data science

Data Analysis: From Theory to Practical Expertise

Data analysis is a dynamic field with endless opportunities, but for many newcomers, the transition from theory to real-world practice can be daunting. While formal education and technical training lay a strong foundation, practical experience is the key to becoming a proficient data analyst.

In this article, we’ll explore how to bridge the gap between theoretical knowledge and hands-on expertise based on lessons learned in depth within our latest guide on the topic, Solve Any Data Analysis Problem, by David Asboth.

Elevate Your Edge: Secrets to Dominating Data Science Interviews

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.

Becoming a Better Data Scientist by Learning Software Engineering Principles

Software Engineering for Data Scientists presents principles that will improve the performance and efficiency of data science projects.

Designing and Building a Robo Advisor with Python

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!

The Threat of Learning Beyond the Intended Purpose

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.

Going Inside Machine Learning and Deep Learning Algorithms

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.

Differential Privacy in Action

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.

What is Causal Inference and How Does It Work?

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.

Working with Dataframes in Julia

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.

Julia Crash Course: Dictionaries

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.

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