Big Data: Three Different Ways to Handle Data That Can’t Fit into Memory (Part II)
Today we would like to cover the second way of handling data that can’t fit into memory — down casting […]
Big Data: Three Different Ways to Handle Data That Can’t Fit into Memory (Part I)
Often times, data professionals need to deal with data sizes that are larger than their local machine’s memory. How to […]
Python: Three Hidden Ways to Create Potential Bugs with Mutable Data Structures (Part III)
We were told tuples are immutable data structures when we first learned Python, but this is not absolute. If you […]
DataYap Virtual Conference Highlights
The recent DataYap Virtual Conference brought together tech experts to delve into the latest trends in data science. From revolutionizing business techniques with data analytics to addressing global challenges like the COVID-19 pandemic, this conference showcased the transformative power of data science. Discover how data analytics is reshaping industries and fostering diversity and inclusion in technology.
Python: Three Hidden Ways to Create Potential Bugs with Mutable Data Structures (Part II)
In this continuation of our three-part series, we delve into the risks associated with using mutable data structures as default parameters in Python functions. Gain insights into which data structures are mutable and immutable in Python and explore practical examples to ensure your code remains bug-free
Python: Three Hidden Ways to Create Potential Bugs with Mutable Data Structures (Part I)
In this edition of Data Hack Tuesday, we explore the common pitfalls associated with mutable data structures in Python. Dive into the concepts of reference, shallow copy, and deep copy, and how they impact your code when dealing with both immutable and mutable data.
Pandas: Detect Mixed Data Types and Fix it
In this week’s Data Hack Tuesday, we tackle the common challenge of mixed data types within columns in data tables. Explore three indispensable techniques for cleaning such data: inferring data types accurately, converting strings of numbers, and selecting rows by data types. Ensure your data is clean and ready for analysis.
Data Simplified – Interview with Troy Hernandez
In this episode of Data Simplified, we sit down with Dr. Troy Hernandez, a distinguished expert in the field with a PhD in Statistics and a Solution Engineer at IBM. Explore the exciting realms of GitOps and Jamstack and gain valuable insights into modern development practices and data management. Tune in for an enlightening conversation that’s sure to simplify your understanding of these cutting-edge technologies
Python: When to Use Set vs. List
In Python, the choice between using lists and sets as data structures often depends on the specific task at hand. Lists are ideal for looping operations, while sets shine when you need to quickly determine if an item exists within a data structure. Dive into this article to understand the performance trade-offs and decide which data structure suits your needs
Data Simplified: Interview with Mark Schwartz
In this thought-provoking episode, we sit down with Mark Schwartz, the Enterprise Strategist for AWS and author of ‘The (Delicate) Art of Bureaucracy.’ Mark shares unique perspectives on how every technologist can learn valuable lessons from monkeys and sumo wrestlers, metaphorically, to harness the power of bureaucracy in the digital age. Don’t miss this fascinating conversation that will reshape your understanding of IT and bureaucracy