DataLemur vs Interview Query for Data Science

In the dynamic and ever-growing field of tech, companies often grapple with hiring the ‘best fit’ out of a large pool of candidates. The interview process has evolved over the years to filter out unqualified applicants through multiple rounds of technical and behavioral interviews.

For aspiring data professionals, consistently putting your best foot forward during the interview process is pivotal to securing a job in such a competitive industry. In the past few years, different companies and industry experts have created numerous interview guides and services to assist those breaking into the field.

In this article, we’ll compare the services and resources offered by Interview Query vs. DataLemur, two data interview preparation platforms.

Interview Query provides a curated range of data science interview preparation services, including individualized coaching, an extensive content blog, and, of course, interview questions.

We focus on the practical: real questions asked by a wide range of companies, role-specific interview guides, and specialized learning paths for topics like SQL, Machine Learning, Product Metrics, etc.

Data Lemur is a learning platform that offers practice questions for SQL, Statistics, Machine Learning, and other data science topics. Inspired by creator Nick Singh’s “Ace the Data Science Interview” guide, DataLemur is structured like a question brochure, with around 180 data-related questions and guides.

To provide a complete and objective review of both products, we’ll compare the two platforms based on a range of categories, with the following score ranges:

Assessment Criteria

• UI/UX (10 points): User experience is significantly affected by a platform’s design, which ideally should be modern, clean, and appealing. Navigation plays a pivotal role, as well as the platform’s responsiveness.
• Content (20 points): Here, we’ll assess how relevant each platform’s content is to current industry trends and needs. In addition to measuring the range of topics and formats, we’re interested in the depth of content available. We’ve weighted this category more as it arguably plays a large part in determining a platform’s ultimate value.
• Features (10 points): Unique features can set a platform apart from its competitors, offering users something distinct.
• Pricing (10 points): A platform’s value proposition is often reflected in its pricing. We’ll evaluate whether costs align with the features and content provided. Flexible and transparent pricing plans cater to a broader audience and provide clarity for potential use

DataLemur has an extended, one-page-esque design language with simple navigation options. As a product primarily focused on interview questions, it’s easy enough to navigate between different topics to find what you need.

On the other hand, Interview Query’s content and features are produced in various forms, including community-based leaderboards, discussions, and blogs. The platform’s design, therefore, inherently requires a greater degree of complexity, though we’ve tried to retain a more minimalistic design despite having a maximalist content philosophy.

Initial Verdict:

In terms of content volume and diversity, Interview Query has a distinct advantage. At the time of this review, DataLemur provides 180 interview questions spanning three key categories: SQL, Statistics, and Machine Learning.

Conversely, Interview Query offers 648 comprehensive questions, with new additions weekly based on current relevance and frequency for specific companies. Beyond our question bank, we’ve diversified our platform with courses, learning paths, takehomes, interview guides, and valuable salary information.

Both platforms have a linked blog with articles available to users. DataLemur has a collection of six articles targeting SQL-specific topics, while Interview Query numbers over 150 blog posts covering SQL and machine learning techniques, company-specific interview question guides across a range of positions and skills, and other topics.

Interview Query also has in-depth guides for SQL, Python, R, and Statistics, all important foundations for any well-rounded data professional. The platform is designed not only for data scientists but also for data engineers, analysts, machine learning engineers, quants, and more.

Given this content breakdown, here’s how we tallied the scores for this round:

Interview Query appears to offer a broader range of features for community engagement on its platform, including discussion boards for users to share their interview experiences and salaries. The leaderboard feature offers a fun way to engage competitively with other users on the platform.

Let’s delve further into the specific features of both platforms:

Data Lemur

• SQL, Machine Learning, and Statistics Questions
• A free interview crash course (depending on your subscription)
• Blog guides

Interview Query

• 600+ Interview Questions: Stay updated with industry-sourced questions updated weekly, spanning 11 different job roles across five domains.
• Takehomes: Dive into over 60 comprehensive takehome assignments complete with datasets, evaluations, and a guided approach to tackling them.
• Challenges: Compete with fellow Interview Query members in multiple-choice challenges. Topics range from Machine Learning to Product.
• Coaching: Practice mock interviews and learn more from personalized 1:1 coaching sessions from seasoned data professionals.
• Learning Paths and Courses: Work through specialized learning paths and course materials, which encompass all of the essential skills for any data role.
• Community Discussion Board: Engage with other users to discuss salaries, job openings, and interview experiences.
• Blogs: Learn niche skills, pick up job guides, and read interesting content pieces that will get you in the know!
• Interview Guides and Other Resources: Browse through tailored interview guides with specific questions and processes for a range of top companies based on real data we’ve collected.
• Mock Interviews: Use peer-to-peer mock interviews to experience the real thing before the real thing. Practice makes perfect.
• Job Boards: Looking for your next job? Explore the job board and see job opportunities around the world.

From the features presented, Interview Query offers a more extensive feature set in this comparison. Based on these features, here’s how the two products stack up in our evaluation at this point:

Pricing is often the key factor for most people when deciding to subscribe to a particular product. Here, we broke down each of the available plans for both platforms.

Interview Query

DataLemur

Comparing the two platforms here is a little more complex than the other categories we’ve assessed thus far. Interview Query provides a more diversified pricing model catering to different user needs for specific features, from basic interview prep to comprehensive coaching. DataLemur’s plans are more based on accessibility to interview questions and specific course content.

Given the depth and breadth of content, features, and the pricing tiers available, our assessment leans towards Interview Query, offering more value, especially for those looking for comprehensive preparation. However, DataLemur may be an alternative for those who don’t need all the other features Interview Query provides.

Final Verdict:

To wrap up this review, let’s break down some of the strengths and weaknesses of Interview Query vs. Data Lemur to help you make an informed decision for your interview preparation.

Interview Query

Pros:

1. Extensive Content: With 648 questions and regular updates, users have a wide range of materials to prepare from. This includes more than just interview questions, with guides, blogs, course material, etc. catering to more than 13 job positions.
2. Diverse Features: The platform offers comprehensive features to cater to various user needs, from discussion boards to peer interviews.
3. Tiered Pricing: Multiple pricing options, including a free, monthly, and yearly tier, allow users to subscribe based on their needs and budget.
4. Community Engagement: The platform’s emphasis on community through discussions, leaderboards, and blogs fosters a sense of belonging and engagement.
5. Comprehensive Preparation: With courses, learning paths, take-homes, and interview guides, users prepare more holistically for their interviews.

Cons:

1. Potentially Overwhelming: The sheer volume of content and features might overwhelm some users.

DataLemur

Pros:

1. Simplistic Design: The platform’s one-page design ensures quick and easy navigation.
2. Focused Content: Given the platform’s focus on interview questions, users get direct and specific preparation material.

Cons:

1. Limited Features: Lacks community features like discussion boards and leaderboards.
2. Static Content: DataLemur’s content appears to be more static, with no indication of regular updates based on industry relevance.
3. Missing insights for the overall interview process: Technical interviews are just one part of what an applicant must go through when pursuing a data role. Company-specific interview guides and materials can be crucial to provide comprehensive guidance for candidates.
4. Not ideal for upskilling or career changers: DataLemur may be less accommodating for those newer to data science, as much of the material assumes a pre-existing level of knowledge.
5. No curation mechanisms: The platform lacks robust curation mechanisms or programs for personalized content, such as coaching and recommendation algorithms.

In conclusion, DataLemur offers a more streamlined and focused approach, while Interview Query is designed to be a comprehensive platform that caters to a broader range of user needs.

Overall, the best way to gauge a platform’s effectiveness for you is to experience it firsthand. If you’re still on the fence, try out Interview Query’s free tier. It’s a risk-free way to experience the platform’s offerings and decide if it aligns with your preparation goals. Jumpstart your data goals with Interview Query now!