In the ultra-competitive world of data science, standing out isn’t just an advantage—it’s a necessity. But what if you had the secrets to acing every data science interview and leaving an indelible mark on your prospective employer? Dive into Acing the Data Science Interview to transform your career trajectory and ensure you’re not just another resume in the pile. Prepare to embark on a journey that will revamp your job-hunting skills, arm you with insider recruitment techniques, and make interviewers remember your name! Whether you’re a newbie trying to break into the industry or a seasoned pro, this book is the golden ticket you’ve been waiting for.
Software Engineering for Data Scientists presents principles that will improve the performance and efficiency of data science projects.
Build a Robo Advisor with Python (From Scratch): Automate your financial and investment decisions teaches you how to construct a Python-based financial advisor of your very own!
This article delves into how Machine Learning algorithms interact with data and the importance of preserving data privacy.
Read it if you’re a machine learning engineer, or a developer building around machine learning.
If you want to excel in ML and deep learning, you need to know more than how to implement the algorithms—you need to know them inside-out. This book delves into selected algorithms and teaches you how to build your own from scratch.
This article introduces the concept and definition of differential privacy.
Read it if you’re a machine learning engineer, or a developer building around machine learning.
An excerpt from Causal Inference for Data Science by Aleix Ruiz de Villa
This article explains:
· Why and when we need causal inference
· How causal inference works
And how the book approaches the topic.
An excerpt from Julia for Data Analysis by Bogumil Kaminski
This article dives into working with data in dataframes with Julia.
Read it if you’re a data scientist or anyone who works with lots of data, and if you’re interested in the Julia language.
An excerpt from Julia as a Second Language by Erik Engheim
This article covers:
· Storing values on keys in dictionaries.
· Working with pair objects.
· Using tuples to create dictionaries.
· Comparing dictionaries and arrays.
Read it if you’re interested in the Julia language or in how it handles dictionaries.
An excerpt from Julia for Data Analysis by Bogumil Kaminski
Many languages designed for doing data science provide ways to perform vectorized operations, which is also often called broadcasting. In Julia, broadcasting is also supported. In this article you will see how to use it.
Read it if you’re a data scientist or anyone who works with lots of data, and if you’re interested in the Julia language.