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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

DataHack Radio Episode #5: Building High Performance Data Science teams with Kiran R

Pranav Dar
Pranav Dar Last Updated : 07 May, 2019
6 min read

Introduction

Very rarely do you come across someone who brings all of the following skills to the table:

  • Great depth and breadth of technical skills – someone who has won Kaggle competitions and is up to speed with the latest developments in data science
  • Business Sense – Very often, the danger which comes with the first skill is that people start looking at everything as a technical problem and not a business problem. An MBA by education (from one of the top colleges) and someone who is keen to solve business problems
  • Excellent Communication skills – A regular speaker at several data science conferences. A person who knows how to articulate their thoughts, so that they deliver the right impact
  • Always willing to give back – Always up for helping people, someone who has mentored a generation of data scientists coming from India and who feel passionately about building a data science ecosystem.

What do you do when you get some one with these skills?

Well – you ask them to be your guest on DataHack Radio and be a speaker on DataHack Summit and you let them do the talking!

Ladies and Gentleman – today we have a guest like this on the show – please give a round of applause to Kiran R, Director of the Data Sciences Center of Excellence at VMware.

He has also agreed to do a talk at DataHack Summit 2018 this year – so stay tuned.

 

Kiran’s Profile

Kiran is a computer science engineer from MSRIT and a post-graduate from the Indian Institute of Management. Kozhikode (IIM-K). He brings more than 14 years of experience building high performance teams for several leading organizations. He is currently leading the Data Sciences Center of Excellence at VMware.

Kiran is one of the few Grandmasters on Kaggle who has made his mark in the overall top 10 data scientists there. He is also a winner of the KDD 2014 Data Mining Cup. He is very passionate about data science, as you will discover in his conversation with Kunal.

This is a MUST-LISTEN podcast that spans the length and breadth of the various components in the world of data science.

This article is essentially a highlight reel of this incredibly knowledge-rich podcast. Happy listening!

You can subscribe to DataHack Radio or listen to previous episodes on any of the below platforms:

 

Kiran R’s Journey

Kiran’s first job was at Motorola as a software engineer writing C/C++ code. After spending a year there, he decided to pursue higher studies and completed his MBA from IIM Kozhikode in 2006. He took up an offer from Dell as an Analytics Manager and worked on various tools like SQL, Access and SAS (to build propensity models).

His brilliant work ethic and keen innovative mind led him to win Dell’s India Innovator of the Year award in 2012, handed to him directly by Michael Dell!

He set up his own proprietorship under the name ‘Chaotic Experiments’ which he used while participating in several Kaggle competitions. He regularly finished in the top 10 in various competitions and achieved his highest rank of 7 till date during this period. He also did some freelance work in data mining at the time.

From there, Kiran had stints at Amazon and Flipkart. He has been in his current role at VMware since the last four years. His role involves managing a team of data scientists that work across various domains including marketing, pricing and strategy.

One of the more fascinating things about Kiran’s career arc has been his choice to stay in a technical role even after getting his management degree. He was determined not to lose touch with his technical skills and carved out a path for himself accordingly.

 

Kaggle Competitions and Experience

Kiran started off his Kaggle journey back when there weren’t too many data science “enthusiasts” around. The way he used to approach these problems was to build a diverse set of models – a linear model was always a good start, random forest, gradient boosting and then an ensemble model.

He made a lot of acquaintances through the platform and these competitions always pushed him to get out of his comfort zone and learn new techniques and build frameworks. This is something he stressed on, because most data scientist jobs restrict you to only those tools and techniques which the business can support. Platforms like Kaggle and Analytics Vidhya allow you to spread your wings.

 

Competitions and Real-World Job

“If you have a hammer and only one tool, everything looks like a nail to you.”

Participating and learning through competitions helped Kiran a lot when it came to his professional job. There is obviously a difference between the two – like in the industry, you won’t get a ready-made dataset and you will need to convert the business problem into a data mining problem.

But competitions and platforms like Analytics Vidhya help you become an expert in techniques and tools, and finding out why and how things work (or don’t work). Concepts like overfitting can really help you in the industry and competitions also expose you to problems from various domains, which is an added advantage.

Kiran also used his experience to tell us that most problems in the industry are classification based. Regression and segmentation are used as well, but pale in comparison to classification problems. Some of the most widely used techniques are logistic regression and tree-based models.

 

Kiran’s take on how Data Science Works in the Industry

Kiran’s experience has mostly been in the B2C space (with the last 4 years in the B2B sector). He uses the following parameters to classify the data science work companies do:

  • How advanced are the techniques being used?
  • How easily does the company adopt these techniques and frameworks?
  • How is the data science team organised?

Kiran used these pointers to tell us his take on how Amazon, Flipkart and Dell leverage data science. His insights are fascinating and will enrich your knowledge of how data science works in the real-world.

He also elaborated on how his current organization, VMware, uses data science. He went on tell us the difference between B2B and B2C, and how it’s easier to test your models and results in a B2B space than a B2C space (primarily because of the longer purchase cycle the former has).

 

Day-to-day Schedule as a Director, Data Sciences CoE at VMware

Kiran’s role is divided into 2 parts – 90% of it is a functional role, which is to lead the Data Sciences Center of Excellence. The other 10% is to play a sight leader role for enterprise information management (BI, Analytics, and Data Science).

His typical day involves a lot of operational activities, tons of people activities, project deliveries, data science technique brainstorming, interacting with stakeholders, among other things. He has a very hands-on leadership style which helps him juggle and manage these various aspects on a daily basis.

 

Hiring and Recruitment Strategy

Kiran believes hiring is the most important part of team building, because if you have the right people, the job will get done. When he hires people for his team, there are a few critical areas he looks at:

  • Business problem solving skills
  • Analytical ability
  • Programming skills
  • Technology understanding of analytics
  • Culture fit for the organization

I highly recommend listening to the podcast to understand the details of these pointers which Kiran has elaborated on in detail. This will give you a brilliant insight into what an industry leader looks for when hiring a data scientist.

 

The Next 5 Years in Data Science

One thing that has stood out for Kiran is the pace at which data science has grown in recent years. The interest in this field is extremely high right now and he sees this continuing in the future.

Advancement in the type of models we make will increase and the time it takes to train models will decrease, according to Kiran. This will of course be helped by far better computational power (GPUs could well get cheaper) and we should see deep learning being used more and more on structured data.

Another change Kiran predicts in the near future is the increasing pressure on business leaders to adopt AI and data mining. As more and more techniques are developed and become interpretable, businesses will be forced into situations where they have the leverage AI to gain any competitive edge they can get.

Most of the proprietor software, like SAS, will go away according to Kiran. Open source languages like R and Python have already eaten away into the lead SAS had and will continue to gain popularity in the coming years. Open source is the driving force behind machine learning and more and more researchers and organizations are realizing this.

 

Advice to Graduates and Freshers in Data Science

Kiran firmly believes one needs to be good in programming to make a career in machine learning. If you can’t do programming, you will have an almighty struggle to get into this field. If you’re new to this field, Kiran’s advice is to pick up Python and get very familiar with it. You should also eventually learn more than one language which will help expand your skillset.

Learn the fundamentals of data science well. There are tons of excellent resources out there so there’s no excuse not to do this. Once you build your base, you can look at applying for a job in this domain. Another critical aspect, often overlooked, is debugging skills. If you can’t debug your program or code, you will end up getting frustrated with the shortcomings in your script.

Also, develop some complementary skills (like web development or application development) to go along with the data science techniques. As a data scientist, you need to understand how the technical aspects of how to put your model output into a deployment friendly format (among other things).

 

End Notes

This was another quality addition to our DataHack podcast series. Kiran R’s incredible knowledge and his willingness to share it with the community was obvious in this conversation with Kunal. You will learn a LOT of new things about the data science domain.

data science podcastDataHack RadioKiran Rmachine learning competitionsmachine learning podcast
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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