You likely know that Python is one of the fastest growing languages for data science.
This is a discipline that combines the scientific inquiry of hypotheses and tests, the mathematical intuition of probability and statistics, the AI foundations of machine learning, a fluency in big data processing, and the Python language itself. That is a very broad set of skills we need to be good data scientists and yet each one is deep and often hard to understand.
That's why I'm excited to speak with Joel Grus, a data scientist from Seattle. He wrote a book to help us all understand what's actually happening when we employ libraries such as scikit-learn or numpy. It's called Data Science from Scratch and that's the topic of this week's episode.
How secure is your application? Do you know the main vulnerabilities that most apps suffer from? How would you even start answer these questions? On this episode of Talk Python To Me, Justin Seitz is here to tell us all about it.
Welcome to Python Bytes. Python headlines delivered directly to your earbuds. In this first episode we cover PyData videos, safety-db project, and more!
In this episode, our guest is Nicola Iarocci discuss his open-source RESTful framework named EVE. You will learn about the history of EVE, how you get started, and some of the more notable deployment and users of the framework.
Nicola and Michael talk about the careful balance of leading a successful open-source project in EVE and keeping the day job going. You'll also learn why Nicola chose MongoDB as the default backend for EVE.
We also discuss how Nicola got into Python and compare and contrast the open-source world of the Python community with other ecosystems such as the C# / .NET ecosystem.
Machine learning allows computers to find hidden insights without being explicitly programmed where to look or what to look for. Thanks to the work of some dedicated developers, Python has one of the best machine learning platforms called scikit-learn. In this episode, Alexandre Gramfort is here to tell us all about scikit-learn and machine learning.
If you run into a problem with some API or Python code what do you do to solve it? I personally through a few keywords into google, sometimes even before checking the full docs.
Why does this work? Because invariably an excellent conversation and answer from StackOverflow comes back with just what I need.
This week you'll meet Martijn Pieters. One of the top Python contributors at StackOverflow with over 16,500 questions answered and a reputation of over 500,000.
What do you focus on once you've learned the core concepts of the Python programming language and ecosystem?
Obviously, knowing a few fundamental packages in your space is critical. If you're a web developer, you should probably know flask or pyramid, and sqlalchemy really well. If you're a data scientist, import pandas, numpy, matplotlib need to be something you type often and intuitively.
But then what? Well I have a few topics for you! This week you'll meet Mark Summerfield, prolific author of many Python books. We spend time digging into the ideas behind his book Python in Practice: Create Better Programs Using Concurrency, Libraries, and Patterns.
What I really like about these topics is that they have a "long shelf life". You find them relevant over time even as frameworks come and go.
It's the end of the year and many of you are probably kicking and taking it easy without a TPS report to be seen. So we'll keep this fun and lighthearted this week. We've teamed up with the Partially Derivative podcast and we're running down the top 10 data science stories of 2015 in this joint episode.
Data science has been one of the major driving forces behind the explosion of Python in recent years. It's now used for AI research, controls some of the most powerful telescopes in the world, tracks crop growth and prediction and so much more.
But with all this growth, there is an explosion of data science and machine learning libraries. That's why I invited Pete Garcin onto the show. He's going to share his top 10 machine learning libraries. After this episode, you should be able to pick the right one for the job.
Full show notes at https://talkpython.fm/episodes/show/131/top-10-machine-learning-libraries
Writing good, clean code and having a deep working knowledge of Python is critical to your success as a Python developer. But if you look at those who have truly excelled in their career, it's often because they bring something in addition to coding skills.
We all want to be the most successful and satisfied version of ourselves. But that's much easier said than done.
That's why I'm excited to introduce you to John Sonmez. He's had a brilliantly successful career as a developer and he wrote a book to help all of us do the same. It's called Soft Skills: The software developer's life manual and it's packed full of concrete, practical steps you can take to stand out in the tech industry.
Do you have a blog? How many articles have you written for it? Do you find it hard to keep writing or hard to get started doing technical writing? We might be able to help you out with that this week.
You're probably aware that blogging is one of the key ways to establish yourself as a thought-leader in the industry. You'll make more connections, open more opportunities, and likely find your work more rewarding if you share your experiences and expertise through blogging.
But it can be challenging to keep writing or find time for writing. That's why I asked A. Jesse Jiryu Davis from MongoDB to share his thoughts on writing an excellent programming blog.
This is Python Bytes, Python headlines and news deliver directly to your earbuds: episode 4: recorded on November 28, 2016. In this episode we cover the case for Python 3, asyncio, pyston, pydoc.io, and q.
Full show notes at https://talkpython.fm/140
How do you learn libraries or parts of Python itself that you don't have actual work projects involving them? Whether that's SQLAlchemy, Slack bots, or map APIs, actually building projects (small and large) with them is really the only way to gain true competency.
You might try a 100 days of Python code challenges.
This week you'll meet Bob Belderbos and Julian Sequeira who created PyBites. The have done a few 100 days of code challenges and are here to share their experience and some concrete examples.
Quick: What's the difference between a module, a package, and packing in Python? Find out in this episode of Talk Python To Me.
All Python programmers use the import statement, but do you really know how it works and what it allows? Join David and Michael to take a deep dive into diabolical issues related to modules, packages, and imports. When we're done, you'll finally be ready to unleash your million line micro framework on the world!
Algorithms underpin almost everything we do in programming and in problem solving in general. Yet, many of us have partial or incomplete knowledge of the most important and common ones. In this episode, you'll meet Adit Bhargava, the author of the light and playful Grokking Algorithms: An illustrated guide book.
If you struggled to understand and learn the key algorithms, this episode is for you.
So, you've build an amazing Python web app and now what? You want to put it online of course but that's a whole different skill set. You're in luck, because Matthew Makai is here to tell us all about deploy Python applications on this episode of Talk Python To Me.
In this show, we'll be discussing Matt's book The Full Stack Python Guide to Deployments, how Twilio manages their deployments, and what to consider when graduating from basic deployments to large-scale professional environments.
How often do you meet people who are looking to get into the software development space? Do they ask you for advice? Maybe they want to know your story of how you got started and landed that first big job. Maybe they want to know what they should be doing right now.
This episode of Talk Python To Me is the second in a two part series that attempts to bring a wide spectrum of thoughts on this discussion. It's "Getting your first dev job as a Python developer, part 1", episode number 41, recorded December 10th 2015.
Full show notes at https://talkpython.fm/139
Data science is one of the fastest growing segments of software development. It takes a slightly different set of skills than your average full-stack development job. This means there's a big opportunity to get into data science. But how do you get into the industry?
That's what Hugo Bowne-Anderson is here to tell us all about.
Welcome to Python Bytes. Python headlines delivered directly to your earbuds. In this episode we cover the new features in Python 3.6, text processing with Pynini, Python is 2nd most popular language on GitHub and more.