I have exciting news!
Since February I have been working on a video course with Packt Publishing, and the course is now published.
I call the course Unpacking NumPy and Pandas, the first in a planned series of four video courses introducing Python for data analysis that I’m calling Taming Data with Python; Excelling as a Data Analyst. (I will keep the titles of the later courses a secret for now.) These courses are intended to teach people Python data analysis from the ground up, assuming Python knowledge only.1 If you are just starting out in data analysis, these courses are intended for you. (This is especially true for this first course; there isn’t much here for even intermediate-level practitioners. That said, more experienced users could benefit from later courses, particularly the third and fourth courses in the series.)
In this two-hour video course, I cover three main topics:
- Setting up a data analysis environment with Anaconda, a popular data analysis environment, along with basic usage.
- Basic NumPy usage.
- Basic pandas usage.
I start the course by guiding viewers through the process of installing Anaconda, then showing its various tools. Viewers learn how to manage packages and environments in Anaconda, including how to use conda. I show how to create and use Jupyter notebooks, arguably the most popular platform for Python data analysis projects. Other topics include alternate environments to Jupyter and set-up and basic usage of MySQL.
After discussing the basics of setting up an environment, I dive right into using NumPy. NumPy defines
ndarray objects, without which data analysis with Python would be impossible. This highly optimized package allows users to store and manipulate data with a clean syntax. Complex mathematical computations, such as linear algebra, are made possible with NumPy. I show viewers basics about NumPy objects, how to slice
ndarrays, and how to do computations with them.
I move on to pandas, the second key package for data analysis with Python. pandas introduces two objects,
DataFrames, that make data storage and manipulation even easier. I show viewers how to save and load data into these objects, how to use indexing to get desired data, their useful methods for calculations, and how to quickly visualize their contents.
The videos in the course–narrated by me–include not only an explanation of the topic at hand but interactive demonstrations, so viewers can see how to use the software and follow along if they so desire. The video course includes the Jupyter notebooks I use in my demonstrations; viewers can run my code blocks to replicate my results, and edit them for their own experimentation.
You can buy the course on Packt’s website. If the price of the course is an obstacle, perhaps consider watching it on Mapt, Packt’s subscription service, which gives you access not only to my course but everything Packt has published (and they publish a ton of stuff), along with one free book of your choice to keep every month (likely without any DRM; just a plain ol’ PDF). I also hear that Packt’s videos are available on other services, such as Lynda (but don’t quote me on that) and Safari Books Online.
Creating this course took much more time than I initially thought it would when I agreed to it, and was not easy (you may recall I fretted about the amount of time spent on non-academic activities lately in my most recent blog post). With three more courses on the way and growing academic challenges, I’m increasingly stressed these days. Honestly, if I had known I would be investing as much time as I had in projects like this, I might not have agreed to them. (I couldn’t resist the prospect of being published; I would not mind at all if later in my career I wrote books and created courses on a regular basis, and I felt this was the first step and an opportunity I could not pass up.) That said, I’m thrilled to finally see the end result, and I can’t wait until I have more books and courses in my portfolio. I thank Packt for offering me this project (Kevin Sequira, acquisition editor at Packt, in particular) and guiding me–who’s painfully new to this–through the process, including my patient and understanding editor, Ajinkya Tilak.
If you like my blog and would like to support me, perhaps consider purchasing the course. If you have no need for it or don’t have the money to spend (which I understand completely; I don’t live a life of glamour myself, being a graduate student), I’d love for you to spread the word about the course. Tell a friend wanting to get started in data analysis or data science, or even share this post on Facebook or Twitter or whatever your preferred social network is. Directing more eyes to the course helps. Write a review if you have watched it; I would love to hear your feedback, both positive and negative (though if negative, be gentle and constructive please).
I have started a section of my site dedicated to video courses (I dream someday this shall include books to; they’re my preferred genre), where I link to a further description of the course. Future courses (and perhaps books too someday) shall be included there as well.
Thanks for reading!
UPDATE: Packt Publishing has made some of the videos included in the course available on YouTube. You can watch these to get an idea of what’s in the course.
- If you don’t know Python, plenty of great learning resources exist online. I learned Python last year in less than a month of personal study using Allen Downey’s book Think Python, which you can read/download free from his website or buy from O’Reilly. He assumes little about you, not even that you have programmed before. Python is easily learned and a great choice of language for data analysis projects. Only R rivals Python in this domain, in my opinion. ↩