I'm going to cry my heart out, so you've been warned.
Long story, short.
- I was the bookish nerd until last year of engineering
- Started writing code to build things in the last year
- Luckily got a job in one of the "mass recruitment" companies
- Got offered their Gold tier so I could skip training and join workforce on Day 7 and get 2.5x the salary offered to freshers because of communication & partially code
- "Development" work is adding small features to old code, changing organization practices (moving to Jira, Github integration, creating CI/CD concept, Wiki-fying documents, etc).
- Realize I suck at Data Structures and Algorithm.
- Tired of monotony and stagnation, tried learning DSA but even the basic question that requires some "logic" looks like a failure.
- I am restarting my algo and DS journey. But is there anything else I can do to get a job? Should I look at changing fields? If yes, how. I love management, CX, etc. I do love coding too, but I am not the competitive coder and that makes me believe that I am the impostor Among Us.
- Communication Skill ✅ Presentation Skill ✅ Coding 🆗 DSA 😧
Long story, long. Chapter 0: The beginning. I was great at computers from when I was young. I was no Chintu developing applications and having investors wrestle to reach me, but I did some basic static HTML pages, could figure my way out in fixing computer and internet issues, etc. This got me the prestigious stature of "geek" and "gizmo" in a household where being able to surf the internet was akin to cavemen discovering fire. Then on, I decided I wanted to study computer science despite being from a school (rather, board) that did not even have Computers as a subject in 8th, 9th and 10th.
Come 11th, I wanted to take up Computer Science and take it up, I did. The first chapter (and I kid you not) was about introduction to computer science, where we had to rut what a peripheral device is and what a non-peripheral device is. The class was basically a teacher highlighting contents of the textbook that would fetch us marks. I nope'd out. Being the cream student, I had an option to switch to Electronics because the demand for electronics was so low that they had only 1 section with 63 people and were really looking to make that an even number. After a few trial classes, I realized electronics is fun too and I ditched my long term love to study electronics.
I guess somewhere, there was always a zeal to want to learn computers but I did not know the sources. I knew I could "learn C/C++" but what would I do with that was something I never knew. I consulted a couple of teachers in my college regarding this but what they'd suggest is for me to learn it to get good marks, improve my score and get into a good engineering college. 🙄 Their assumption was that the real CS happens in engineering college and 12th does not matter.
I stuck to electronics, scored fairly decent marks and in engineering, I opted for CS.
Chapter 1: University. (Can skip) For some reason, I thought of University as a magic box where a dumb person goes in and comes out as a "coder, rider, provider". But alas, life is no TikTok and I realized it on day 1 when in my Science and Humanities department, I was the only one who did not know a line of code. There were kids who'd be inducted into Mechanical, Electronics, Civil departments and 99% of them knew how to write some code and I was caught off guard, by this. I thought I could wing it and yet again focused only on scoring marks. This is what I was thought whole my life and it did not help that the Director drew a correlation that every kid who scores a 8+ GPA lands up a job that pays them at least 12lpa.
Focusing on marks, studying what "looked" important and with a goal to maintain a 8+ GPA, I strictly adhered to rules that would help me achieve my goals. 3 years of this, you could ask me to write an API and I would first see if this was "in syllabus" or not. In the 3rd year, we had something called Practice School. This was a term that we borrowed from some IIT / NIT, but proudly wore it on our chests like it was some discovery we secretively made. Practice School is a fancy term for unpaid internship where you could either work for a company, or work under a professor to do some research work for some minor credit. For some weird reason, I was asked about what activity I did apart from academics in every interview that I attended. Did they really expect me to do anything apart from study and score marks? 😲 \s. So, obviously, I got a great internship in a very good company called the company of friends, located in the Boys hostel.
With an industry internship that went flying while jamming to "Hum Toh Udd Gaye, by Ritviz", I was left with no choice but to get a research internship. Thanks to my face, communication and luck I could convince one professor that it was me who discovered gravity and that I have some secretive potentially mind-blowing scientific research going on that would shock Stephen Hawking. I was a Research Assistant to a professor with initials MDD (which co-incidentally also stands for Major Depressive Disorder).
Chapter 2: Research Assistant. (Can skip) Being a research assistant, my job was to build apps to capture data, propagate these apps to a set of users and generate datasets. Not bragging, but I could learn the technology he wanted and build apps very quickly. It was not production quality as this was the first "project" I was working on, but it was there. It could house about 100-150 users who actively used the application to log data. Spending more time towards this, I neglected studies a bit. My grades were still the same thanks to the easing up of the portions and subjects. I absolutely loved what I was doing and the fact that I could see a weekly impact when I release a new version of the app was something that gave me immense thrill. The professor, too, was extremely impressed by my efforts and gave me a couple of interns to "manage" in order to churn more apps. This was fun, we experimented with multiple frameworks, presented our "research" work to a couple of potential "investors" and this experience improved my communication, presentation, documentation, coding and every other skill I could think off. In one of the monthly "appraisal" scheduled by the professor, I asked him how "industry-ready" I was and he gave me compliments like I was the one of the many forms of Lord Vishnu. I was pretty satisfied and I could nail interviews (is what I thought).
Chapter 3: Placement Season (Please dont skip) As 3rd year came to an end, placement season began. If placement season was a mood, it would be the mood associated with "winter is coming". First company that opened up doors to an interview was Uber. With a pay package that equates to my family's 2-year pay, they came in with a bang. The first round was an online round. As I read the questions, i could physically feel my hair jump and fall off and the one's remaining grey themselves in order to fool my body that we're old now and death was only a matter of time right now. I could solve 1 question, but most people could solve 2. I discussed this online and found out that Uber is notoriously asking difficult questions and that makes sense because they're paying a huge salary.
I was not aiming for such a huge salary, so I was fine. After this came Intuit, Microsoft, GS, AWS, HP, Cisco, Myntra, Sabre, Shell, Infosys and I could not clear even one of the first coding rounds. Sometimes I got 2 questions right, sometimes I got 1. But all the times I never got the interview.
I was genuinely depressed and realized that it is time to up my DSA game. This game isn't new to me. I was "preparing for placements" by referring to sources like HackerRank (which was the go-to choice of more than 90% of the recruiters). I reduced the time spent on this because I was convinced that my practical experiences will be valued. I restarted my practice and one fine day a small company came to campus. They asked the most simplest coding questions that just tested if you can translate the logic to code. I could and I got in, after 38 rejections and 1 interview. Pretty much a TWICH-isq company.
Chapter 4: The work (Please dont, thx) The company that I got into typically trains all the employees for 6-8 months before giving them work. But thanks to my practical experience, I was one of the 10% people who was offered a role to join immediately out of college for a 2.5x increase in pay. I thought my luck is changing and apna time aayega. I joined the company. Next month, I will have completed 3 years in this company. The most "development" work I have done is add 3-4 minor "adapters" to the existing product and expand support. Apart from that, I aided migration to Jira, Github, setup CI / CD pipelines, got the Wiki culture, etc. It's a very old fashioned place but what I got going for me is that my team is not rigid in their mindset. In the 3 years that I am here, my salary has increased by a grand total of 7% (not annually, overall).
The ACTUAL question. I am restarting my algo and DS journey. But is there anything else I can do to get a job? Should I look at changing fields? If yes, how. I love management, CX, etc. I do love coding too, but I am not the competitive coder and that makes me believe that I am the impostor Among Us. I am not looking to go abroad as I am the sole breadwinner in my family and I can barely sustain with my present salary.
Thanks for reading if you did.
Sorry for making this Quora-isque, lordships of Reddit.
Thanks,
Regards,
Bye.
Introduction
Hello, my name is Alexander and I'm a recent graduate of
The Data Incubator program.
I know before I joined the program
datascience was an invaluable tool in helping me make my final decision to attend (a big shout out to all members who responded to my private messages) and this in-depth (read: way too long) review is my small attempt to give back. I also believe that my perspective is somewhat unique in that most of the existing great reviews out there are from a fresh grad/post-doc perspective while mine is one from someone who is already an experienced professional looking to transition into data science.
About Me
I'm formerly trained in computer science and have been a
Senior Software Engineer for over 15 years. I started my career with a deep love of operating systems and UNIX (I'm getting old, I remember installing RedHat from a dozen or so floppy disks) so the first half of my career has been spent writing fairly low-level/high performant (well, sometimes) code mainly in C/C++. So think storage and network drivers, boot code, deep packet inspection, and just general platform work.
However in 2016, I read
"The Great A.I. Awakening" in the NYT and was completely blown away by it. The only time I have ever heard of anything related to neural networks was the venerable perceptron algorithm which I knew was
used on some CPUs for branch prediction. But I had no idea how far the AI community had come with deep learning and vowed that I wanted to be part of the action too! Since then I've taken numerous online MOOCs on machine learning and now consider
Andrew Ng to be one of my closest friends (disclaimer: I have never met Professor Ng).
The Data Incubator (TDI)
If you aren't familiar with
TDI's Fellowship program, it is considered by many to be one of the premier data science bootcamps in the country (US anyway). It is an eight week program that is supposed to not only teach you the foundations of data science but also help you land a job as well through their ever expanding partner network. Their main competitor is probably
Insight but they are also battling an entire cottage industry of multi-week camps such as
Springboard and
Metis to name a few.
Admissions
What's makes TDI somewhat unique compared to other bootcamps is their non-trivial admission process which is broken down into three rounds:
- Resume/CV Review
- Project Challenge
- Capstone Pitch
My guess is the majority of folks get past the first round provided you have a graduate degree from a
reputable university but get rejected in the second round during the project challenge phase. They claim their acceptance rate is 2-3% which is about right: My cohort I think had ~4k applicants with a little less than 36 attending.
The project challenge is actually broken down into two or three subprojects with each subproject covering areas of probability, statistics, and basic data science (mostly dataset handling and EDA, not modeling). I would say knowledge of Python is pretty much required to get through the challenges intact.
This is how it works: TDI will send you a link to a real, midsized dataset (at least a few gigabytes) and ask you to perform some in-depth EDA about it. For the stats/probs part, they will ask you to write some code to simulate an experiment and then ask various basic probability questions about your results. So I would say if you are looking to "book-up" for the admissions process you should be fairly comfortable with Python, pandas/numpy, web scraping, and SQL. Obviously, challenge questions will vary with each cohort but my guess is they are all similar with respect to the skillsets you need to do them. You have a few days to finish it and can submit as many times as you want, i.e. you can work on some, submit, work on another part, submit, redo the first section, submit, etc.
I talked to several classmates about the challenge project and I think on average most folks said they spent at least 20 hours working on it - so be prepared. The admission process says it takes a few hours to finish but that is just not realistic unless you happen to be not only fluent in the above technologies (I was) but also familiar with the dataset in question (obvioulsy not).
Frankly, I found all the challenge problems to be a lot of fun! I got to not only flex my data science muscles but also learn a few things along the way. However, I must admit that if I hadn't gotten accepted into the program it would have been a heavy investment on my part with very little gain (I literally didn't sleep one Friday night coding one of the challenges up, read: the wife was not happy).
If you make it pass the first two rounds, next is your capstone pitch which consists of a stand alone short video of yourself explaining what you want to do for your capstone project as well as a separate video preso further explaining it. It's typically very high level though; some candidates (including yours truly) had alpha/beta-ish projects from other courses as a basis which gave us a clear advantage while others were still in the incubation stage (literally a single page with scribbles on it that vaguely resembled "Look out, Data Science!").
Note that this round is mainly about gauging your personality, how you present in front of a crowd under a time crunch, and how articulate you are when talking about a technical topic. My main advice here would be to practice your pitch and have a few sensible slides to work off of. Please note that you do not get to share your desktop but rather have to send your slides to everyone over a chatroom, which means you can't drive the whole process as you normally would in a formal presentation.
If all goes well, you're in! Congrats!
Fellow vs. Scholar
During the admission's process, you can apply to be a Fellow or a Scholar but what does that
really mean since both are part of the Fellowship program?
Fellows attend TDI tuition free but have to
agree to interview with TDI's partner network for a period of time before being able to interview with any company of their choosing and have to
attend in-person and thus can not be online.
Scholars on the other hand have to
pay a tuition fee but are
not tied to TDI's partner network. They can also
attend online and are
eligible for a 50% refund if they land a job with a partner.
However, after the admissions process, everyone is treated as a Fellow, i.e. there is no distinction during and after the program. It's purely an admission distinction only, and in fact the faculty at TDI treat everyone as Fellows - that includes partner meetings, projects, you name it. Again, there is no distinction once you start the program.
I attended the program as an Online Fellow since I worked full-time and was not going to leave my current job without another one in-hand.
Online vs In-Person
One aspect about the TDI Fellowship that really stands out is that it was designed to be accessible for online students since its inception. It's one of the major reasons why I applied in the first place and why I think the Insight program is a bit behind the times in this regard.
But that begs the question: Do you loose anything by being an Online Fellow instead of attending In-Person? Yes, there are a few drawbacks:
- It's harder to build a relationship with any of the extremely talented and smart people in your cohort. My advice is if you are near any of TDI offices you should trek in for various events such as Partner Panels mainly in order to mingle with your classmates. That's how I made a few friends!
- Some of the resident data scientists are honestly not as chatty as they should be over Slack. Getting help at times was like pulling teeth and it shouldn't really feel that way. Obviously, your mileage may vary, but that's how I felt throughout the course minus our capstone group leader who was awesome.
- Working with the HR folks over Slack can be very painful. For example, an announcement is made for a company you are really interested in interviewing with but have a few basic questions not covered by the CRM so you quickly direct message the primary contact managing that account hoping to get an answer immediately. Think again. My guess is these folks are uindated with requests all day long and it's just hard for them to be very interactive over chat. Most of the time I would send a direct message and wait at leat 24 hours for a response. My guess is if you were in person it would be easier to interact with these folks and get the information you need faster or at least confirm the person is researching the answer for you. When you are online, you stare at a blank chat window for a while and hope they get back to you. I actually think one big improvement would be to treat the CRM kinda like JIRA and have the ability to file Issues in your dashboard (each Issue would be a question that would start a thread). I think this would be much more effective way for everyone involved.
- Attending Partner Panels as an Online Fellow meant very little interactivity with said partner - both during and after the session is over.
- There is no graduation ceremony for Online Fellows. Very minor but definitey something they could easily improve upon. I kinda felt that with the Online folks they should do a final get together to talk a little bit about their experience and do some kind of exit interview as a group.
Location, Location, Location!
TDI is a self-styled WeWork company so all the classes are held in shared workspaces (read: at any given moment that location's wireless connection may just drop). At the time of this writing, the main office locations are in New York, San Francisco and D.C. Everyone else is online. Note that your daily lecture maybe given from any of these sites based on what resident TDI DS is teaching it.
Here's the thing: If you do decide to attend in-person, your location will have a huge impact on your placement as the bulk of TDI partners are located in New York and San Franscisco (which to some extent is to be expected). If you are willing to relocate though, then this may not be too much of a big deal. But for those looking for jobs in their local metropolitan area, you are most likely on your own. Don't get me wrong, TDI has partners worldwide (seriously they do) but there is definitely a high concentration of them that bookend the US Coasts.
Onboarding
Before you officially start your cohort, TDI has a 12-day onboarding program that they recommend you work on as well as
a homework project that you must complete and submit before attending class. So be prepared to start coding on day one after accepting the Fellowship.
The 12-day program is a crash course in data structures and algorithms, probability/statistics, and Python. Take it seriously. One of the biggest mistakes many Fellows made was to not to do go through the 12-day program in earnest and to work on their day-one homework assignment late in the game. I'm telling you as someone who knew about 95% of the 12-day program that I still needed a refresher on a few things: When is the last time you did any kind of dynamic programming? When is the last time you had to write quicksort from scratch? You get the idea.
A Day In the Life of a Fellow
Each day the course follows the following outline:
- Coding challenge
- One hour lecture on the topic of the week
- Time to work on the Mini-Project due that week
- Capstone group hours/pitch night discussion (twice a week)
- Job interview lecture (twice a week)
- Office hours (once a week) to discuss any mini-project issues/roadblocks
- Partner Panels (one or two a week after the first month or so)
Coding Challenges
Every morning you have an hour to do a coding challenge. They are mandatory and vary wildly in quality. Once thing that really bothered me throughout the course was the fact that the coding challenges are somewhat random both in topic and difficulty. I also generally believe that
HackerRank problems are generally less useful than say
LeetCode which groups coding interview questions by company which is key if you are trying to find a common thread of topics across industries to study (and also somewhat motivating knowing full well that you may see that exact problem in an actual interview). There were many times while struggling on one particular HackerRank "Hard" problem where I was like no one (not even Google) is gonna ask me this.
The resident data scientists will go over the solution afterwards; though the reference solutions are sorely lacking in detail and occasionally flat out ridiculous, i.e. the solution will focus on brevity instead of explainability. But overall, I do think the coding challenges were good practice and gets you in the mode of what a job interview could be like.
Lectures
Every day there is lecture on a particular aspect of a major overarching topic for that week. So one week it maybe on machine learning while another week maybe dedicated to
Apache Spark. Since they are only an hour each, it can sometimes feel very overwhelming, or counter intuitively very underwhelming, depending on your existing background on the topic and the course material itself. Most of the material is driven from a bunch of loosely coupled Juptyer notebooks which is good and bad - I found there was absolutely no excuse to have to open up multiple notebooks for an hour long lecture. I think that is just lazy and the notebooks should be re-organized accordingly. But I admit it's a relatively minor grievance in the grand scheme of things.
As for quality, again, it varies a lot. For example, I got a lot out of the Apache Spark and MapReduce lecture series and miniprojects since I have never worked with either of those technologies before and was very eager to learn. However ironically, I didn't get that much out of the machine learning ones since I already knew most of the material.
Overall, I think the lectures were OK. It's just very hard to teach advanced subjects in one hour chunks and it shows.
Mini-Projects
The mini-projects are nothing short of fantastic! Seriously, if there is one aspect of the program that I think they got right it's this one. They are challenging, realistic, and attempt to really test your understanding of the subject matter they cover. They are also as a result a lot of work and sometimes a bit frustrating too (and this is coming from someone who finished all of them a month early).
Every Satudary that week's mini-project is due and is auto-graded on a 0.0 to 1.0 scale. You need to get a 0.9 or higher on every mini-project to graduate the program. If you fall behind then you loose access to the CRM until you catch up. One thing they made clear is that this isn't to punish the Fellow. Rather, it's to ensure you understand the underlying course material - and I agree with them. Completing these mini-projects not only gives you a sense of accomplishment but actually makes you feel like a data scientist!
Capstone
Throughout the course you will be working on
your capstone. The capstone can be the one you pitched during the admission process or can be sponsored by a TDI partner. I know what you're thinking: Of course, I'm going to do one sponsored by a TDI partner since that will allow me to get in the door for a interview and land that dream job! Well, yes and no.
I had two online colleagues that did sponsored projects and were treated pretty poorly by their partners. One person finished the capstone and the partner didn't even show up to watch the person pitch it during Pitch Night (I'm pretty sure they didn't even get an interview to boot). Ironically, another was treated fairly well up until he actually finished the project (he did a fantastic job too) only to find out that his partner wasn't really interested in hiring him full-time. My advice is to research the "sponsor" first and try to gauge if there is a post-capstone process in place.
In general though, I would pick a capstone you feel somewhat passionate about - either in its subject matter or its methodology. Remember, this project is mainly for you in that it gives you something to talk about in an interview when asked what kind of DS have you done outside of saving passengers on the Titanic (drum roll please)!
Job Interview Lectures
These were obviously less useful for me since I have been in the industry for many years and have gone through several interviews in my career. There are few things that I strongly disagreed with that they stressed during the lecture (outside of maybe finance I would never ever wear a suit and tie to an interview - not happening) but that's for a another day.
Overall the job presos were presented well and I did learn how to write a proper cover letter (though a few of my more experienced colleagues debated if anyone actually reads them?). I am also very happy with my updated resume.
Pitch Night
Pitch Night is exactly what it sounds like: Fellows pitch their capstones to a few TDI partners in order to both sell themselves and indirectly the quality of the TDI program. I participated in it and I thought the experience was positive overall. I
did have the feeling that Pitch Night is more about TDI showing off their product (read: me) more so than about Fellows getting actual job interviews. But to be fair, that might have been more to do with the group of partners that showed up than the actual format of the program itself (read: selection bias).
If you do happen to be one of the lucky souls that gets voted to do Pitch Night, I encourage you to do it. The process is a bit nerve racking but the TDI staff really excelled at making sure you were ready for it.
Partner Panels
A Partner Panel is where a TDI partner is invited to one of the WeWork office spaces scattered over the country to give some background about their company, how they hire, what's it like to be a data scientist, etc. Usually, an in-person Fellow is the panel "lead" and is responsible for introducing the partner and asking the first set of questions.
I found that the quality varied widely - some partners were really prepared and made me want to interview with them. Others, not so much. But even more disappointing was the fact that Online Fellows had practically zero interaction with these folks which put us at a disadvantage. I also think that this process could be improved a lot by formalizing the partner side more, i.e. require them to follow a certain format and answer a few standard questions right off the bat. But overall I think they were generally positive experiences and I did learn a lot by just listening to partners answer other people's questions.
The "CRM"
TDI's partner network is encapsulated by their internal
CRM website that allows bidirectional communication between Fellows and Partners. Fellows can spam Partners with their CV/resumes and cover letters begging for an interview while Partners can peruse Fellow's resumes/CVs and contact them directly.
The good: TDI has built a fairly large partner network and it is ever growing. Also TDI's reputation as far as I can tell is pretty good within the industry, e.g. there are some partners who only hire TDI graduates believe it or not!
The bad: The CRM is simply not kept up to date. So there were many, many partners in the CRM that were either listed as inactive or unresponsive. Worse still, there were some partners who were listed as "active" who really weren't. Obviously, some of this stuff is out of TDI's control but it is disheartening to spend hours crafting a cover letter the stuff of legends only to find out the company isn't hiring.
The ugly: The site iself I found pretty awful in layout and design. Seriously,
Wordpress is their friend. I also thought even simple things are missing like complete descriptions about what the company does, have they hired TDI fellows before, and any interviewing tips you should know (you can ask for this but I think it should just be baked into the CRM as a free service for Fellows).
Conclusion/TLDR
TDI is overall a good program and definitely helped me transition into data science. But your results will vary a lot depending on your location, your background, and the number of partners involved in your particular cohort. I think the TDI staff is excellent but there are numerous places where they could improve the Fellowship's overall daily flow as well as make it a bit more personal (especially for Online folks).
FAQ
- Can you attend TDI as an Online Fellow while working a full-time job?
You can but it will be difficult. I did the program this way but benefited from the fact that I knew the basics of all the topics being discussed and I already worked from home twice a week. The latter allowed me the flexibility to attend all of the mid-afternoon lectures and participate in my capstone study group. Lectures are usually at noon EST so I could just use my lunch hour to watch them. There were a few Online Fellows who did somthing similar. Some were successful, some still struggled to balance everything and fell behind (but did evenutally complete the program).
But the workload is a lot so be prepared to work long nights and weekends. I lost every night and my entire weekend for a few weeks which can be really tough if you have a family (read: I do).
- Am I guaranteed a DS job if I take this program?
Probably not. Based on talking to a lot of folks who took the program in past cohorts, most were still looking for jobs months after the program ended. Again, it's really the luck of the draw when it comes to the number of partners who are participating and how many of them are actually hiring.
- Ok, but do I have a better chance of landing a job if I'm a TDI graduate?
I would say generally speaking, yes! Particularly if you have no formal background in DS whatsoever (like yours truly). It gives you an instant network to work with between TDI's and your fellow classmates. Moreover, if things go well, you will at least land a few interviews off the bat and get some practice in. All good experiences for landing that first DS job!
- Do you really have an advantage using their CRM versus applying directly on the partner website?
It depends. I believe in general, for smaller companies, you absolutely do have an advantage as a Fellow over a random applicant off the ether since you usually get to talk directly to a hiring manager. For larger companies though, I think you are treated like everyone else as most of the contacts are either someone in HR or a corporate wide recruiter.
- What does the average TDI graduate make?
It varies wildly depending again on your location, your existing experience, and the industry you are working in. You already knew that so this answer is not going to be very satisfying. However, it is the ground truth (literally).
- I heard that parters don't offer as much to TDI graduates since they have to pay TDI a finder's fee?
I forgot where I read this (either here or Quora) but this isn't universally true. There is some truth to it particularly for start-ups and small outfits where resources are by definition limited. But for medium to large enterprises, a finder's fee is fairly typical in the industry and has really noting to do with a certain position's salary range.
- I got accepted as a Scholar. Should I try to reapply as a Fellow instead? And do I have a better chance of landing a job as a Fellow vs. a Scholar?
Honestly, if you can afford it, I would advocate that you just take the course. Also, TDI is heavily biased towards having a PhD for the tuition-free Fellowship program so if you only have a Master's degree keep that in mind.
As I said earlier, it's not more prestigious - everyone is a Fellow once you are admitted in the eyes of both the staff and perspective employers. It's simply a matter of cost.
- Is there a TDI Alumni network?
Unfortunately, no there isn't. Apparently, this has to do with their partner confidentiality agreements (at least that was my impression after inquiring).
TDI Alum Slack Channel
I've started a TDI Alumni Slack channel which is invite only. Please PM me for details.
Our first dataset is related to the problem of identifying duplicate questions. An important product principle for Quora is that there should be a single question page for each logically distinct question. As a simple example, the queries “What is the most populous state in the USA?” and “Which state in the United States has the most people?” should not exist separately on Quora because the intent behind both is identical. Having a canonical page for each logically distinct query First Quora Dataset Release: Question Pairs. zdcs 2018-02-22 00:51:06 1020 收藏. 分类专栏： 深度学习 机器学习 自然语言处理 一般技巧和资源介绍. 最后发布:2018-02-22 00:51:06 首次发布:2018-02-22 00:51:06. 版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。 本文链接：https://blog Quora's first public dataset is related to the problem of identifying duplicate questions. At Quora, an important product principle is that there should be a single question page for each logically distinct question. For example, the queries “What is the most populous state in the USA?” and “Which state in the United States has the most people?” should not exist separately on Quora because the intent behind both is identical. Having a canonical page for each logically distinct query Our first dataset is related to the problem of identifying duplicate questions. An important product principle for Quora is that there should be a single question page for each logically distinct question. As a simple example, the queries “What is the most populous state in the USA?” and “Which state in the United States has the most people?” should not exist separately on Quora because the intent behind both is identical. Having a canonical page for each logically distinct query Tackling the Quora Questions dataset. Richard Townsend . Mar 3, 2017 · 5 min read. Semantic similarity is basically deciding how similar two documents are to each other, and assessing it is quite useful for things like identifying duplicate posts, semi-supervised labelling, whether two news articles are talking about the same thing, and lots of other applications. So I was quite interested Quora Question Pair consists of over 400k question pairs based on actual quora.com questions. Each pair contains a binary value indicating whether the two questions are paraphrase or not. The training-dev-test splits for this dataset are provided in. Source: Semantic Sentence Matching with Densely-connectedRecurrent and Co-attentive Information Quora dataset is composed of questions which are posed in Quora Question Answering site. It is the only dataset which provides sentence-level and word-level answers at the same time. Moreover, the questions in the dataset are authentic which is much more realistic for Question Answering systems. We test the performance of a state-of-the-art Question Answering system on the dataset and compare it with human performance to establish an upper bound. We will be using the Quora Question Pairs Dataset. Like any… Get started. Open in app. Sign in. Get started. Follow. 546K Followers · Editors' Picks Features Explore Contribute. About. Get started. Open in app. Quora Question Pairs: Detecting Text Similarity using Siamese networks. Quora Similar Questions: Detecting Text Similarity using Siamese networks. Aadit Kapoor. Aug 17, 2020 · 4 min This dataset includes all English-language questions and answers within the Quora Topic ‘Cars & Automobiles’ created in 2020. The set encompasses more than 95,000 questions and 280,000 answers, with detailed metadata to help you surface the most relevant and authoritative discussions from Quora’s expert community. The dataset is updated monthly. The dataset first appeared in the Kaggle competition Quora Question Pairs and consists of approximately 400,000 pairs of questions along with a column indicating if the question pair is considered a duplicate. After you complete this project, you can read about Quora’s approach to this problem in this blog post. Good luck!
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