Suman's Journey to Walmart: A Data Scientist's Success Story

Suman's Journey to Walmart: A Data Scientist's Success Story

Table of Contents

1. Introduction
2. Suman's Background
3. Enrolling for Applied AI Course
4. Suman's Interview Experiences
   • Walmart Experience
   • E-commerce Company Interview
   • Recommendation Engine Company Interview
5. Suman's Learning Strategy
6. Ups and Downs in the Learning Process
7. Suggestions for Data Analysts and Data Scientists
8. Importance of Understanding the Production Side
9. Importance of SQL Skills
10. The Value of Applied AI Course

Article

Introduction

In this article, we will discuss Suman's Journey as a Data Scientist and his successful transition to a product-Based company. We will explore his experiences and learnings throughout the process, as well as his recommendations for fellow professionals looking to make a similar career move. Suman's story is an inspiration for those who want to level up their skills and excel in the field of Data Science.

Suman's Background

Suman, one of the applied AI Core students, recently joined Walmart as a Data Scientist. His Current role involves automating the allocation of items to store shelves in thousands of Walmart stores worldwide. Before joining Walmart, Suman worked as a Data Scientist and Senior Data Analyst at Tiger Analytics for nearly two years. Prior to that, he gained valuable experience working as an Analyst at Infosys for four years.

Enrolling for Applied AI Course

Even though Suman was already a Data Scientist at Tiger Analytics, he felt the need to expand his knowledge and confidence in various machine learning concepts. He wanted to have a structured learning approach and a deeper understanding of classical machine learning concepts before exploring deep learning. Enrolling in the Applied AI course provided Suman with the foundation and platform he needed to fill the gaps in his knowledge and gain the confidence to Apply for top product-based companies.

Suman's Interview Experiences

Suman interviewed with multiple companies, including Walmart, an e-commerce company, and a large-Scale recommendation engine company. Each company had its unique interview process and focus areas.

• Walmart Experience: At Walmart, Suman's interview focused primarily on his project experience. The interviewers were interested in understanding how he approached and solved problems in his previous projects. They delved into the entire end-to-end life cycle of his projects, from data understanding and gathering to model selection, deployment, monitoring, and model governance.

• E-commerce Company Interview: The e-commerce company had specific rounds dedicated to testing programming skills, SQL knowledge, basic probability and statistics, and machine learning concepts. They wanted to assess Suman's foundational knowledge in these areas and ensure he had a solid understanding before moving forward in the interview process.

• Recommendation Engine Company Interview: The recommendation engine company, with its focus on building large-scale recommendation systems, conducted intensive interview rounds specializing in recommendation systems. They assessed Suman's approaches to defining recommendation problems, his knowledge of collaborative filtering, content-based filtering, and even state-of-the-art deep learning techniques for recommendation systems.

Suman's success in cracking these interviews can be attributed to the strong foundation he built through the Applied AI course. The course materials provided valuable content and deep understanding of the concepts required to clear the rounds.

Suman's Learning Strategy

Suman began his learning journey in March 2021. With his prior knowledge in mind, he devised a strategy to spend at least one hour every day studying the course materials. Being a morning person, he dedicated this time to Delve into the classical machine learning concepts covered in the course. Suman also took extensive notes and made sure to revise them regularly, especially during weekends. This allowed him to grasp the concepts effectively and solidify his understanding.

Ups and Downs in the Learning Process

Despite having some prior knowledge, Suman faced challenges and encountered topics that required multiple iterations of study to fully comprehend. For example, Support Vector Machines (SVM) initially posed difficulties, requiring additional online research and reading through references like Wikipedia to grasp its concepts fully. However, with determination and persistence, Suman managed to overcome these obstacles and strengthen his understanding.

Suggestions for Data Analysts and Data Scientists

Suman offers valuable suggestions to data analysts and data scientists aiming to transition to product-based companies:

1. Understand the Production Side: A crucial aspect often emphasized during interviews is the ability to deploy models and work on the production side. Familiarize yourself with the challenges and requirements of deploying models in real-world scenarios. Understanding model monitoring, governance, and production aspects set candidates apart during interviews.

2. Master SQL Skills: SQL proficiency is a must-have for data science roles. Companies, both product-based and service-based, will often rely on SQL for data retrieval, manipulation, and analysis. Focus on honing your SQL skills, including writing complex nested queries. This skill adds immense value to your profile.

3. Specialize and Broaden Your Knowledge: After completing foundational courses like Applied AI, identify your area of interest within data science and specialize in it. For instance, if time series analysis appeals to you, focus on enhancing your understanding and skills in that specific domain. However, maintain a broad knowledge of other areas to have a well-rounded profile.

4. Continuous Learning and Upskilling: Data science offers immense opportunities for growth and innovation. Stay updated with the latest trends, techniques, and tools in the field. Continuous learning and upskilling are essential to excel in this rapidly evolving field. Consider enrolling in advanced courses or attending industry conferences to stay at the cutting edge.

The Value of Applied AI Course

Suman credits a significant portion of his success to the Applied AI course. The course provided a well-structured learning pathway, ensuring a comprehensive understanding of various concepts within data science. By completing the course, Suman gained Clarity on the areas he needed to focus on and was equipped with the knowledge and skills necessary to tackle interviews with confidence.

In conclusion, Suman's journey showcases the importance of a solid learning strategy, perseverance, and continuous upskilling. With the right skills, knowledge, and mindset, transitioning to a product-based company is an achievable goal for data analysts and data scientists.