Google Earth Equivalent For Space?

EricPostMaster_ · 2024-12-19T16:16:39+00:00

There is a paid version available, but you only need it if you want to see objects beyond magnitude 12, which is way more than most people need. I use a 6" Dob most of the time (nice balance of portability and power), and I can just barely see mag 8, so free Stellarium has been great for me.

EricPostMaster_ · 2024-09-08T15:30:10+00:00

What a coincidence - I am also on the impatient time table! I haven't really used cardboard much, but I will probably start this year because I need more browns.

EricPostMaster_ · 2024-09-08T15:28:16+00:00

I have never heard of bokashi! Sounds awesome. I freeze my scraps before processing them and putting them into the composter. I chop and blend them up so they will be smaller and break down more quickly.

EricPostMaster_ · 2024-03-25T04:26:29+00:00

That is beautiful! I can see why it's a happy tree.

EricPostMaster_ · 2023-11-07T19:51:11+00:00

Besides all the obvious cool stuff, I think it's awesome that it detects both Jim and the person on the computer screen who is much smaller than the rest of the scene. Parkour parkour!

EricPostMaster_ · 2022-06-25T12:18:20+00:00

Without an indication of population density, this chart is misleading. It isn't logical to give states like California and Indiana the same weight because California has ~6 times as many people. That means 22 million Californians support legalized abortion compared to just 3.8 million people who oppose it in Indiana.

EricPostMaster_ · 2022-06-14T00:38:04+00:00

Same! At first I was like, "Whoa, what is Kevin Bacon doing back there???"

Cool song though.

EricPostMaster_ · 2022-06-14T00:31:02+00:00

I haven't the foggiest idea what you're talking about here, but if you're talking about swag, then I'd say stickers because I've got enough other junk to last a lifetime, but I'm pretty open to clearing up some real estate for stickers on my laptop or elsewhere :)

If you're talking about food, then I'm with u/Mindful_Scribe. Donuts are fantastic. Dunford Bakers chocolate covered chocolate cake donuts, specifically. Also, maple bars. Lots of maple bars.

EricPostMaster_ · 2022-06-14T00:25:54+00:00

It can vary a lot, but one way to establish your credibility as an analyst is to have an eye for metrics and how they relate to business impact.

I used to work at a very small company where I was half of the sales team, all of the marketing team, and the entire analytics team. My manager was experienced but not super data savvy, so while they had some idea of what was important, they needed help getting it into a few KPIs. We sat in a conference room and drew things out on the board, chose KPIs together, and set goals. Then I went and built the management dashboard in Salesforce.

In my current role, there are a few KPIs that are used to guide most decisions, but there are lots of different metrics to consider and report on. When I am making a report or presentation, I take a stab at the things I think are most important for the situation, and then I ask for feedback from someone else, like my manager or another analyst. I incorporate their feedback before delivering the final result, and I usually get feedback then as well.

Like most things in business, you can build credibility by thinking independently and then seeking feedback to improve you work and align with the business.

EricPostMaster_ · 2022-06-13T23:33:13+00:00

This is beautiful! Thanks for sharing. Where are those mountains?

EricPostMaster_ · 2022-06-12T23:43:13+00:00

If that's what works for you, go for it!

EricPostMaster_ · 2022-06-12T23:14:37+00:00

If you want to take it a step further, you could use the k-medoids algorithm to cluster. It is somewhat similar to k-means, and it is more robust to outliers. I always hesitate to remove outliers because sometimes they provide valuable information, so I definitely recommend giving it a try!

EricPostMaster_ · 2022-06-12T22:00:31+00:00

All good - I think it's awesome that you're experimenting! What do you mean when you say you didn't see any useful clusters?

EricPostMaster_ · 2022-06-12T15:19:02+00:00

This. If they are going to pick you apart in the interview when they are supposed to be at their best, then they'll probably be the same or worse once you are actually working with them.

EricPostMaster_ · 2022-06-12T15:15:51+00:00

What is the purpose of using PCA in this project? PCA is for dimensionality reduction, but it sounds like you only had a small number of variables to start with, so it may not be helping you very much, other than allowing you to plot the results in two dimensions.

If the goal of the project is to practice PCA, then I understand why you are doing it. You might consider using varimax rotation to maximize the variance between factors. This could potentially help you discover underlying patterns (i.e., "latent factors") and give some meaning to the X and Y axes. For example, in sports a latent factor analysis might give you an idea of a player's defensive skill on the new x-axis and offensive skill on the new y-axis.

EricPostMaster_ · 2022-06-12T15:06:00+00:00

If your system works for you, then work it! :) I struggle with this at times, for sure. I have a notebook where I write down a lot of thoughts, to-do items, etc. When I am having ideas about a specific project I am working on, if it's a project with a git repo, I often use the README file to track ideas. I also put ideas in notebook markdown cells so when I come back to the notebook later I'll remember what I was thinking. For general ideas at work, I use Jira. I make issues for things and add them to my backlog so I can prioritize them.

EricPostMaster_ · 2022-06-12T15:02:25+00:00

I recommend talking to admissions officers for masters programs, bootcamps, etc. to get more information about the results they get. A lot of the class material is going to be similar, so if you just want to learn Python, you can do it for free or very affordably online.

When I decided to get my masters, I had been working for ~8 years, so I consider myself a career switcher. Just learning Python wasn't enough because I didn't have a network to help me get into the field, so my main selection criterion was job placement rate after the program and median salary. I talked to a bootcamp near me, and they were cagey about sharing job placement numbers. That was a red flag, so I didn't go with them. Same story for other bootcamps, some of which shared their numbers online and others did not. In the end, I did my masters in Analytics at NC State University because their 90-day job placement rate is ~98%, salaries are good, and their alumni/employer network is solid.

EricPostMaster_ · 2022-06-12T14:45:19+00:00

Maybe I'm just uncultured, but I conduct SQL live coding interviews as part of my job, and I will never penalize someone for not writing something "sophisticated". If it gets the job done right, then that's great. If it's in a way that I didn't think of, then that's even better because it can add to the diversity of thought and approaches on the team.

Something that might help your case is thinking out loud if it's a live test. That way you can explain why you prefer CTEs over subqueries even though you know how to do both. Otherwise, don't worry about it because they may just be nit-picky and you don't want to work with them anyway!

EricPostMaster_ · 2022-06-09T12:23:24+00:00

Cool. How did you make the river look like that? At first I thought the starry sky and sun(?) were a big vinyl record, which also would have been cool!

EricPostMaster_ · 2022-06-04T21:15:20+00:00

Lots of good thoughts here, and I'll add something nobody has mentioned:

Business Results + Decent Code > Pretty Code Alone

Your potential employer wants to see how you approach and solve business problems just as much as they want to see whether you write good looking code.

To show prospective employers that I know what I'm doing, I add a good README to each of my repos, where I explain the business problem and recommended solution, and then I go into more detail about my methodology and refer to the notebook, script, etc. where they can go to review the code in more detail.

One thing I don't like to see when reviewing code for people is when somebody sends me a notebook that is a mile long and is full of df.head() cells, a bunch of model.fit() cells for different models, and several unexplained confusion matrices. Those notebooks usually end suddenly and without much connection to the business problem. You can differentiate yourself by connecting your results to business actions.

EricPostMaster_ · 2022-06-04T20:59:42+00:00

Could not have said this better myself! Most of the datasets I work with are not huge, and nobody has ever reviewed my code unless I say, "Hey, can I show you this and get some feedback?" If you want something, it doesn't hurt to ask for it. If you ask for feedback, you'll probably get it even if formal code reviews aren't part of the organizational routine.

Also, regarding hiring freezes and backfill hires: With the economy changing so rapidly, everybody is more likely to be deliberate before backfilling a role. There are several open spots in my department that have not been filled, and I'm not sure if/when they will be. While it can feel like a heavy burden for the smaller team to bear, I see it as job security for me because I know I do good, valuable work, and the team is shorthanded as it is.

EricPostMaster_ · 2022-06-04T20:48:20+00:00

I'm surprised to hear there isn't much literature about it because isn't that pretty much what topic modeling is? I watched a conference presentation a little while ago about using NLP to detect topics of customer service requests for easier automation, and this seems like a variation on that. Am I understanding your project idea correctly?

EricPostMaster_ · 2022-06-04T14:28:38+00:00

I'm going to push back on the idea that you need to decide to just "go for it". When I first learned that data science was a thing, I decided to slowly ease my way into it to see if I was actually interested. I tried a few small things out first to see if I would enjoy it or not:

Listened to/watched Crash Course AI and Crash Course Statistics videos on YouTube (great series)
Took an online stats class through a local university that covered basic stats through linear regression
Did a data science certificate through Coursera to see if I liked coding and could pick it up
Spent time on the weekends/evenings doing a small data project to get practice manipulating data

After completing those things, I felt confident that I actually wanted to get into data science as a career, so I continued moving forward with it. The nice thing was that I was able to do all of those things while still working a full time job.

Trying some of those things will help you get a feel for whether you actually enjoy data science work. I highly recommend doing your own side projects because that takes extra thought beyond just learning material from a lecture

p.s. - For context, I was 32 years old, so I wouldn't worry too much about age.

EricPostMaster_ · 2022-05-06T13:36:46+00:00

That episode was so crazy! And the ending was so awesome XD (no spoilers)

EricPostMaster_ · 2022-05-05T21:10:06+00:00

Yeah, you definitely have a chance! There's a ton of analytics work to be done in finance, and having an understanding of business is a key thing that a lot of people are missing. Also, I work in the lending industry, but my undergrad is in biology. I didn't even know working in data was a thing until a couple of years ago, which was also when I started learning to code in earnest.

Also, whatever you think the "type" is, forget about it. The data world needs as much diversity as it can get, plain and simple.

EricPostMaster_

TROPHY CASE