they start rolling out products aimed for data scientists and business intelligence (BI) professionals, it is better that you watch rather than being caught off-guard!

Like always, the products Amazon has come out with aren’t made for just organizations, but for personal use as well. And they aim to disrupt a multi-billion dollar market with deep customer dis-satisfaction. Let me tell you how!

P.S – This article only highlights big data & analytics related knowledge from re:Invent 2015. I do not intend to promote any product or service here.

 

Data Science Products

Not only the process of collecting, uploading, storing and processing data on AWS has become faster, but addition of data-centric services has made this a comprehensive platform for a data scientist. Below are some products introduced at re:Invent 2015:

 

AWS IoT

IOT

IoT is no longer a dream. It’s right here.

AWS IoT enables a cloud service which allows an easy and secure connection with cars, factory floors, sensory grids, factory engines and almost anything which transmits data. Amazon has made sure that this service is great fit even for devices that have limited memory, processing power or battery life.

AWS IoT is build up of components such as things shadows, rules engine, message broker, SDKs, things registry whose sole job is to ensure the devices stay connected irrespective of bad connectivity, lack of storage and other unfavorable conditions.

The best part is, first 250,000 messages exchanged among these devices are free! ANd it is dirt cheap even after that.

If this excites you, check out Dr. Werner Vogels, CTO, Amazon introducing AWS IoT.

 

 

Amazon QuickSight

quicksight

Next surprise, Amazon has made the process of delivering Business Intelligence (BI) solutions faster (in seconds) with QuickSight.

For those of you, who know the BI industry, you would know how broken it is! Before the QlikView / Tableau became popular, a BI project remained gargantuan task. It would cost you a lot, people will be working on it for years and by the time some thing is ready, the customer requirement would have changed! After spending ton of money, human resource and time, the Organization would move to next vendor with a hope that they will deliver and similar story continues for most of the projects.

While solutions like QlikView and Tableau have already changed this to some extent, Amazon is setting things up for disruption. QuickSight, not only delivers faster BI solutions, but at 1/10th the cost of traditional available solution. Isn’t that amazing ?

This software is designed to work on many data types of collected using ad targeting, customer segmentation, forecasting & planning, marketing & sales analytics, inventory & shipment tracking and many more. In short, if you have data and you want its insights, switch to QuickSight. It will be available at $9 per user month for a year commitment and would be $18 for corporate users.

Here are the pricing plan:amazon_pricingI am just waiting to give this a spin!

 

Amazon Kinesis Firehose

Kinesis

You didn’t like QuickSight? No problem!

Amazon’s Firehose provides the facility of integrating your existing BI tools and dashboards, thereby enabling real time analytics. It provides the easiest way to load streaming data into AWS.

This software allows you to spend more time focusing on your application and less time on your infrastructure. Since, Firehose, automatically takes care of monitoring, scaling and data management for users. It can also be used with other amazon services such as Lambda, Redshift, EMR, aimed at making this process of data management reliable and faster. Check this 2 minute video.

 

AWS Database Migration Service

Database

As the name suggests, this service allows you to shift data to AWS easily and securely. Infact, while migration happens, the source data remains fully operational.

This service can be used with all forms of data from all types of widely used commercial and open databases. For example: You can migrate data from Oracle to Oracle, as well as Oracle Microsoft SQL Server. There is no limitation at data transferring even using heterogeneous data sources.

This includes facility such as Schema conversion tools and many other build in features which facilities successful transfer of data between databases.

 

AWS Lambda (Update)

lambaIf you are still unaware of Lambda, let me introduce it to you!

At times, you would have faced the difficulty of running code on server. Like your server would have failed to compute code successfully, or worse ( it crashed). Atleast I have!

Lambda provides you the opportunity to run code without thinking about servers. Incredible fact is, you only pay for the compute time – there is not charge when your code is not running. You just need to upload your code and sit back. This software takes care of everything required to run and scale the code with high availability.

This year, at re:Invent 2015 had amazon has announced Lambda’s extended support for Python function, increased function duration, function versioning & aliasing and many more.

Summing up, among all, I found these 5 products to be most useful to a data science / big data professionals. Whether you analyze or you visualize data, these product surely will make your life simpler and happier. In case, you feel intrigued to find about more products, you can always find them here.

 

6 Must Watch Videos for a Big Data Scientist

re:Invent wasn’t just limited to product showcasing, but extended to delivering knowledge on some of the most talked about topics in data science / big data industry. Here are the top 6 videos, I found useful for a data science professional. Remember, the ways of analyzing and visualizing data is changing, we must embrace this change.

1. Deep Learning – Going Beyond Machine Learning

 

2. Data Science & Best Practices for Apache Spark

 

3. Real-World Applications with Machine Learning

 

4.  Big Data Architectural Patterns and Best Practices

 

5. Amazon Elastic Map Reduce

 

6. Your First Big Data Application on AWS

 

End Notes

With AWS re:Invent 2015, Amazon has not only launched / renewed its product line. It has challenged the traditional BI industry in the way it does best. It has also democratized IoT by launching its product.

Whether these highly competitive products end up becoming the kings of their own markets? – only time will tell. But, I’ll put my money on Amazon to do so.

With this article, my aim was to provide you the reserve of data science knowledge from re:Invent 2015. Hopefully, I’ve done justice to it. Finger crossed, though! While writing this last line, I’m curious to know which product you would like the most? Do share it in the comments section below!

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter 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

Sanjay
Sanjay

Wonderful and full of insights article . Thanks Kunal!!!. All products are good . I feel AWS Database Migration Service will be the one which I think will be used mostly . Specially with its capabilities to transfer between heterogeneous data sources.

123

Lanneseri
Lanneseri

Did I understand correctly that QuickSight has a graphing option that updates from a live feed from the original data source? Has anyone worked with this and if so, what is your opinion on its efficacy and the complexity of setting it up? Thnx.

123

Ben
Ben

Good article but it's Amazon Kinesis Firehose, not Firehouse.

123

Kunal Jain
Kunal Jain

Thanks Ben for highlighting! My bad - just corrected it. Regards, Kunal

123

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