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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

Moving beyond frontiers in Data Science – Interview with Mahesh Kumar, Founder & CEO, Tiger Analytics

Kunal Jain
Kunal Jain Last Updated : 06 Apr, 2017
6 min read

Introduction

This DataFest, we are bringing thought leaders & influencers from industry as part of our interview series – Moving Beyond Frontiers in Data Science section. We are featuring founders of top data science & analytics startups. These leaders have challenged the status quo to redefine the industry.

We have done these exclusive interviews to understand their journey and learn from their perspective.  Here’s the first interview of the month of – Mahesh Kumar, Founder & CEO, Tiger Analytics.

Mahesh has 14 years of experience in predictive analytics, statistical modeling and machine learning with applications in the fields of retail management, digital marketing, e-commerce, customer analytics, transportation, energy planning, and healthcare.

Mahesh was an Assistant Professor at R.H Smith School of Business before starting Tiger Analytics. He holds a PhD in Operation Research from Massachusetts Institute of Technology.

Read on to know about his journey & story of setting up Tiger Analytics.

 

Kunal: Hi Mahesh, thanks for finding out time for this interview. You started Tiger Analytics after teaching Operation Research at R. H. Smith School of Business. What gap did you discover in analytics industry which persuaded you to start Tiger Analytics?

Mahesh: When I was teaching data analytics, I used to consult for several clients for their analytics needs. In the process, I realized that there were lots of players in BI & Reporting, but very few focused on advanced analytics – especially being able to use data science to address complex business problems. Hence I started Tiger Analytics with an emphasis of creating an organization that uses advanced analytics focused on delivering tangible business outcomes

 

Kunal: How did you meet your co-founder and the initial team?

Mahesh: I connected with Pradeep Gulipalli, my co-founder, during the early days, while looking for someone with complementary skills and the passion for building a firm truly specializing in advanced analytics. Between us we covered functional expertise, business operations, revenue generation, global delivery etc.

To build the initial team, Pradeep and I primarily used our professional networks to identify personnel with data science and advanced quantitative skills. Today, we are a team of 150+ data scientists and data engineers, solving problems using advanced analytics.

Pradeep Gulipalli & Mahesh Kumar (left to right)

 

Kunal: What were the challenges you faced during the initial months of Tiger Analytics?

Mahesh: There were several challenges ranging from sales to recruitment to infrastructure. When we started, the only way to convince potential clients to work with us was through our individual resumes. Every sales discussion would be like a business, technical, and data science interview rolled into one. Today, as a company, we have built credibility as a premier data science solutions and services firm.

Similar challenges existed when bringing together a team. To join us, one had to believe in our vision to build the world’s best advanced analytics firm, when we even did not have an office. Today, we have a top-notch data science community and attract top talent.

 

Kunal: Tell us about your first success story?

Mahesh: One of our initial projects was with a leading North American railroad company. One of their biggest expenses was in replacing damaged wheels. To identify them, they had sensors on the railway tracks, which streamed data when wheels pass over them. The problem was that, at times the sensors would be damaged, because of which they would provide wrong readings, resulting in the replacement of perfectly healthy wheels. This resulted in significant avoidable costs.

We built a warning system that would analyze the sensor data to predict if a sensor was damaged and needed inspection. This project was a huge success and today our algorithms process sensor data from railway tracks all over the US and Canada. There are lots of similar interesting stories featured on our website and I would encourage readers to go through them to get a feel of our work.

 

Kunal: What differentiates you from your competitors?

Mahesh: Our ability to deliver quality data science and advanced analytics solutions is one of the biggest factors for multiple Fortune 500 companies from a wide range of industries to work with us.

Internally, we have a significant emphasis on culture and work-life balance and strive to ensure that people are happy to work with us. The atmosphere is informal and authentic, and very professional. Additionally, opportunities to solve cutting-edge big data analytics problems keeps our team motivated and excited.

We are building a company that is focused on quality first – in terms of work, customers, and employees – and are not in a haste to scale rapidly, as it could cause the quality of work to suffer. We will scale but in a gradual manner.

 

Kunal: Did you expect such massive growth & success in a short span of time?

Mahesh: The response so far has been phenomenal. We have worked with 50+ clients in a span of 5 years: many of them are marquee names from the Fortune 500 roster, and almost all of them are repeat customers. Our business has been expanding at a healthy pace.

In 5 short years, we have established a niche for ourselves in the data science space as a dependable partner for organizations across the globe. We have been ranked by Deloitte as among the Top 10 fastest growing tech firms in India in 2016.

 

Kunal: What are the top priorities as the CEO of Tiger Analytics?

Mahesh: My top 2 priorities would be:

  1. Work with clients to identify areas where advanced analytics can help address business issues – guide them through the new wave of analytics that is sweeping organizations worldwide
  2. Provide a good work environment where our employees feel excited and motivated – help them learn the nuances of analytics and the big picture of business applications – groom the analytics leaders of tomorrow

 

Kunal: You have a presence in multiple locations – how is the set up in each of these countries?

Mahesh: We started in the San Francisco Bay Area and have our primary delivery center in Chennai, India. Our teams work out of both these offices as well as from customer locations globally.

 

Kunal: What are the different analytics solutions offered at Tiger Analytics?

Mahesh: We offer custom services and solutions in the areas of Marketing Science, Customer Analytics, Operations & Planning, and Risk Analytics. The services span Data Science, Big Data Engineering, and Business Analytics.

We have developed a portfolio of proprietary process accelerators and solution blueprints that underpin our project execution.

 

Kunal: The awareness for analytics is increasing among students & professional. Do you think the candidates are well-prepared for the role today?

Mahesh: We find that business analytics courses including platforms such as Analytics Vidhya, do a commendable job in equipping aspirants with the tools required for a career in analytics. However, we see some very frequently occurring gaps – the primary one being of first understanding the business problem and data before beginning a modeling exercise.

 

Kunal: What are the skill sets you look for in a candidate while hiring? And what is the selection process?

Mahesh: Depending on the role which we hire for, we look for programming, quantitative, and/or business aptitude. For someone starting their career, deep understanding of data science is not expected, but that becomes the expectation for a relatively senior role.

More than the above, culture-fit and coachability of a candidate is very important to us. In a field that is evolving rapidly, one should be able to unlearn and relearn.

 

Kunal: What impact do you think analytics & big data will have in the next 5 years?

Mahesh: We are seeing a significant increase in the volume, variety & velocity of data, decrease in data storage costs, increase in computing power, and availability of new methods and tools. Most crucially, with an increase in business adoption, the scope of big data analytics has increased manifold.

The emergence of positions such as Chief Analytics Officer and Chief Data Officer indicates how seriously analytics is now being viewed at the highest level. Enterprises are understanding the need to think data-first as it is increasingly playing a critical role in determining business direction.

We anticipate that analytics will see significant growth in the next 5 years.

Kunal: Any advice for students and working professionals?

Mahesh: Analytics is a very attractive career option today. Whether you are a student or a professional, if you are someone who wants to solve problems, work with data, learn new math concepts and technologies, then you should seriously consider analytics as a career option.

There is significant innovation happening in the field and new application areas are emerging. In the future, we will see several professional opportunities that don’t exist today. It’s exciting to be a part of this new wave!

 

Kunal: Thanks Mahesh for your time and thoughts. I’m sure a lot of people will be inspired by your story and will be motivated to join the analytics industry. 

Check out the activities coming up in AV DataFest 2017

Learn, compete, hack and get hired!

Accounting Analyticsadvance analyticsAdvanced AnalyticsAnalyticsmahesh kumarquantitative analysisquantitative modelingTiger analytics
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

Mohit Kumar
Mohit Kumar

Thanks Kunal for sharing these interviews, It really gives insights into what goes inside the big data, analytics companies. And how people are leveraging the analytics part to solve real world problems. The point i get from their first success story is that ,"One must not treat data as word of gospel even if it is coming from automated sensors,machines. " . And Secondly, feedback part is very important for improving the model apart from all the fancy algorithms and hyperparameter tuning.

123

Amar
Amar

Nice.

123
Mantej Singh Dhanjal
Mantej Singh Dhanjal

Great article!

123

Raj Salecha
Raj Salecha

Thanks for sharing the interview..

123

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