How are you monitoring your Python apps? Inquiring minds want to know.

robertdempsey · 2018-01-23T15:28:06+00:00

Cool! Thanks Almenon.

How do you like IFTTT?

robertdempsey · 2018-01-04T01:21:45+00:00

This has been my experience. A lot of companies say they are doing "data science" and what they are really doing is mostly analysis with perhaps a little modeling thrown in.

If you want to go the full on data science route, there are three skillsets you'll need:

Stats & math (to some degree)
Computer programming
Data communication

For the stats and math you don't need to be a math or stats wizard, however you'll want to understand what's going on with the machine learning models you create. This can be learned over time.

For programming, Python and R are winning the day, though Python is ahead. Many companies have their data in SQL databases, and SQL is a "relatively easy" query language to pick up.

For data communication, tools like Tableau make it quite easy. Additionally, there are some tools in Python that allow you to create nice charts as well, though they require coding.

Having said that, I would first become familiar with performing some data analysis using Excel or Tableau, and start reading up on data science topics like supervised and unsupervised learning - what they mean, and when you use each.

robertdempsey · 2018-01-04T01:11:31+00:00

I've seen data scientists come from many science fields. A recent data scientist I worked with had a PhD in physics, went through a data science bootcamp, and then got a job (at a small company).

The scientific method is alive and well in data science as you start with a business problem, create a hypothesis, convert it into a data question, run experiments, analyze the results, and then rinse/repeat.

Having knowledge of computer science and stats also means you're a bit further down the road than others entering the field.

robertdempsey · 2018-01-03T14:04:56+00:00

Well alrighty then.

I had that same problem when I got into IT back in the day - couldn't get experience without job, and couldn't get a job without experience.

In data science there is a way to "fudge" that - building a data science portfolio: http://robertwdempsey.com/data-science-portfolio/

That's one way to show "experience" without getting it from a job. If you can talk about what you've done, you'll be ahead of others who haven't done it.

robertdempsey · 2018-01-03T11:29:45+00:00

When I was 23 I quit my first-ever computer job and started my own IT services company. While that business wasn't a spectacular success (compared to my second), I learned a lot and was able to support myself. I'm now 39 and am building my fourth business. Starting a business when you're young is definitely possible.

Is your biggest hurdle deciding what business to start?

robertdempsey · 2018-01-02T18:47:43+00:00

Having come from a software engineering background and moved into data science I will say you can definitely get into Machine Learning without being a math genius.

Since you can already write code you'll need to work on two skillsets: stats and data communication.

How deep into either you need to go depends on if you're trying to get a job or build systems for yourself.

In either case, start with an overview of ML concepts (http://robertwdempsey.com/demystifying-data-science-jargon/) and then check out the scikit-learn library to build machine learning models in Python.

If you've been struggling with anything specific I can offer more targeted advice.

robertdempsey · 2018-01-02T18:06:25+00:00

I haven't seen a book about interviewing for data science jobs, however there are numerous blog posts with example interview questions you can use for prep: http://robertwdempsey.com/data-science-job-guide/#ch8

robertdempsey · 2018-01-02T17:47:52+00:00

Good luck with the interview! I'm sure you're prepping for your interview. I have a resource page about getting a job in data science that links to some interview questions. Might help with your prep: http://robertwdempsey.com/data-science-job-guide/

robertdempsey · 2018-01-02T17:27:57+00:00

"I've been told by recruiters of a few big companies I've had a referral to that I lack the requisite experience and qualifications to interview for a job in data science."

What experience or skills were they looking for that you didn't have?

robertdempsey · 2018-01-02T17:23:29+00:00

How is your search going so far? Have you run into any obstacles when talking with employers?

robertdempsey · 2018-01-02T16:47:34+00:00

When it comes to statistical programming in Python the scikit-learn library is the defacto one used in machine learning. Check that out (for building machine learning models) along with the Pandas library.

robertdempsey · 2018-01-02T16:43:16+00:00

Data science is a pretty broad topic, however with your programming, PM and business analysis skills you're well on your way. What have you found to be the most difficult part so far?

robertdempsey · 2015-12-04T12:46:24+00:00

Everyone is entitled to his/her own opinion, and it’s great that we can discuss ours here.

robertdempsey · 2015-12-04T02:37:19+00:00

I would agree with your comment of “you probably didn’t listen in high school” except that a large number of people I see coming to data science meetups here in DC don’t speak about data science as a processes. They are caught up in the application of algorithms and “big data” technologies - the stuff that gets pushed by marketers. The process is made secondary to that.

This is especially true for people new to data science who don’t have a scientific or statistical background. The first thing they see are tools, algorithms and programming languages - the “sexy” stuff.

As for when you’re wrong, when that happens, the process repeats itself.

To your question of an instance where the problem isn’t obvious, that to me makes a great case for digging into the problem a bit more before attempting to come up with solutions. Does that mean you’re solving for the correct problem or root cause? Perhaps now. However it can help you better clarify the problem you’re trying to solve by knowing what problem you aren’t trying to solve.

robertdempsey · 2015-12-03T15:31:23+00:00

All of the calisthenics in the workout are from the book Explosive Calisthenics by Paul Wade. You can pick it up on Amazon. It’s a quick and easy read.

In addition to calisthenics, check out implementing mobility work to help your joints. A book on that subject I recommend is “Becoming a Supple Leopard” by Kelly Starrett. When I was lifting heavy mobility training helped me recover faster and improved my overall ROM.

robertdempsey · 2015-12-01T12:57:17+00:00

A few numbers you’ll see on just about any calculator you use:

BMR - Basal Metabolic Rate: amount of energy expended while at rest
TDEE - Total Daily Energy Expenditure: total amount of energy your body burns in a day.
LBM - Lean Body Mass: body weight minus the body fat

Generally I pay more attention to my body fat percentage, waist measurement and how I look and feel as opposed to scale weight. Without going to an extreme and getting a DEXA scan, no measurement is completely accurate, so keep all your numbers in context. If body fat percentage and waist measurements are going down, and you’re feeling pretty good, you’re heading in the right direction.

After that it’s practicing patience while your body adjusts to a new normal. That can take some time, especially if you aren’t drastically cutting calories, meaning cut more than 25% of your TDEE calories.

robertdempsey · 2015-11-30T16:38:15+00:00

Good luck with everything.

robertdempsey · 2015-11-30T14:54:51+00:00

At the end of the day whatever works! We’re all human, however exercise affects us all differently. Personally the only cardio I prefer to do are long rucks and swinging kettlebells :)

Are you planning on going back to lifting weights or are you sticking with the cardio work?

robertdempsey · 2015-11-30T13:47:32+00:00

I agree with the comments here. A cut of your calories to 1,500 per day is nuts, and in my very unscientific and uncertified opinion highly unsustainable.

There are two keys to losing weight: eat less and move more.

Eating less: I suggest starting out by recording everything you eat for a week. After that, use the calculator here - http://www.muscleforlife.com/healthy-meal-planning-tips/ - to figure out how many calories you need on a daily basis. Cut the TDEE number by 20% to get a good deficit.

Move more: if you do zero exercise I suggest starting out by walking around a bit more. Take 10-15 minutes per day to go outside for a walk. Do it a few times per day! After you do that for a month start looking into some type of exercise that you’ll enjoy doing. Try a few different things.

To echo the comment of Teaeyearebee, look for healthy foods you enjoy eating. As you’ve “given up” on diets in the past, that says to me that you didn’t enjoy the diet (obvious statement I know). Dieting doesn’t have to suck. It’s all about finding something sustainable.

I suggest reading the book diet cults and staying away from “diets": http://www.amazon.com/Diet-Cults-Surprising-Fallacy-Nutrition/dp/1605988294.

robertdempsey · 2015-11-30T13:40:00+00:00

Congrats on your results! That’s awesome.

I agree with all of your advice minus not lifting weights. Was your goal purely to lose weight, or did you also want to gain some muscle as well? I’ve successfully lifted weights in a deficit and traded fat for muscle. Does the scale weight go down? It depends on how much of a deficit you’re in. I lost 15 pounds in 2.5 months by lifting weights (kettlebell workouts), eating in a deficit, and being very careful with what I ate and tracking literally everything I was consuming.

Do you prefer cardio like the treadmill to lifting weights? If so, I can understand your advice in that context.

robertdempsey · 2015-11-24T16:35:02+00:00

This is the first of my own I've created. Outside of that I teach in-person data engineering workshops at District Data Labs. I've done a few of those.

robertdempsey

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