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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

Data Scientist’s Insights: Strategies for Innovation and Leadership

A
Analytics Vidhya Last Updated : 06 Jun, 2023
7 min read

Introduction

Welcome back to the success story interview series with a successful data scientist and our DataHour Speaker, Vidhya Chandrasekaran! In today’s data-driven world, data scientists play a crucial role in helping businesses make informed decisions by analyzing and interpreting data. With their expertise in statistics, machine learning, AI, and programming, they are able to extract meaningful insights from complex datasets.

This article features an interview with Vidhya, a successful data scientist who currently leads the Machine Learning and AI products at PayPal. With over 18+ years of experience, Vidhya shares insights into her career journey, the challenges she faces as a leader in applied data science, and her strategies for fostering innovation in her team.

She also discusses the importance of proper planning in AI projects. Also, shares advice from her mentors on trying new things and failing fast. So, let’s embark on this thrilling interview session with Vidhya.

Interview Excerpts with Vidhya Chandrasekaran

AV: Hello Vidhya! Please introduce yourself and give us an insight into your professional and educational background.

Interview

Vidhya: Hi, I work as a senior manager at PayPal and lead ML and AI product management. I have about 18 years of experience in data and 8 years in leadership. With Bachelor’s in Mathematics and a Masters in Computer applications, I also did a 1 year PG program in AI with Great Lakes. Currently, I am doing my Doctoral research program on Personalization with Data.

At PayPal, over the past 5 years, I have had the opportunity to lead and build BigData, ML Engineering, AI product, and ML Science teams and initiatives. In my current role, I show the ML for Merchant products and Marketing, building Product recommendations and personalization solutions.

AV: That sounds spectacular and insightful. You started as a Software Engineer; how did you get into the field of Data Science?

Vidhya: I was always connected with Data in my career one way or the other, starting as a Database application engineer to being an architect to leading ML initiatives now; data has been the connecting thread. However, my career journey has been dynamic due to my personal circumstances. I was a homemaker for many years after graduation and had to strive harder than most to get an entry in tech owing to a lack of experience. But once I started, I found opportunities in each role that prepared me for the next role. I have also been blessed with great mentors and managers in all the companies I worked for, who gave me opportunities to indulge in my aspirations.

AV: I agree. A great mentor can help you climb the corporate ladder easily, which shows in your career trajectory. How do you foster a culture of innovation as a leader in Applied Data Science?

Vidhya: Unlike research teams, Applied ML teams operate under tight guidelines and strict timelines. Due to these constraints, they sometimes do not have the same luxury to explore new technologies, algorithms and implement new papers. However, I have remained conscious of the importance of innovation in ML teams, where every single day, a copious amount of new things going on.

Data Scientist | Interview | Machine Learning | AI

Here are some of the strategies that have been instrumental in my management of an applied ML team without compromising on long-term innovation.

  1. Providing 10 to 15% of the time for working on stretch assignments or research/Proof of concepts.
  2. Encourage the culture of intra and inter-team collaboration with an emphasis on feedback loops – Innovation happens more in groups than in isolation.
  3. Allow safe space to fail. Machine learning is experimentation compared to software engineering and has many possibilities of failing. If failing safely is not allowed, the attempt to innovate is curtailed.
  4. Make innovation a part of the goals.

AV: Those are some pretty interesting strategies you follow. That’s great! Although, managing a team can be difficult. What do you consider a top challenge in leading a Machine learning and Artificial Intelligence team?

Vidhya: One of the challenges is striking a fine balance between timely business deliveries and innovation which is a time-intensive process. Apart from the fact that innovation is non-negotiable in any Tech Industry, also as a leader, we are responsible for our team’s careers by providing them opportunities for continuous learning and keeping them motivated, especially in AI and Data Science where the related tech is constantly moving at a faster pace.

AV: What do you consider is key to succeeding in the Applied AI team?

Vidhya: Most projects fail due to poor planning. AI Product managers play a key role in this phase. The key first step is understanding the business problem and where AI can solve it. As part of the planning, estimating opportunity sizing and agreeing upon clear, well-defined KPIs is of tantamount importance. A key next step is working backward from the expected outcome to arrive at and crystalize the appropriate metric or KPI. For example, the model could bring more customers to the website or drive new customer acquisition. Model metrics like Precision, Recall, or F1 are often misunderstood as KPI; the business would not worry about the model metrics but would be very much interested in the business metrics.

Developing capabilities and processes to bring an idea to fruition is another crucial aspect of success. Incorporating capabilities such as data catalog search functionalities, retraining automation, monitoring capabilities, continuous integration, etc., can significantly shorten the time required to test and learn from your ideas. This approach also guarantees that the valuable resources of our teams are not diverted towards monotonous and repetitive tasks but instead utilized to create engaging solutions.

AV: Going back to your mentors, what is one piece of advice that you got from your mentors, and how did you implement it?

Vidhya: There are two pieces of advice I got from 2 of my mentors, which is:

  1. Try At Least One New Thing in Every Model Development. The ‘newness’ can be anything like a new algorithm, a new type of data that is experimented or different feature engineering techniques.
  2. Fail Fast: Try to get the model deployment as soon as possible. Going for a perfect model, excellent results, and a new in-the-market algorithm could be enticing but oftentimes comes with an opportunity cost. Try a simpler, lean model to measure success or fail fast as soon as possible. We can always go for improvisation later. Shooting for a faster time to market and incrementally improving it to a cutting-edge model is critical.

AV: According to your profile, you have teaching experience as a mentor; how do you think that your experience as a mentor has influenced your career growth and success in machine learning?

Vidhya: Teaching is a great way to learn. I signed up to teach AI/ML on the weekend when I was doing my PG program in AI. When you teach a topic to someone else, you have to organize your thoughts, break down complex concepts into simpler ones, and explain them clearly and concisely.

Data Scientist | Interview | Machine Learning | AI

I have also learned from mentoring e-commerce business leaders on Data driving marketing. As part of the Chennai Entrepreneurial chapter’s mentorship program. This gave me a perspective on strategic/ structural thinking, decision-making, planning and also made me come out of my comfort zone.

Vidhya: I followed a disciplined learning approach for many years, spending either regular hours in the week or on the weekends learning from courses, blogs, and books. These days, though I continue my individual learning, I spend lesser time than before as I learn from my team directly when they try novel things; that is a perk of working with a team who are way smarter than you.

The latest development that I am very excited about, like several others, is what is happening with Generative AI, which seems to have all ingredients to disrupt everything from the creative industry to personalization. This is set to revolutionize the way businesses are done. As more concerns are raised regarding ethics and the possibility of obfuscated narratives, I am curious to see how Governments, organizations, and policymakers create processes to fortify the social fabric against potential threats.

AV: So, wrapping up, I would like to ask you, what is your advice for people who are planning to transition to Data Science? 

Vidhya: Although there is no one-size-fits-all approach, I believe that the strategies and plans for transitioning to a data science or ML role are dependent on an individual’s career stage.  Transitioning from a junior role is significantly simpler than a mid-management or a senior-level role.  Those that are individual contributors can start their learning from basics, learning statistics, probability, Mathematics, and basic ML concepts.  There are several free courses and YouTube resources. Following a bottom-up approach is important when learning, as it is easy to get a model done in a few lines of library code. This knowledge is not sustainable.   There are several hackathons that one can compete to learn or just follow along the code to understand different feature engineering and model strategies. Once you have gained sufficient expertise on the concepts, plan to switch over to a team internally that has exposure to ML projects.

While it is crucial for individuals in senior and middle management positions to comprehend the capabilities of machine learning, it is even more critical to acquire expertise in the broader context of artificial intelligence strategy, engineering, and integration requirements. They can start to identify opportunities to innovate in their own area/project or domain and then communicate the success to their leadership and stakeholders. They can use their small initial successes to build ML capabilities and teams internally.

Some Resources for learning ML ground up – ML and Deep learning specializations by Andrew NG in coursera:

Conclusion

In this interview, Vidhya Chandrasekaran’s journey showcases the transformative impact of data science in the technology industry. Her insights, challenges, and strategies for fostering innovation offer valuable lessons for aspiring data scientists and leaders in applied data science.

She has shown that effective planning, understanding business problems, and defining clear KPIs are critical to succeeding in the applied AI team. Her mentors’ advice to try new things in every model development and fail fast has also contributed to her success. Vidhya’s expertise and leadership continue to shape the field of data science. It serves as an inspiration to future data scientists and leaders in applied data science.

If you wish to read more about our interview on the journey of a person in tech, click here.

A
Analytics Vidhya

Analytics Vidhya Content team

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