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From 0 to 2000 Data Science Practitioners: Confetti AI's 2020 Year In Review



Confetti AI was born at the start of this year with the goal to help talented data science and machine learning practitioners jumpstart their careers. We've been in data science and machine learning for nearly a decade and we've experienced firsthand the difficulty of preparing for jobs in these fields.


Today Confetti AI provides the most comprehensive interview question bank for data science and machine learning, study guides to help individuals fill their conceptual gaps, and skills assessments covering topics like SQL, Pandas, machine learning theory, machine learning productionization, and more.


While this year has been a tremendously difficult one for millions of individuals around the world, we wanted to take a moment to celebrate some of our platform's milestones and show gratitude for what we've accomplished thanks to all of our wonderful users.


Here's a look at Confetti AI's 2020 year in review, where we've come and what we've learned.


Stats At A Glance


We spent the bulk of this year building out the platform and creating content for Confetti AI. Therefore our "official" launch came around the end of August. As we've been announcing our content on various distribution channels, our platform has seen substantial growth in monthly active usage and number of newsletter subscribers.


Another particularly noteworthy detail is how many people are using our resources. As of this date, users have done more than 10K topic-specific skills assessments and completed over 73K machine learning and data science questions!


Highlights


Outside of some of our raw statistics above, there are a few other milestones we want to highlight. Early on in our marketing efforts, we discovered that Reddit was a particularly successful distribution channel for us. By targeting relevant subreddits including r/datascience, r/learnmachinelearning, and r/MachineLearning we found users with whom our resources resonated. One of our biggest achievements on Reddit was having a resource become one of the top 25 posts of all-time on r/datascience.


Though we have achieved some degree of internet virality through our resources, we are even more proud that many incredibly talented individuals are using our platform. Today students at more than 60 different universities (many of which are the top educational institutions in the world) visit our site to augment their learning.


Top Resources


We released a lot of educational tools this year, including curated skills assessments, conceptual and programming questions, study guides, and informational articles. Here are our most popular resources based on usage:


Articles

- 26 Top Machine Learning Interview Questions and Answers

- What You Can Expect in Your Machine Learning Interviews

- Best Online Machine Learning Courses: 2020 Edition


Exams

- Machine Learning Starter

- Numpy Practice I

- Data Science I


Things We've Learned


It's been an incredibly eventful year, and we wanted to share some of our biggest learnings.


1. The data science and interview landscape is complicated.


Going into this project, we knew from our personal experiences that data science and machine learning career preparation is complex. From a job interview perspective, there's just so much one has to know to to feel truly prepared. While we had our biases about this, in talking to our users it became more clear how pronounced this phenomenon is. Job candidates today feel truly overwhelmed by all the different skills they are being tested on.


Feeling good about your classical machine learning theory? Expect to get an interview question about statistics. Practicing your deep learning fundamentals? Expect to get a data structures coding question. And so on.


While this makes job preparation incredibly difficult, this seems to be the unfortunate nature of the market right now. As companies that are hiring get more specialized in the roles they are hiring for (instead of just the catch-all "data scientist"), it's possible that some of preparation load will be eased. In the meantime, if you are one of those people feeling like there's just too much to know, don't blame yourself! Others feel exactly the same way you do.


2. Invest in making your user interface good.


There's a common wisdom that design shouldn't be your priority when building something new. As developers by trade, we happily subscribed to this viewpoint because it meant we had to spend less time focusing on the stuff we weren't good at.


The reality is that today having a good design can do you a lot of favors. There are is so much noise out there on the web today and people's standards for a functional UI are getting higher. We finally bit the bullet and got help from a designer for our platform, which ended up being one of our best decisions this year.


We had countless conversations with people that said they either discovered us or spent the extra few seconds on our page learning what we do just because our design was eye-catching. Remember, people make implicit visual decisions all the time.


3. Viral growth is not the same as sustained growth.


As we mentioned previously, we had quite a few viral moments in marketing for Confetti AI. Ones where we went from 10-15 people on our app per day to like 3000 in the span of a few hours. While it felt amazing at the time and we felt like we were riding a rocket ship to the moon, this level of activity did not last.


After every one of those 15 minutes of internet fame, there was a slump. Some had a longer tail than others but a slump nonetheless.


It became clear to us that this was not a way to sustain engagement. After every front page Hacker News post, every viral Twitter thread, eventually the world moves on.


So how do you sustain growth? We don't have a perfect answer yet, but for now we are continuing to double down on the quality of our offering so that organic word-of-mouth does some of the heavy lifting for us. In addition, we are working a lot on content marketing so that SEO improves our passive discoverability.


4. Don't use click-bait when marketing.


People aren't stupid. We've become conditioned to ignoring gimmicky titles. Even if you are able to "trick" certain groups into clicking on your post, you leave a bad taste in people's mouths.


While we never explicitly tried to game the system with click-bait, we found that people were sensitive to stuff we didn't even consider false advertising. This is probably a consequence of years of previous bad internet experiences.


Since then, we've found the truest way to a reader and user's heart is being honest and focusing on your genuine value proposition from the first sentence. You can't go wrong with this strategy.


5. Always remember the mobile experience.


Our vision for Confetti AI was never to have phones be the primary medium of interaction. We have resources like interactive programming questions that just don't work well on mobile. Moreover as programmers, we are desktop power users that always prefer the benefit of a high resolution 4K monitor.


Imagine our surprise when in the first few weeks of launch, Google analytics told us 50+% of traffic came from mobile! 🙄


After that we quickly fixed our mobile experience. Today it doesn't have complete parity with the desktop one (and we still recommend desktop for the best experience), we've certainly improved the first thing people see on their phones.


Remember, first impressions matter and people will quickly move on if their first few seconds on your platform are icky and clunky.


6. People may like your content though moderators may not. 😅


As mentioned previously, a lot of our early traction came via sites like Reddit which are open (but moderated) communities. Moderation is important, of course, as it preserves the integrity of the community and the quality of interaction on a forum.


So when we posted on various subreddits, we always wanted to add value to the internet discussion, to offer resources to help people. Most of the time, users thought we were providing value and even thanked us for our contributions.


Moderators, on the other hand, didn't always agree. A lot of our posts were taken down on various subreddits just as they were gaining momentum and upvotes.


We've always made every effort to adhere to the guidelines of a community. We hypothesize that getting taken down has to do with something about limiting self-promotion. We can't say for sure though. We've tried to reach out to the mods of these communities to learn from our transgressions but have never received responses...


Not sure what the lesson learned here is other than that the internet is a funny place where you can always tick someone off (and maybe remember to tip your mods?)



And with that we want to thank all the students and data science/machine learning practitioners that have made 2020 such an incredible year for Confetti AI. Your enthusiasm and ambition make all of our efforts worthwhile. Looking forward to many great things in 2021!

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