In the realm of Data Science, a significant 70% of interview questions delve into how candidates have applied their skills in real-world scenarios. This focus on practical application over mere tool knowledge is crucial for success in interviews. Data Science interviews often explore candidates’ experiences with tools like SQL, Python, and machine learning frameworks, assessing their ability to implement these in production environments. This trend mirrors the approach seen in Data Engineering, where similar emphasis is placed on real-world problem-solving. As the demand for Data Scientists who can demonstrate tangible results grows, preparing for these types of questions becomes essential. Are you ready to showcase your practical skills in your next Data Science interview?
Gabby
@thenaijacarguy
·
Apr 7 If you’re interested in starting a career in any of the following fields ASAP:– Data Analytics
– Data Science
– Data Engineering
– Cloud Computing
– SalesforceFill out this form and start your journey today: http://datafrik.co/form. A mentor will be assigned to you to hold
Python Programming
@PythonPr
·
5h Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
https://amzn.to/42GtIQ8
#datascience
Darshil | Data Engineer
@parmardarshil07
·
Apr 15 Practice PySpark, DBT(Snowflake), SQL, Python, and Scala to Crack Interviews (Leetcode for Data Engineers)This is just the beginning, we are adding more exciting features!Data Modelling Playground
With that, you will be able to work on your data modeling skills and get
Darshil | Data Engineer
@parmardarshil07
·
Apr 11 Data Engineering Interviews Typically Have 3 to 4 Rounds……Here’s how you can prepare for itRound 1: SQL & Data Manipulation (Foundational Skills)
Round 2: Python/PySpark Implementation (coding questions)
Round 3: Architecting Solutions: Pipeline & System Design
ruwzeta
@RuwZeta
·
Apr 15 DataLemur – Ace the SQL & Data Science Interview https://datalemur.com/profile?referralCode=9sR8a5TL… #DataLemur
Near
@HireWithNear
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Apr 16 Most data analysts say they know Python. Few can use it to solve real problems.If you’re hiring for a role that involves automation, large datasets, or anything beyond dashboards and spreadsheets, surface-level Python isn’t enough.You need someone who can clean messy data,
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