In this blog, Devika, my mentee, with 3+ years of experience in data science, is sharing her unique approach to create a data scientist interview prep routine.
Just like a well-constructed algorithm, a well-thought-out plan for interviews helps you succeed.🎯
So, whether you're new to data science or you're experienced, I’m sure you’ll find something of use in this guide!
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Hello, I’m Devika, I've been a data scientist for the past 3 years.
Feeling like it was finally time to upgrade my career, I’m currently in the process of making a job switch to my dream profile.
I’m lucky to have found a mentor in Chetan Mahajan, an Advanced Analytics Specialist at Hewlett Packard Enterprise.
His influence has been extremely rewarding in my entire interview preparation journey.⭐
From helping me create a preparation plan and fine-tune my data science skills to conducting real-time mock interviews, he has supported me through it all!
Here, I’m sharing how I prepared my entire routine for the data scientist interview in 2 months’ time.
By the end you will have a clear idea about how to prepare for data scientist interview questions, what are data science interviews like, and what does a career in data science entail.⭐
9 Steps to Conquer the Data Scientist Interview Prep
This is how I created my interview preparation strategy and routine with the help of my mentor:
1. Understanding the data science interview landscape
It’s important to understand the different interviews in data science: technical, behavioural, and project/experience based rounds.
👉 Technical interviews dive into your coding, algorithmic, and machine-learning skills. They may involve coding challenges, data manipulation tasks, and algorithmic problem-solving.
👉 Behavioural interviews assess your soft skills and how well you fit into the team. They often revolved around teamwork, conflict resolution, and handling project challenges.
👉 Projects/case studies are like real-world problem-solving scenarios. They require analysing datasets and presenting insights.
Knowing this, I understood I needed to prepare for a mix of technical prowess and effective communication. I knew what areas to focus on during my preparation and how to work
2. Tailoring technical skills enhancements
Here are 6 things I did to tailor my upskilling to focus my energy and resources at the right places:
➡️Identifying strengths and weaknesses
First, I took stock of my technical abilities.
I found I had a sound foundation in programming, but I needed to reinforce my understanding of certain machine learning algorithms and data manipulation techniques.
My mentor, Chetan, advised me to be honest about my strengths and weaknesses; this self-awareness was crucial in planning an effective learning path.
➡️Customising learning goals
With my mentor's guidance, I began tailoring my learning goals to the specific requirements of data science interviews.
Instead of trying to cover everything, I focused on a few key areas.
We reviewed job descriptions of the roles I was targeting and identified common technical skills they sought.
This personalised approach helped me allocate my time and energy more efficiently.
➡️Leveraging online resources
To address my weaknesses, I turned to online resources that were both beginner-friendly and comprehensive.
Chetan recommended courses ranging from the basics of machine learning to more advanced topics like deep learning.
These courses provided interactive lessons, practical exercises, and hands-on projects that mirrored real-world scenarios.
⭐I’d especially suggest only taking courses customised to the learning needs that help you target your weaknesses.
There are many courses online, and it's confusing to pick the right ones.
Without my mentor’s guidance, I would not have been able to select the right courses for myself!
➡️Applying theory to practise
One valuable lesson from my mentor was the importance of hands-on practice.
Learning theory was crucial, but the real growth happened when I applied that theory to practical problems.
⭐We found datasets related to my target industry and used Python libraries like pandas and scikit-learn to work on data cleaning, feature engineering, and model building.
This practical experience improved my understanding of algorithms and boosted my confidence.
➡️Diving into projects
As part of my tailored plan, my mentor encouraged me to undertake mini-projects that showcased my skills.
These projects aligned with the work I'd encounter in a data science role.
For instance, I tackled a sentiment analysis project on social media data, demonstrating my ability to process unstructured text data and extract meaningful insights.
📍 Want to upskill strategically with a senior Data Analyst, for getting into your dream role?
Try a free mentoring session with Chetan Mahajan.
3. Creating a structured study plan
These are the 4 methods I used to create a structured study plan that helped me tackle my preparation strategically.
📌Setting a realistic timeline
Creating a realistic timeline was key to avoiding overwhelm.
Chetan and I discussed the time I had available each day after work and other commitments.
We then allocated specific time slots for studying different topics.
For example, I dedicated the first hour after dinner to review fundamental concepts, the next hour to practising coding challenges, and the weekends to in-depth machine learning topics.
📌Tracking progress
To keep myself motivated and ensure I was making progress, I used a simple spreadsheet to track what I studied each day.
I set specific goals for each study session.
For instance, I aimed to complete two modules on descriptive statistics or solve three coding challenges on LeetCode.
📌Regular check-ins with my mentor
Every week, I had a check-in with Chetan to discuss my progress, address any challenges I faced, and adjust the study plan if needed.
This accountability and guidance were incredibly valuable.
⭐Chetan could offer insights on areas I might have overlooked or provide alternative resources if I found something too challenging.
📌Adjusting the plan along the way
Flexibility was key.
Sometimes I found certain topics easier than expected and others that required more time.
My mentor encouraged me to adapt the plan based on my changing understanding and needs.
This allowed me to dive deeper into areas where I needed more practice without feeling rushed.
4. Practising with realistic problems
Solving actual data science challenges gave me the hands-on experience and confidence I needed to tackle interview questions effectively.
Here's how I went about it:
🎯Choosing the right platforms
I started by identifying platforms that offered a variety of data science problems.
Platforms like LeetCode, HackerRank, and Kaggle became my go-to places.
These platforms provided various problem types, from coding challenges to real-world data analysis tasks, which helped me simulate interview scenarios.
🎯Diverse problem types
Chetan helped me diversify my practice by focusing on different problem types.
This included coding challenges that tested my algorithmic skills and more data analysis-oriented tasks like exploratory data analysis and feature engineering.
This approach helped me develop a well-rounded skill set.
🎯Analysing solution approaches
Once I encountered a problem, I would first take my time to understand the problem statement thoroughly.
This step was crucial, as misinterpreting the problem could lead to a completely wrong approach.
I'd then brainstorm and outline potential solutions before coding.
🎯Breaking down problems
I learned the importance of breaking down complex problems into smaller, manageable parts for coding challenges.
This approach made the problems seem less daunting and helped me identify potential corner cases or edge scenarios that I might have missed otherwise.
🎯Learning from others
On platforms like Kaggle, I often explored solutions submitted by others.
This was incredibly insightful as it exposed me to various ways of approaching the same problem.
Chetan taught me to pay attention to the code structure, libraries, and problem-solving strategy.
This exposure expanded my problem-solving toolkit.
📍 Connect personally with top tech mentors to hone your problem-solving skills and ace your interviews.
5. Handling behavioural interviews
Behavioural interviews aim to uncover your soft skills, problem-solving abilities, teamwork, and how you approach challenges.
It's not just about having the right answers but also about demonstrating qualities that align with the company's values.
✔️Identifying key competencies
To prepare, I researched common competencies valued in data science roles.
These included communication, adaptability, teamwork, and critical thinking.
I wanted to have specific examples ready to show each of these qualities.
✔️Building the STAR framework
I learned about the STAR (Situation, Task, Action, Result) framework from Chetan to structure my responses effectively.
This framework helped me provide clear and concise answers while highlighting my accomplishments.
✔️Collecting examples
I took time to recall experiences from my academic, professional, and personal life that showcased the competencies I wanted to emphasise.
For instance, I had a situation where I collaborated on a challenging group project that showcased teamwork and problem-solving.
✔️Tailoring responses
For each competency, Chetan taught me to tailor my examples to match the skills the company was seeking.
If they were looking for analytical thinking, I highlighted a situation where I had to analyse a complex dataset to derive insights.
✔️Practising storytelling
I practised turning these experiences into concise and engaging stories.
I emphasised my role, the actions I took, and the positive outcomes.
I wanted my responses to be easy to follow and memorable.
✔️Researching the company
I understood that companies often ask questions related to their values or specific projects.
So, I thoroughly researched the company's website, blog posts, and recent news to understand their mission, projects, and culture.
⭐My mentor emphasised the importance of practising, but also of staying true to myself and not trying to be someone I'm not.
This advice boosted my confidence and helped me approach interviews with a more authentic mindset.
6. Handling experience/project round
The experience/project round is a chance to showcase your ability to apply theoretical knowledge to actual situations.
Interviewers want to see how you handle challenges, collaborate with teams, and deliver results.
So, I started by selecting projects that aligned well with the job I was applying for.
For example, I chose a project where I analysed customer churn in a subscription-based service. This project showcased my skills in data preprocessing, exploratory data analysis, and predictive modelling.
⚡Preparing project details
To ensure I could clearly explain my projects, I broke them down into specific steps
- Problem statement
- Data collection and exploration
- Feature engineering
- Model selection
- Evaluation metrics
- Results and insights
⚡Showcasing collaboration
I emphasised any collaborative efforts during the projects.
For instance, how I worked with the marketing team to understand the business context and validate the findings from the churn analysis.
⚡Lessons learned and challenges face
In this round, interviewers often inquire about challenges faced and lessons learned.
Chetan helped me think of sharing a situation where my initial model wasn't performing well due to class imbalance.
And explain how I addressed this challenge using techniques like over-sampling and adjusting class weights, improving the model's accuracy.
⚡Impact and business value
To wrap up, highlight the impact of the project.
In the churn analysis example, the insights led to targeted retention strategies, resulting in a measurable reduction in churn rates.
7. Time and energy management strategies
💡Implementing the Pomodoro technique
One of the most valuable techniques my mentor, Chetan, introduced me to was the Pomodoro Technique.
This technique involves working for a focused 25-minute interval followed by a 5-minute break.
After completing four cycles, I took a longer break.
This strategy improved my concentration and prevented mental exhaustion during long study sessions.
📍Read: Effective Time Management: Balance Work and Mentorship
💡Incorporating physical activity
I learned that physical activity directly affected my energy levels and concentration.
I incorporated short bursts of physical exercise during my breaks.
Whether it was a quick walk, stretching, or simple exercises, these activities rejuvenated my mind and prevented the monotony of long study hours.
💡Balancing preparation and relaxation
My mentor emphasised the importance of balancing preparation and relaxation.
Overworking could lead to burnout, so I scheduled leisure activities that I enjoyed.
This could be watching a movie, spending time with friends, or engaging in a hobby.
These breaks allowed me to return to studying with renewed vigour.
💡Reflection and adaptation
Throughout the preparation process, I regularly reflected on my progress and the effectiveness of my strategies.
If certain techniques didn't yield the expected results, Chetan and I quickly adapted and tried fresh approaches.
This flexibility ensured that I optimised my study routine for maximum efficiency.
8. Staying motivated and dealing with setbacks
Who doesn’t face setbacks in their preparation journey?
However, the next time you question your abilities because of it, you can implement these mindset shifts.
They’ve helped me tremendously.
🔹Embracing a growth mindset
One of my mentor's most valuable lessons was the importance of a growth mindset.
Instead of viewing setbacks as failures, I learned to see them as opportunities for growth.
This shift in perspective allowed me to approach challenges with a positive attitude and a determination to learn from my mistakes.
🔹Celebrating small wins
My mentor encouraged me to celebrate even the smallest wins along the way.
Whether it was solving a tricky coding problem or successfully answering a behavioural interview question.
Counting these achievements boosted my confidence and kept me motivated to keep progressing.
🔹Creating a supportive routine
With Chetan's guidance, I developed a productive daily routine that supported my overall well-being.
This routine included dedicated study time, exercise, relaxation, and time for pursuing hobbies.
Having a well-rounded routine prevented burnout and helped me maintain a positive mindset.
🔹Learning from setbacks
Instead of allowing setbacks to discourage me, my mentor taught me to analyse them objectively.
When I struggled with a certain concept or performed poorly in a mock interview, my mentor helped me break down what went wrong and how I could improve.
This approach transformed setbacks into valuable learning opportunities.
9. Mock Interviews and getting feedback
I scheduled multiple mock interview sessions with Chetan over a span of weeks.
These were like rehearsals before I had to face the real deal.
The feedback I received during these sessions gave me the last-minute insights I needed to fine-tune my approach.
For instance, I tended to rush through my explanations.
Also, I sometimes got stuck on a problem for too long.
To address these issues, I started practising more deliberately.
Each time, I could see my improvement.
⭐I was more confident in tackling various types of questions and felt comfortable in articulating my thought process.
When the actual interviews arrived, I felt far more prepared and at ease!
The mock interview practice had not only improved my technical skills but also boosted my self-assurance.
So, I highly suggest conducting mock interviews with an experienced professional in your field.
The results are immensely helpful!
👉 Want to practise your mock interviews with an industry expertto fool-proof your preparation?
Try a free mentor-led prep session.
Wrapping Up
As you navigate my data science interview preparation journey, a well-rounded approach is key.
Tailor your preparation routine to your strengths and the specific requirements of the roles you're targeting.
Through continuous learning, dedicated practice, and effective time management, you can stride into your interviews confidently.
And if you feel you need a little personalised help in your career journey, reach out to a career mentor.
Their strategic and customised approach will help you achieve your goals 10x faster and wiser.
Good luck with your interviews!
Book a free 1:1 career mentorship session with an experienced data science professional.
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Note: mentee’s name has been changed for confidentiality reasons.
Also read:
Meet the Top 5 Data Science Mentors at Preplaced
Deepesh’s Transition From Software Engineering to Data Science
