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.
From Causal Inference in Data Science by Aleix Ruiz de Villa
Causal inference models predict why something will happen, i.e. causal effects, rather than the outcomes themselves. This is useful in many instances and is a budding field in machine learning and data science.
Read on to see how it works and what you will learn from this book.
From Pandas Workout by Reuven Lerner
This article discusses using multi indexes in Pandas.
From Pandas Workout by Reuven Lerner
This article discusses using Data Frames in Pandas.
An excerpt from Julia for Data Analysis by Bogumil Kaminski
This article shows you how to use Multiple Dispatch in 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.
From Data Analysis with Python and PySpark by Jonathan Rioux
This chapter covers using transformer and estimators to prepare data into ML features.
An excerpt from Julia as a Second Language by Erik Engheim
This article covers:
What type of problems Julia solves.
The limits of statically-typed languages.
Why the world needs a fast dynamically-typed language.
How Julia increases programmer productivity.
Read it if you’re interested in the Julia language and its strengths and weaknesses.
From Data Science Bookcamp by Leonard Apeltsin
This 3-part article series covers:
Clustering data by centrality
Clustering data by density
Trade-offs between clustering algorithms
Executing clustering using the scikit-learn library
Iterating over clusters using Pandas