data science

Doesn’t this make you miss dplyr tho?

Doesn't this make you miss dplyr tho? — Ashir Aseesh Borah (@AshirBorah) May 2, 2020 Listen I’m as surprised as you. I’m on record in multiple places talking about how much I love dplyr. I’ve referred to it as “my favorite package” more than once, and have definitely said that you could pry it “from my cold, dead hands.” dplyr is a fantastic package for data manipulation, and has been the primary workhorse in my analytics workflow for the past few years.

There’s No Crying in Data Science

What is it like learning Python if you know R? It’s like being every single character in this scene all at once, all the time: Which is to say that the last two months have been some of the most frustrating months of my learning life - and I’m including 3.5 years in an Immunology & Infectious Disease PhD program in that calculation. Sure I’m a little frustrated with Python, and I’m kind of frustrated with the fields of machine learning (and deep learning and data science and AI), but nothing compares with how frustrated I am with how slowly I’m learning.

Sid Meier’s Civilization and My Machine Learning Path

Have you ever played Civ? You start the game by choosing your historical figure, set up your gameplay, and are suddenly dropped in the middle of nowhere. Well, you’re somewhere - but everything on the map is obscured, completely shrouded in an impenetrable fog. Which in a lot of ways is what it feels like when you decide that this is it. This time you’re going to learn Data Science//Machine Learning//AI//Deep Learning//the “it” thing of the moment.

R4DS (v1 & v2): A Retrospective

Introduction As all amazing opportunities in my life are wont to do, it started with a tweet: …which resulted in me spending last Friday talking data science education with some of the great folks at JHU, largely centered on work happening with the Chromebook Data Science project. It’s rare that I find myself dealing with imposter syndrome, but I did spend Thursday night eating all of my feelings of doubt and insecurity.

Learning to Learn: Process Over Product

Learning to learn can be… well, challenging I find myself on social media for the longest stretches of time when I’m just beginning a project. Social media is a fantastic distraction from getting started, because burying yourself in the seratonin hits from a Like or ❤️ is far more pleasant than staring at the sheer amount of project yet to do. It doesn’t even have to be a big project to send us into a perpetual state of procrastination.

R4DS June Challenge: Summer of Data Science 2018

We’re going on a learning adventure! This summer we’d like to challenge you to participate in the “Summer of Data Science” Twitter initiative hosted by none other than Data Science Renee! While the Summer of Data Science (SoDS) supports all languages and endeavors related to becoming a (better) data scientist, we wanted to host a space within our R4DS Online Learning Community for our community members to ask questions, provide support, and in general discuss what we’re learning within our chosen text!

Kaggle panel recap: my data science journey

Introduction This past March I had the distinct pleasure of participating in a panel about making the career transition to data science as part of Kaggle’s CareerCon 2018. As a result of this experience, I’ve gotten enough emails asking for more information about my data science journey that it warrants a blog post, per David Robinson’s advice: When you’ve written the same code 3 times, write a function

YMMV: non-profit data science

(YMMV = your mileage may vary) Introduction Feeling inspired by some recent data science collaborations, on Friday I released the following tweet into the wild: want to build data science experience? reach out to a local non-profit you're interested in, and ask them if you can volunteer with data collection, cleaning, and basic analysis and reporting. you get experience, the NPO gets a product they desperately need, and everyone wins.

R4DS April Challenge: time for some spring cleaning!

Introduction April is such a fantastic time of the year - the weather starts to warm up, trees and flowers start blooming, and many of us start in on some spring cleaning. So it’s only natural that we’d focus on the same thing in our R4DS Online Learning Community! Finishing up March’s Viewing Parties Thank you to everyone who participated in our viewing party in early March, where member Chris B.

Data Science with R: how do I start?

It always starts with a DM on Twitter, where someone shares with me their personal data science ambitions, where they currently are in their plans, and then they follow up with a request for me to help them figure out where to go next. I love these messages—they’re an affirmation that the R community continues to grow and attract new members in part by creating a welcoming and supportive space for beginners, and that our community members are deemed approachable (enough) for someone brand new to R to reach out!

R4DS: the next iteration

Background In August of 2017 I launched an experiment, referred to as the R for Data Science Online Learning Community, with the goal of creating a supportive and responsive online space for learners and mentors to gather and work through the R for Data Science text. Like most online learning endeavors, we had a massive surge of interest at the onset, with exponential drop-offs week after week as we progressively worked through each chapter based on an established schedule.

Join the “R for Data Science” online learning community

Introduction Every day I talk to individuals who are working in one field, but are interested in learning more about how and where to get started in data analysis either as a hobby, or as part of a broader career transition. While there are a myriad of options out there - from online bootcamps to self-guided study through various web platforms and textbooks - it can be daunting to start something like this on your own.

Want to work with Data? Don’t wait.

Data is everywhere, and it seems you can’t go more than 48 hours without hearing how data scientists are going to rule the world–if only we can train enough of them. The thing is, you don’t need to wait until after you get that Master’s (or PhD) in Data Science, or even until complete that online course that cost you several thousand dollars. You can start working with data now, and depending on your interests, skills, and commitment to learning, be job-ready in six months to a year.