Effective Pandas

Seven-time bestselling author Matt Harrison reveals his top tips and tricks to enable you to get the most out of Pandas, the leading Python data analysis library. Enjoy!

Matt's books, all of which have been Amazon best-sellers, are:
1. Effective Pandas
2. Illustrated Guide to Learning Python 3
3. Intermediate Python
4. Learning the Pandas Library
5. Effective PyCharm
6. Machine Learning Pocket Reference
7. Pandas Cookbook (now in its second edition)

Beyond being a prolific author, Matt:
• Teaches "Exploratory Data Analysis with Python" at Stanford
• Has taught Python at big organizations like Netflix and NASA
• Has worked as a CTO and Senior Software Engineer
• Holds a degree in Computer Science from Stanford University

On top of Matt's tips for effective Pandas programming, we cover:
• How to squeeze more data into Pandas on a given machine.
• His recommended software libraries for working with tabular data once you have too many data to fit on a single machine.
• How having a computer science education and having worked as a software engineer has been helpful in his data science career.

This episode will appeal primarily to practicing data scientists who are keen to learn about Pandas or keen to become an even deeper expert on Pandas by learning from a world-leading educator on the library.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Data Science at the Command Line

This week's guest is Dr. Jeroen Janssens, a global expert and bestselling author on effectively leveraging the Unix command line as a data scientist.

Jeroen:

• Wrote the popular book "Data Science at the Command Line", the second edition of which was published by O'Reilly Media in October

• Is the Founder Data Science Workshops B.V., which provides hands-on workshops to global orgs such as Amazon and The New York Times

• Is Organizer of the Data Science Netherlands Meetup (3000+ members)

• Former Assistant Professor at the Jheronimus Academy of Data Science

• Has worked as a data scientist for Elsevier, YPlan, and Outbrain

• Holds a PhD in A.I. from Tilburg University

In today’s episode, Jeroen details:

• Why being able to do data science at the command-line — for example, in a Bash terminal — is an invaluable skill for a data scientist to have

• How mastering the command line is the glue that facilitates “polyglot data science”, the ability to seamlessly borrow functions from any programming language in a single workflow

• His PhD research on detecting anomalous events in time-series data

• Why LaTeX is a great typesetting language to consider using particularly for creating lengthy documents or technical figures that adapt automatically to new data

• How his consulting company, Data Science Workshops, grew organically out of his success as an author

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Exercises on Limits

Final YouTube video from my thematic segment on Limits out today! It's a handful of comprehension exercises. Starting Thursday, we'll begin releasing videos from a new Calculus segment, on derivatives and differentiation.

We release new videos from my "Calculus for Machine Learning" course on YouTube every Monday and Thursday. The playlist is here.

Calculating Limits

Today's video introduces Limits, a key stepping stone toward understanding Differential Calculus. This one has lots of interactive Python code demos and paper-and-pencil exercises to ensure learning the subject is both engaging and fun.

We release new videos from my "Calculus for Machine Learning" course on YouTube every Monday and Thursday. The playlist is here.

New Series: Calculus for Machine Learning! Plus a New Video Schedule

With all of the Linear Algebra of my Machine Learning Foundations series already available on YouTube, it's time for a new subject: Calculus! The first Calculus video — an intro to the subject — is live today!

Up until now, I've been releasing my YouTube videos in thematic blocks, however I'm going to try something new now to set clearer delivery-timeline expectations with you. Starting with this video, I'll release a new YouTube video every Monday and every Thursday.

My Calculus for ML YouTube playlist is here. The series is full of hands-on demos in Python (particularly the NumPy, TensorFlow, and PyTorch libraries) and all of the code is available open-source in GitHub.

The first quarter of the series covered Linear Algebra. We're now kicking off the second quarter, on Calculus. The third quarter will be on Probability and Statistics. The final one will be on Computer Science (Data Structures, Algorithms, and Optimization).