Nervous about your Data Science Interview? Got rejected and don’t know why? We’re here to help!

In this video, I interview an aspiring Data Scientist named Ashley - an upper year Statistics and Computer Science student from the University of Waterloo in Ontario, Canada - for an Advanced Analytics Internship Position (COOP). In real-time I give my true thoughts on her answers. I ask her real Data Science Interview Questions that YOU will be asked, and upon watching this you’ll know why you got rejected for the job.

This will significantly help you prepare for your Data Science job interview by giving the questions and answers that are commonly asked to Data Scientists, Data Engineers, Machine Learning (ML) Engineers, and Deep Learning Engineers.

We give questions and answers to Python programming in Pandas, Numpy, Matplotlib, Spark / PySpark and Big Data / Analytics, SQL and Relational Databases, Distributed / Parallel Computation, Object Oriented Programming (OOP) and Classes / Interfaces.

We also discuss Probability and Statistics, Data Cleaning / Data Wrangling, and the entire Machine Learning Lifecycle including Data Visualization and Graphing, Model Training, Image Classification, Feed Forward Neural Networks (or MLPs) vs Convolutional Neural Networks (CNNs), the difference between Overfitting and Underfitting, scaling / preparing your data, why you need a Training, Test and Validation set, and many other topics.

After watching, you will be much more likely to pass your job interview in Data Science, and you’ll know why your application was rejected.

Be sure to watch until the end where I give Ashley an Overall Evaluation and summarize the main points!

Timeline:

• 0:00 Introduction
• 0:51 Gear Your Summary Towards the Position
• 1:31 Introduction to Spark and Big Data
• 2:50 Probability - Markov Chains and Stochastic Processes
• 3:39 Make your Employer Feel Powerful
• 4:26 Be Inclusive of Everyone in the Room
• 5:05 Answer their Question - THEN Pivot to What you Want to Discuss
• 6:59 Image Classification - Convolutional Neural Networks (CNNs)
• 10:50 Model Training - Overfitting vs Underfitting
• 11:54 Pandas vs Numpy, Data Cleaning / Analytics
• 14:30 SQL and Relational Databases
• 16:59 Spark and RDDs
• 23:15 Python
• 25:01 Classes and Object Oriented Programming
• 26:44 Binary Classification and Logistic Regression
• 31:03 Why you Need Training, Validation, and Test sets
• 34:55 What to do First When you see a Dataset
• 36:26 Overall Evaluation

#data-science #interview-questions