This course focuses on solving real-world Data Science interview questions using Python Pandas. The learning outcomes include mastering Python Pandas, groupby & aggregate DataFrames, regex analysis of text, working with datetime objects in Pandas, filtering data by conditionals, and applying lambda functions to DataFrames. The course includes both coding and non-coding interview questions, gradually increasing in difficulty. The teaching method involves hands-on problem-solving and high-level thinking. The intended audience for this course is individuals preparing for Data Science interviews or looking to enhance their Python Pandas skills.
Overview
Syllabus
- Intro & Video Overview
- Check out this Video’s Sponsor, Brilliant!
- Coding #1 Microsoft, Easy - Finding Updated Records
- Coding #2 Airbnb, Easy - Number of Bathrooms and Bedrooms
- Coding #3 Google, Medium - Counting Instances in Text
- Coding #4 Meta/Facebook, Medium - Customer Revenue in March
- Coding #5 Amazon, Hard - Monthly Percentage Difference
- Coding #6 Microsoft, Hard - Premium vs Freemium
- Non-Coding #1 Visa, Easy - Credit Card Activity
- Non-Coding #2 IBM, Easy - Outliers Detection
- Non-Coding #3 Google, Medium - Probability of Having a Sister
- Non-Coding #4 Uber, Medium - Uber Black Rides
- Non-Coding #5 Capital One, Hard - Terabyte of Data
- Video Conclusion & Recap
Taught by
Keith Galli
