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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

Mastering the Art of Data Science Strategy: A Conversation with AI Visionary Vin Vashishta

Nitika Sharma
Nitika Sharma Last Updated : 28 Nov, 2023
4 min read

Dive into the transformative world of data science with Analytics Vidhya’s groundbreaking series Leading With Data. In this exclusive interview from the series, Kunal Jain, CEO of Analytics Vidhya, engages in a riveting conversation with Vin Vashishta, a distinguished AI leader. Unveil the secrets of Vin’s journey, marked by a strategic shift from technical roles to leadership, as he shares invaluable insights and experiences.

In conversation with Vin Vashishta, Founder & AI Advisor, V Squared

Let’s get started!

Key Insights

  • Embark on Vin Vashishta’s extraordinary journey, transitioning from installing PCs to becoming a trailblazer in AI strategy.
  • Uncover his perspective on critical decision-making for leaders: balancing quick solutions with the reliability of Data Science applications.
  • Gain insights into Vin’s unique process of foreseeing industry trends before they explode, guiding his strategic moves in the ever-evolving landscape.
  • Explore the genesis of his startup and witness its evolution over the years, offering a firsthand account of the challenges and triumphs.
  • Delve into Vin’s belief in the significance of a business vision, even for late adopters of cutting-edge technologies, as a driving force for sustained success.
  • Understand why Vin advocates for technical experts branching into diverse domains, emphasizing the necessity for forward movement in the rapidly advancing field.

How did you Embark on your Data Science Journey?

I started my education to go into civil engineering, following in my father’s footsteps. However, my first encounter with programming at 12 profoundly impacted me. I was captivated by the ability to create something in a virtual environment.  Took a programming class during my first year of college and immediately knew it was my passion. My focus switched to programming, which was around 1994-95. My journey into data science was not straightforward. I graduated during the first AI hype cycle in the ’90s. Despite my aspirations to work for Microsoft and build advanced models, I was in more traditional software engineering roles. I worked my way up from installing PCs to building websites and database administration. My first corporate job involved installing software and platforms in-house and working directly with clients. This experience was crucial as it taught me the importance of delivering on software promises.

Top 10 Data Science Job Profiles for the Future

What were the Early Challenges you Faced with Data Science Models?

My first data science project was in 2012, and back then, we didn’t have the libraries and resources we have today. I built models in various languages, including C, C++, and Java, because we had to optimize everything due to technology limitations. We didn’t have the cloud infrastructure we have now, and data at scale was only available to massive companies. My early clients were large companies, and it wasn’t until around 2016 that small and midsize businesses began to approach me. Working with these smaller clients introduced me to real-world constraints like budget and time, a different experience from the corporate world.

How did you Transition from Technical roles to Strategy and Leadership?

After being laid off in 2012, I quickly turned my side consulting practice into a full-time business, V Squared. Initially, my work was more BI analytics than data science. As the field evolved, I began building statistical models and working with scientists who taught me the importance of model explainability. This experience led me to bridge the gap between traditional machine-learning approaches and the rigorous standards of science. I learned to discern when a quick and more reliable solution was necessary. This understanding of balancing value delivery with technical rigor propelled me from technical roles into leadership and strategy.

How did Social Media Influence your Business Growth?

Social media, particularly Twitter and later LinkedIn, played a significant role in expanding my business. It changed my sales funnel completely, increasing the number of inquiries and opportunities. I found a unique voice by discussing data science and machine learning from an executive perspective, which set me apart. My brand has always been about pragmatism, discussing what works in the field and what doesn’t, based on my daily work and experiences.

What does your Current Role as an AI Advisor Entail?

Nowadays, my role is primarily advisory. Past clients or colleagues often bring me in to sit in on calls, answer questions, and explain technical concepts regarding monetization for businesses. For example, when Apple announced its new silicon, I sent a newsletter explaining the significance of running inference on a watch and what it means for IoT. My job is to help C-level leaders understand the implications of technology for their business and how to turn it into a value story.

What are your Thoughts on the Future of Data Science and Generative AI?

I believe data science has the potential to live up to its hype because it works and delivers on its promises. I saw the potential of generative models like GPT early on, and although I didn’t predict the exact impact of ChatGPT, I knew the direction we were headed. The challenge is not just having the vision but also being able to convince businesses to prepare for and adopt these technologies.

What Advice do you have for Data Scientists Transitioning into New Roles?

I advise you to recognize when you’ve hit a technical plateau and focus on multiplier skills that improve the team and organization. Instead of continuously learning new technical skills, develop capabilities to enhance everyone around you. This could mean transitioning into roles like principal, staff, or distinguished data scientist or moving into leadership, product management, or strategy. When you feel bored or trapped, consider becoming a multiplier to reignite your passion and help others grow.

Can you share Some Insights from your Book and your Experience as an Author?

Writing a book was the hardest thing ever, but it was a great experience. My book has received mixed reactions, with some technical practitioners finding it lacking in code and implementations. However, it has found its niche with sales teams, C-level executives, and specialized practitioners looking to transition into strategy roles. The book focuses on creating value with data science, not just delivering more technology.

What Excites you most about the Next Few Years in Data Science?

I’m excited to see the field mature. We now have senior data scientists with leadership experience who are forcing the field to grow. Data science is unique in that it can deliver on its promises, and I’m looking forward to watching this evolution.

Summing Up

From grappling with early challenges in model development to harnessing the power of social media for business growth, Vin’s story is a testament to resilience and adaptability. As an AI Advisor, he emphasizes the crucial role of translating technical advancements into tangible business value.

Stay tuned with us on Leading with Data for more such inspirational data talks. See you next week with another exciting episode!

AI advisorData science modelsData StrategyLeading With DataVin Vashishta
Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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