Book Image

Cracking the Data Science Interview

By : Leondra R. Gonzalez, Aaren Stubberfield
Book Image

Cracking the Data Science Interview

By: Leondra R. Gonzalez, Aaren Stubberfield

Overview of this book

The data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.
Table of Contents (21 chapters)
Free Chapter
1
Part 1: Breaking into the Data Science Field
4
Part 2: Manipulating and Managing Data
10
Part 3: Exploring Artificial Intelligence
16
Part 4: Getting the Job

Working with imbalanced data

In this section, we will explore the challenges posed by imbalanced datasets in machine learning and various methods to effectively address this issue. Imbalanced data refers to datasets where one class (the minority class) is significantly underrepresented compared to another class (the majority class). The class imbalance can lead to biased and suboptimal model performance, as models tend to favor the majority class, making accurate predictions for the minority class challenging. We will delve into the consequences of imbalanced data and several techniques to handle imbalanced datasets for improved model performance.

Understanding imbalanced data

Since models prioritize the majority class, there are serious consequences of imbalanced data on model training and evaluation.

In the context of imbalanced datasets in machine learning, the majority class refers to the class that has a significantly larger number of instances or observations compared...