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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

Shaping the Future of Work: Insights from Meta’s Arpit Agarwal

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

The COVID-19 pandemic has transformed the workplace, with remote work becoming a lasting norm. In this episode of Leading with Data, Arpit Agarwal from Meta discusses how the future of work involves virtual reality, enabling remote collaboration that mirrors in-person experiences. Arpit shares insights from his journey, emphasizing pivotal moments and the challenges of analytics in product development’s early stages.

You can listen to this episode of Leading with Data on popular platforms like Spotify, Google Podcasts, and Apple. Pick your favorite to enjoy the insightful content!

Key Insights from our Conversation with Arpit Agarwal

  • Future work hinges on virtual reality for remote collaboration.
  • Launching a data science team fosters innovation and business impact.
  • Early product-stage data science prioritizes quality, using internal tests and feedback.
  • Hiring for data science needs technical prowess, problem-solving, and strong character.
  • Data science career growth demands broad exploration followed by specialized expertise.

Join our upcoming Leading with Data sessions for insightful discussions with AI and Data Science leaders!

Now, let’s see the questions Arpit Agarwal answered about his career journey and industry experience.

How has the COVID-19 pandemic reshaped the way we work?

The pandemic has fundamentally changed our work dynamics. We’ve transitioned from office-centric environments to embracing remote work as a new reality. Even with return-to-office policies, a significant portion of the workforce will continue to operate remotely. The challenge lies in maintaining productivity and fostering connections that were once built within office walls. Current tools fall short in replicating the in-person experience, which is where Meta’s vision comes into play. We’re developing products that provide the feeling of working side by side, understanding each other’s body language, and collaborating effectively, all within a virtual space.

Can you share your journey from college to becoming a leader in data science?

My journey began at BITS Goa, where I pursued a computer science degree. Initially, I was academically focused, but BITS allowed me to explore other interests, including data interpretation. I led a puzzles club, which sparked my interest in data. Post-college, I joined Oracle, where I worked in data warehousing and business intelligence, helping clients make data-driven decisions. This experience solidified my interest in analytics and its business applications. I pursued an MBA to deepen my business understanding and later joined Mu Sigma, where I honed my analytics skills. My career progressed through consulting roles and leadership positions in startups like Zoomcar and Katabook, where I tackled diverse data science challenges.

What were the key moments in your career that shaped your path?

Joining Zoomcar was a pivotal moment. I was tasked with building the data science team from scratch, which allowed me to work on innovative projects like driver scoring systems using car data. This experience gave me the opportunity to work closely with C-level executives and influence business decisions directly. Another significant moment was my time at Katabook, where I helped the company become data-driven and launched various analytics initiatives, including loan offerings based on machine learning models.

How does Meta envision the future of work, and what role does data science play in it?

Meta’s vision for the future of work revolves around virtual reality, aiming to create a space where remote collaboration is as natural and effective as in-person interactions. Data science plays a crucial role in setting ambitious organizational goals for products that are ahead of their time. It involves aligning product strategy with these goals, ensuring product quality, and managing diverse, global teams. Data science also addresses the challenge of analytics for products that are in the early stages of development, where customer data is scarce.

What are the challenges of doing analytics for products that are in the 0 to 1 phase?

Analytics for products in the 0 to 1 phase is challenging because there’s limited customer data to guide decision-making. The focus is on ensuring product quality and functionality, which is critical for enterprise products. We rely on internal testing (dogfooding), alpha and beta testing with select groups, and user research to gather feedback and validate the product’s direction. Once we have a solid foundation, we can launch the product to a broader audience and use data science to measure adoption, retention, and iterate based on user feedback.

How do you assess candidates for data science roles, especially in emerging fields like generative AI?

When hiring for data science roles, I look for candidates with strong problem-solving skills, a deep understanding of machine learning fundamentals, and proficiency in programming languages and data manipulation. For generative AI specifically, candidates should have expertise in the relevant domain, such as natural language processing or computer vision. Additionally, I value character and work ethic, which I assess through behavioral questions, reference checks, and a candidate’s ability to explain their projects in depth.

What advice do you have for individuals starting their careers in data science?

For beginners in data science, explore diverse interests before specializing. Utilize abundant free learning resources, prioritize skills for value and fulfillment over quick financial gains. Seize opportunities, even in smaller projects or companies, for substantial growth. Recognize that hard work forms the basis of luck; success is an ongoing journey of learning and improvement.

Summing Up

Arpit Agarwal’s journey exemplifies the impact of data science on diverse industries. Meta’s vision for the future of work highlights the pivotal role data science plays. Aspiring data scientists can glean valuable advice from Arpit’s emphasis on skill development, embracing opportunities, and the enduring journey of continuous learning. 

For more engaging sessions on AI, data science, and GenAI, stay tuned with us on Leading with Data.

Check our upcoming sessions here.

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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