Sagar is a Data Science leader with over 12 years of experience, built high-performing teams from scratch by hiring, coaching, and enabling the growth of team members. Sagar is recognized for solid applied skills and succesfully driving projects/teams in solving complex business problems in multiple areas. He loves building a wide range of scalable data solutions for decision support and embedding them into product offerings and enjoys solving problems and thrives in a fast-paced environment functioning at the intersection of data, engineering, product, and business teams.
Sagar is passionate about giving back to the broader community, specifically, interested in helping students and early career professionals who are looking to break ground in data science, he believes that project-based learning and feedback are the best methods to create confident and technically skilled professionals who will in turn give back to the community. Sagar also appreciates the opportunity to connect with like-minded professionals and shape the mentoring programs to improve the reach and help more students, he understands the value of mentorship and coaching at every stage of his career, reach out to Sagar if you are looking for input in shaping up your DS career.
My Mentoring Topics
- DS & ML for Product, Marketing, and Business
- Building a data science career
- Building and nurturing high-performing data science teams
E.
4.February 2023Sagar was very helpful. He gave me great feedback on what projects to tackle to build my portfolio. He was kind and easy to talk to.
M.
23.December 2022The session was very helpful. Sagar provided some insightful advice and gave me actionable suggestions on how to improve my journey towards a career transition into the tech field.
D.
20.December 2022Yes, our session was very helpful. As a new data analyst without many connections in the industry, meeting with Sagar to share our paths and learning opportunities helped reorient my work. Whether resume building and advice, ideas for networking, learning and practice opportunities like Kaggle or hackerrank, or tips for finding and completing projects, Sagar helped me understand what would help me grow. Although it takes time to build an understanding of a mentee's skills, I believe Sagar gave appropriate examples and feedback fit towards helping me. For the time being, I want to adjust my resume, portfolio, practices, and learning based on what I can take from Sagar, but also feel confirmed that I am heading in the right direction and networking well.
A.
18.November 2022Sagar is very kind and experienced in data field, he is a great source for anyone looking for advice in different stages in their career. Thank you for your help and much appreciation.
S.
21.October 2022You speak and listen well. Your advice is concise and relevant.
Anonymous
10.September 2022Great mentor.
He helped me update my CV and give me straight instruction what should I do before next meeting.
I'm looking forward to meeting him again.
Radical Candor: Fully Revised & Updated Edition - Be a Kick-Ass Boss Without Losing Your Humanity
Kim Scott
* New York Times and Wall Street Journal bestseller multiple years running * Translated into 20 languages, with more than half a million copies sold worldwide * A Hudson and Indigo Best Book of the Year * Recommended by Shona Brown, Rachel Hollis, Jeff Kinney, Daniel Pink, Sheryl Sandberg, and Gretchen Rubin Radical Candor has been embraced around the world by leaders of every stripe at companies of all sizes. Now a cultural touchstone, the concept has come to be applied to a wide range of human relationships. The idea is simple: You don't have to choose between being a pushover and a jerk. Using Radical Candor—avoiding the perils of Obnoxious Aggression, Manipulative Insincerity, and Ruinous Empathy—you can be kind and clear at the same time. Kim Scott was a highly successful leader at Google before decamping to Apple, where she developed and taught a management class. Since the original publication of Radical Candor in 2017, Scott has earned international fame with her vital approach to effective leadership and co-founded the Radical Candor executive education company, which helps companies put the book's philosophy into practice. Radical Candor is about caring personally and challenging directly, about soliciting criticism to improve your leadership and also providing guidance that helps others grow. It focuses on praise but doesn't shy away from criticism—to help you love your work and the people you work with. Radically Candid relationships with team members enable bosses to fulfill their three core responsibilities: 1. Create a culture of Compassionate Candor 2. Build a cohesive team 3. Achieve results collaboratively Required reading for the most successful organizations, Radical Candor has raised the bar for management practices worldwide.
ViewIntroduction to Algorithmic Marketing - Artificial Intelligence for Marketing Operations
Ilya Katsov
Introduction to Algorithmic Marketing is a comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers. It summarizes various techniques tested by major technology, advertising, and retail companies, and it glues these methods together with economic theory and machine learning. The book covers the main areas of marketing that require programmatic micro-decisioning - targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization. "A comprehensive and indispensable reference for anyone undertaking the transformational journey towards algorithmic marketing." ―Ali Bouhouch, CTO, Sephora Americas "It is a must-read for both data scientists and marketing officers―even better if they read it together." ―Andrey Sebrant, Director of Strategic Marketing, Yandex "The book gives the executives, middle managers, and data scientists in your organization a set of concrete, actionable, and incremental recommendations on how to build better insights and decisions, starting today, one step at a time." ―Victoria Livschitz, founder and CTO, Grid Dynamics Table of Contents Chapter 1 - Introduction The Subject of Algorithmic Marketing The Definition of Algorithmic Marketing Historical Backgrounds and Context Programmatic Services Who Should Read This Book? Summary Chapter 2 - Review of Predictive Modeling Descriptive, Predictive, and Prescriptive Analytics Economic Optimization Machine Learning Supervised Learning Representation Learning More Specialized Models Summary Chapter 3 - Promotions and Advertisements Environment Business Objectives Targeting Pipeline Response Modeling and Measurement Building Blocks: Targeting and LTV Models Designing and Running Campaigns Resource Allocation Online Advertisements Measuring the Effectiveness Architecture of Targeting Systems Summary Chapter 4 - Search Environment Business Objectives Building Blocks: Matching and Ranking Mixing Relevance Signals Semantic Analysis Search Methods for Merchandising Relevance Tuning Architecture of Merchandising Search Services Summary Chapter 5 - Recommendations Environment Business Objectives Quality Evaluation Overview of Recommendation Methods Content-based Filtering Introduction to Collaborative Filtering Neighborhood-based Collaborative Filtering Model-based Collaborative Filtering Hybrid Methods Contextual Recommendations Non-Personalized Recommendations Multiple Objective Optimization Architecture of Recommender Systems Summary Chapter 6 - Pricing and Assortment Environment The Impact of Pricing Price and Value Price and Demand Basic Price Structures Demand Prediction Price Optimization Resource Allocation Assortment Optimization Architecture of Price Management Systems Summary
ViewData Science for Business - What You Need to Know about Data Mining and Data-Analytic Thinking
Foster Provost, Tom Fawcett
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates
ViewHands-On Machine Learning with Scikit-Learn and TensorFlow - Concepts, Tools, and Techniques to Build Intelligent Systems
Aurélien Géron
Graphics in this book are printed in black and white. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
ViewCustomer Success - How Innovative Companies Are Reducing Churn and Growing Recurring Revenue
Nick Mehta, Dan Steinman, Lincoln Murphy
Your business success is now forever linked to the success of your customers Customer Success is the groundbreaking guide to the exciting new model of customer management. Business relationships are fundamentally changing. In the world B.C. (Before Cloud), companies could focus totally on sales and marketing because customers were often 'stuck' after purchasing. Therefore, all of the 'post-sale' experience was a cost center in most companies. In the world A.B. (After Benioff), with granular per-year, per-month or per-use pricing models, cloud deployments and many competitive options, customers now have the power. As such, B2B vendors must deliver success for their clients to achieve success for their own businesses. Customer success teams are being created in companies to quarterback the customer lifecycle and drive adoption, renewals, up-sell and advocacy. The Customer Success philosophy is invading the boardroom and impacting the way CEOs think about their business. Today, Customer Success is the hottest B2B movement since the advent of the subscription business model, and this book is the one-of-a-kind guide that shows you how to make it work in your company. From the initial planning stages through execution, you'll have expert guidance to help you: Understand the context that led to the start of the Customer Success movement Build a Customer Success strategy proven by the most competitive companies in the world Implement an action plan for structuring the Customer Success organization, tiering your customers, and developing the right cross-functional playbooks Customers want products that help them achieve their own business outcomes. By enabling your customers to realize value in your products, you're protecting recurring revenue and creating a customer for life. Customer Success shows you how to kick start your customer-centric revolution, and make it stick for the long term.
ViewThe Manager's Path - A Guide for Tech Leaders Navigating Growth and Change
Camille Fournier
Managing people is difficult wherever you work. But in the tech industry, where management is also a technical discipline, the learning curve can be brutal--especially when there are few tools, texts, and frameworks to help you. In this practical guide, author Camille Fournier (tech lead turned CTO) takes you through each stage in the journey from engineer to technical manager. From mentoring interns to working with senior staff, you'll get actionable advice for approaching various obstacles in your path. This book is ideal whether you're a new manager, a mentor, or a more experienced leader looking for fresh advice. Pick up this book and learn how to become a better manager and leader in your organization. Begin by exploring what you expect from a manager Understand what it takes to be a good mentor, and a good tech lead Learn how to manage individual members while remaining focused on the entire team Understand how to manage yourself and avoid common pitfalls that challenge many leaders Manage multiple teams and learn how to manage managers Learn how to build and bootstrap a unifying culture in teams
ViewDesigning Machine Learning Systems
Chip Huyen
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
ViewThe Power of Moments - Why Certain Experiences Have Extraordinary Impact
Chip Heath, Dan Heath
The New York Times bestselling authors of Switch and Made to Stick explore why certain brief experiences can jolt us and elevate us and change us—and how we can learn to create such extraordinary moments in our life and work. While human lives are endlessly variable, our most memorable positive moments are dominated by four elements: elevation, insight, pride, and connection. If we embrace these elements, we can conjure more moments that matter. What if a teacher could design a lesson that he knew his students would remember twenty years later? What if a manager knew how to create an experience that would delight customers? What if you had a better sense of how to create memories that matter for your children? This book delves into some fascinating mysteries of experience: Why we tend to remember the best or worst moment of an experience, as well as the last moment, and forget the rest. Why “we feel most comfortable when things are certain, but we feel most alive when they’re not.” And why our most cherished memories are clustered into a brief period during our youth. Readers discover how brief experiences can change lives, such as the experiment in which two strangers meet in a room, and forty-five minutes later, they leave as best friends. (What happens in that time?) Or the tale of the world’s youngest female billionaire, who credits her resilience to something her father asked the family at the dinner table. (What was that simple question?) Many of the defining moments in our lives are the result of accident or luck—but why would we leave our most meaningful, memorable moments to chance when we can create them? The Power of Moments shows us how to be the author of richer experiences.
ViewThe Monk Who Sold his Ferrari
Robin Sharma
An internationally bestselling fable about a spiritual journey, littered with powerful life lessons that teach us how to abandon consumerism in order to embrace destiny, live life to the full and discover joy. This inspiring tale is based on the author's own search for life's true purpose, providing a step-by-step approach to living with greater courage, balance, abundance and joy. It tells the story of Julian Mantle, a lawyer forced to confront the spiritual crisis of his out-of-balance life: following a heart attack, he decides to sell all his beloved possesions and trek to India. On a life-changing odyssey to an ancient culture, he meets Himalayan gurus who offer powerful, wise and practical lessons that teach us to: - Develop joyful thoughts - Follow our life's mission - Cultivate self-discipline and act courageously - Value time as our most important commodity - Nourish our relationships - Live fully, one day at a time
ViewOutliers - The Story of Success
Malcolm Gladwell
From the bestselling author of Blink and The Tipping Point, Malcolm Gladwell's Outliers: The Story of Success overturns conventional wisdom about genius to show us what makes an ordinary person an extreme overachiever. Why do some people achieve so much more than others? Can they lie so far out of the ordinary? In this provocative and inspiring book, Malcolm Gladwell looks at everyone from rock stars to professional athletes, software billionaires to scientific geniuses, to show that the story of success is far more surprising, and far more fascinating, than we could ever have imagined. He reveals that it's as much about where we're from and what we do, as who we are - and that no one, not even a genius, ever makes it alone. Outliers will change the way you think about your own life story, and about what makes us all unique. 'Gladwell is not only a brilliant storyteller; he can see what those stories tell us, the lessons they contain' Guardian 'Malcolm Gladwell is a global phenomenon ... he has a genius for making everything he writes seem like an impossible adventure' Observer 'He is the best kind of writer - the kind who makes you feel like you're a genius, rather than he's a genius' The Times
ViewHow to Lead in Data Science
Jike Chong, Yue Cathy Chang
A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook
ViewBuild a Career in Data Science
Emily Robinson, Jacqueline Nolis
Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder
ViewBuilding Data Science Teams
DJ Patil
As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics include: What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.
ViewAn Introduction to Statistical Learning - with Applications in R
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
ViewThe Algorithmic Leader - How to Be Smart When Machines Are Smarter Than You
Mike Walsh
The greatest threat we face is not robots replacing us, but our reluctance to reinvent ourselves. We live in an age of wonder: cars that drive themselves, devices that anticipate our needs, and robots capable of everything from advanced manufacturing to complex surgery. Automation, algorithms, and AI will transform every facet of daily life, but are we prepared for what that means for the future of work, leadership, and creativity? While many already fear that robots will take their jobs, rapid advancements in machine intelligence raise a far more important question: what is the true potential of human intelligence in the twenty-first century? Futurist and global nomad Mike Walsh has synthesized years of research and interviews with some of the world's top business leaders, AI pioneers and data scientists into a set of 10 principles about what it takes to succeed in the algorithmic age. Across disparate cultures, industries, and timescales, Walsh brings to life the history and future of ideas like probabilistic thinking, machine learning, digital ethics, disruptive innovation, and de-centralized organizations as a foundation for a radically new approach to making decisions, solving problems, and leading people. The Algorithmic Leader offers a hopeful and practical guide for leaders of all types, and organizations of all sizes, to survive and thrive in this era of unprecedented change. By applying Walsh's 10 core principles, readers will be able to design their own journey of personal transformation, harness the power of algorithms, and chart a clear path ahead--for their company, their team, and themselves.
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F.
18.February 2023Sagar helped me giving his impression about me, highlighting my strengths and points that I could improve as well. As a result of this session, I am already putting them into action his advices.
Thanks a lot.