resolaibohp

Curious what companies these are or where you are seeing this data. This has not been what I have seen, I actually think leetcode is becoming more and more common in interviews. Just adding some of my own anecdata.

I was able to witness the 2017 eclipse under the path of totality and can confirm that animals really did act strange.

Minutes before totality started, a black bear walked casually within 10 feet of me. I remember thinking that my viewing experience was going to be ruined because some bear was going to attack me. It ended up just looking at me and strolling casually along. Very strange indeed.

This article is pretty spot on. As someone who has worked in data science/analytics for over 6 years I have found that the field is filled with hype, managers who are not sure what data science actually is, and an absurdly wide amount of skills jobs expect you to be able to do well.

Apply for and interviewing for data science jobs is a total nightmare. You are competing against 100s or even 1000s of applicants for every job posting because someone said it was one of the sexiest careers of the 21st century. Further exacerbating this, Everyone believes that data is the new oil, and large profit multipliers are just waiting to be discovered in this virgin data that companies are sitting on. All that is missing is someone to run some neural network, or deep learning algo on it to discover the insights that nobody else can see.

The reality is that there is an army of people who know how to run these algos. MOOC's, blogs, youtube, etc have been teaching everyone how to use these python/R packages for years. The lucky few who get that coveted data science job can't wait to apply these libraries to the virgin data only to find that they have to do all kinda of data manipulating to make the algos even work, which takes days and weeks of mundane work. Finally they find out the data is so lacking that their deep learning model does very little in providing actual business value. It is overly complicated, computationally expensive, and in the back of your mind know you can get the same results using some simple logic.

Managers who don't understand data science fundamentals learn from the news and have their data scientist implement those buzz words so they can look good in front of their bosses.

I think there is a place for data scientists who understand the fundamentals of the models out there, and know when you should not use them. Data science is also increasingly a subset of software engineering and a good data science in a tech company should be able to code well. I also think that there is not some huge unmet demand for data scientists. Just a huge amount of hype and managers wanting to look good by saying they managed a data science team.

I currently run analytic queries on a page view dataset around ~600 million rows on postgres. You can start by looking into partitioning by date. Also take a look at your IO on your queries. IO was the biggest bottleneck for me. Make sure you have hardware that is not limiting it.

To even use FMLA you must have worked for the employer for at least a year, at least one of the major restrictions. For example if you have your baby a couple days before your 1 year work anniversary and your employer does not offer parental leave, you are pretty much forced to quit just to be able to take care of your baby for 3 months. FMLA is not the solution for everyone.

Denver seems to pay very low, considering the cost of living these days. I think they refer to it as the "Denver Discount"

Desperate or naive people take these jobs, these people only exist where there are more job seekers than jobs. It is a chance for the desperate to finally gain that experience. Being indispensable is not possible in those conditions.

Don't invest into option contracts if you are not already familiar with them. They come with much more risk. But most all brokers offer them, even robinhood.

To be clear I was not trying to make fun of the OP. I frequently hear people say that they would invest if they had some certain amount of money. I always like to point out that there are plenty of ways to invest without huge amounts of capital, however with much more risk. It is kind of a temperature gauge to see how serious they are about their words.

If you really believe in Tesla to bet big on them you do not need 100k. You can buy 4 $275.00 call contracts for approx 26k that expire in 2020. This would be equivalent to ~$110k worth of tesla stock. And if you are really a believer you can just pick up a bunch of way out of the money contracts making the initial cash requirements even lower.

But this is exactly why TSLA is worth so much right now as so many people think the same way as you. They all want to bet big on Tesla and Elon. It is the most interesting stock to watch as it hemorrhages money and just keeps going up and up.

There are not as many data scientist jobs as the hype makes it seem. Maybe this will change in the future but right now there is not such a lack of qualified candidates to justify taking chances on lesser degree candidates.

All this hype makes it so everyone wants to be a data scientist. You get people who change careers to go into this new hot career. You also have a pool of people who have been working with data well before the hype with experience. The people trying to break into data science will have a very hard time competing with the people with experience over the pool of jobs out there.

I don't think anyone doubts that the skills can be learned outside of school or fancy degrees.

The problem is that there are hundreds of applicants in your situation WITHOUT experience. There are usually a couple of PHD or MS applications WITH experience for every job. Who do you think the company would give preference to?

When my company posted a data science job we received something like 300 applications on the first day. When looking at them most were people with no experience but a fresh degree or bootcamp grad plus all the other people like you who want to change careers from software with portfolios of data science projects.

The field is absolutely saturated with people who want to be a data scientist but have no experience. This is where some of that gate keeping comes from.

The people who have it easy are the ones with a MS or PHD and years of experience doing data science work at companies under their belt. There are very few of these people right now.

There is this idea that data science is needed everywhere and there is a HUGE supply of jobs. As an example if you search for data scientist jobs at glassdoor.com in San Francisco there are ~2000 jobs. If you search for software engineer in San Francisco there are ~9000. Similar ratios can be found in any major tech city. Data science does not scale like software engineering in companies but the narrative out there is that this is the job to be in and there is this huge unmet need. It is all hype.

In my experience, candidates who have experience in dev work but could not pass our interview tests always end up in a dev role somewhere else shortly afterwards. Even some of them go on to find much more success.

Either there are a lot of companies which employ unskilled developers who cannot even code fizzbuzz, or interviewing is not a great way to understand specific types of people.

Yep, a lot of the people who want to get into it run away when they find out you need to know how to code and understand the math. For some reason there is this idea that it is easy money.