How I built my 3-month routine for Amazon Data Science interview prep?

If you’re here, chances are you are preparing for a Data Science role at Amazon.

Well let me tell you that you are at the right place as in this article you’ll get an in-depth preparation routine to crack Amazon Data Scientist interviews.

Recently I connected with one of our mentees Pratik who is preparing for a Data Science role at Amazon with mentor Snehansu Sekhar Sahu, an Applied Scientist II at Amazon.

Snehansu has tailored the three-month data science preparation journey for Pratik. Hence, you can be sure that it is tried and tested.

Now I do believe that everyone’s learning curve is unique so feel free to modify it according to your requirements.

And if you have any doubts, please feel free to connect with our Data Science Mentors on a free strategy call.

Now, let’s hear it from Pratik himself.

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I'm Pratik, and I'm on a journey to transition into a Data Scientist role at Amazon. 

I currently have about two years of experience in the field, but joining Amazon has always been a dream of mine.

I did try self-preparation for a while but managing both my job and preparation became hectic. 

Hence I decided to get some help and connected with Preplaced mentor Sehansu who is an Applied Scientist II at Amazon and has a total work experience of 11+ years.

Below is the Amazon Data Science interview preparation routine for 3 months.

Let’s dive right in.

Month 1: Understanding the  Interview Process and Fundamentals

🎯 Week 1: Understanding Amazon's Interview Process

In the first week of my preparation, I wanted to get the lowdown on how Amazon's interviews work. 

It's a unique process, and I knew I needed to be in the know. 

My mentor shared their experiences, the types of questions asked, and what Amazon looks for in candidates. 

I learned insider insights that you won't find on just any online forum. 

This set the stage for the rest of my preparation.

🎯 Week 2: Technical Fundamentals Review

Moving on to Week 2, I decided to get back to basics. 

I revisited the fundamental concepts of data science. 

Think of it like a refresher course. I brushed up on things like statistics, probability, and data manipulation. 

I didn't want to leave any gaps in my knowledge. 

I dug into topics like hypothesis testing, which often pops up in technical interviews. 

🎯 Week 3-4: Putting into practice

In Week 3, I moved from reviewing theory to applying what I had learned by working on real data. 

I started by finding a dataset that interested me. 

For example, I found a dataset about e-commerce sales, which I thought was fascinating. 

Then, I used Python and data visualisation libraries like Matplotlib and Seaborn to explore this data.

I created various charts and graphs to better understand the sales trends. 

For instance, I made bar charts to visualise sales by category and line graphs to track sales over time. 

This practical work not only reinforced my understanding of data manipulation but also improved my data storytelling skills.

I also worked on a small data analysis project during this week. 

I decided to analyse customer behaviour by looking at factors like purchase history, browsing patterns, and demographics. 

I used Python to clean and preprocess the data and then applied statistical techniques to draw insights.

By the end of Week 3, I felt more confident in my ability to handle real-world data and draw meaningful conclusions from it. 

This hands-on experience was invaluable for my preparation, as it helped me apply the technical fundamentals I had reviewed earlier.

Month 2: Advanced Topics and Practical Application

🎯 Week 5-6: Deep Dive into Machine Learning

I rolled up my sleeves and dug into advanced machine learning. 

I wanted to understand things like ensemble methods (where you combine multiple models to make better predictions), deep learning (like neural networks), and recommendation systems (think Netflix suggests shows you might like).

After that, I built my own recommendation system. 

You know how Amazon suggests products you might want to buy? Well, I created something similar using real data and algorithms.

These projects helped me get comfortable with using machine learning libraries and working with real data. 

While working on practical projects, I occasionally sought input from my mentor. 

🎯 Week 7-8: Data Engineering and Big Data

During these two weeks, I focused on strengthening my skills in data engineering and big data technologies. 

It was crucial for handling large datasets effectively.

I refreshed my understanding of ETL (Extract, Transform, Load) processes. 

This involves getting data from different sources, transforming it into a usable format, and storing it efficiently. 

Next, I explored big data technologies like Hadoop and Spark. 

These are essential for processing and analysing vast amounts of data efficiently.

As a practical exercise, I set up a small Spark cluster. This allowed me to work with large datasets and apply data processing techniques.

By the end of these two weeks, I felt more confident in handling big data and processing it efficiently for data science tasks.

Month 3: Specialisation and Interview Preparation

🎯 Week 9-10: Amazon-specific Preparation

In these two weeks, I dedicated my time to preparing for Amazon's Data Science interviews.

I began by researching common technical questions asked in Amazon interviews. 

Here are a few examples: 

• "Can you explain the concept of p-value in hypothesis testing?"
• "How would you handle missing data in a large dataset?"
• "Describe a machine learning algorithm you've used and its applications."
• "Walk me through the process of building a recommendation system."

To practice, I conducted mock interviews with Sehanshu, simulating the interview experience. 

This allowed me to tailor my interview responses to highlight my skills and experiences that aligned perfectly with Amazon's requirements.

🎯 Week 11-12: Behavioral and Soft Skills

In these two weeks, I focused on improving my non-technical skills to ace the interview.

📌 Behavioural Interview Questions: 

I practised answering behavioural questions using the STAR method (Situation, Task, Action, Result). For example, I prepared a story about a challenging project I handled.

📌 Leadership Principles: 

I aligned my responses with Amazon's Leadership Principles. 

I made sure to include examples from my past experiences that showcased these principles, such as "Customer Obsession" and "Invent and Simplify."

📌 Problem-Solving Scenarios: 

I created scenarios where I faced complex problems and practised explaining how I solved them. This helped me demonstrate my analytical thinking and adaptability.

There was an established feedback loop with my mentor. This allowed me to gain valuable insights and fine-tune my responses.

🎯 Week 13: Final Review and Practice

In this final week of my preparation, my mentor and I sat down to review all the key topics and interview questions I had covered in the past three months. 

We went through everything from the basics to the advanced concepts.

📌 Stress Management: 

My mentor also shared tips on managing stress during interviews. They reminded me to stay calm, take a moment to think before answering and maintain a positive attitude.

📌 Time Management: 

We discussed strategies for managing my time effectively during the interview. This included allocating a specific amount of time to each question and not getting stuck on a single problem for too long.

📌 Questions for the Interviewer: 

We also prepared thoughtful questions to ask the interviewer. This showed my genuine interest in the role and the company.

We also worked on my resume and portfolio. 

We went through them carefully, making sure they were in great shape. 

My mentor gave me some valuable tips on how to highlight my relevant experience and the projects that really showed off my data science skills. 

This step was super important because my resume and portfolio are what Amazon recruiters will see first, so they need to be perfect.

Wrapping it Up

The Amazon Data Science interview preparation journey was both challenging and rewarding.

My hope in sharing my three-month data science preparation routine is to shed light on the real-world experience of transitioning to a role like a Data Scientist at Amazon.

Whether you're looking to join Amazon or excel in the field of data science, keep learning, stay curious, and don't hesitate to seek mentorship.

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In closing, we couldn't be happier with Pratik's progress over these three months. 

His dedication and hard work have paid off, and I have no doubt that he's well on his way to a successful career in data science at Amazon.

If you also find yourselves on a similar journey or have questions, don't hesitate to reach out to mentors and book a free 1:1 trial session. 

Preplaced mentors are here to help, offer guidance, and share insights to make your path smoother. 

Whether you're facing challenges or simply seeking advice, remember that you're not alone in this pursuit.

Best of luck, and keep pushing towards your goals!

📍 Note: Mentee’s name has been changed for confidentiality purposes.

Recommended Readings:

Samrudha's Story: Path to Becoming an Amazon Data Scientist

Data Engineer Interview Experience - Walmart