Blake Anderson

[blake-anderson]

Coming soon

[blake-anderson]

Inspiration Point / Zion NP / September 2018

[blake-anderson]

Happy Camper / Bear Valley CA / August 2018

[blake-anderson]

It was a HARD summer / LA / August 2018

[blake-anderson]

Udacity Data Analysis Nanodegree Review

I’ve been really interested in improving my data skills, so when I came across the Data Analyst Nanodegree program from Udacity in December I thought I’d give it a shot. I really had fun with it. Here’s my recap of the 6 month (Term 1 & Term 2) Data Analyst Nanodegree course:

## Term 1 Term 1 is all about learning Python and it’s powerful data libraries, pandas and MatPlotLib. There’s also some SQL sprinkled in and a nice dose of statistics, but the focus is on the data analysis process with Python. This term was broken down into three parts: an introduction to Python, Data Analysis, and Statistics.

The first section, an introduction to python, consisted of learning the basics of Python using Jupiter notebooks. All of the essentials were covered: strings, functions, modules, arithmetic.  If you’re familiar with computer programming you’ll breeze through this section. The project was a simple data analysis on bike sharing using Python. Most of the project implementation details were scaffolded out, so it’s left up to the student to fill in the details. Overall, it was a nice introduction to start.

The second section was focused on learning the basics of SQL and investigating datasets using Python. I had a pretty good grasp on SQL before this course, so I didn’t spend very much time on this portion. But I should mention they did a great job covering all of the necessary commands and clearly explained some of the trickier bits, like joins and window functions. This section also went in depth on how to analyze data using Python. Understanding how to clean data with Pandas and plot it with MatPlotLib is the cornerstone of this nanodegree program, nearly everything in Term 2 is built off the foundational knowledge learned here, so don’t skip it if you’re going to be moving on. The project in this section was one of my favorites of the course: an investigation into datasets of your choosing. I chose to look at the effects of government health care spending on life expectancy and colon cancer.

The last section covered statistics. In it, we covered all the good stuff: regressions, confidence intervals, hypothesis testing, bootstrapping, and Bayes. I found the content to be well taught and thorough, but my biggest complaint about this section was it’s format and pacing.  The format of some of the videos and quizzes were noticeably different from everything else. Videos were very, very short which made it more difficult to let the topic flow, and many of the quizzes were unnecessary in the early setup of the problems. Even with its flaws, this was unquestionably one of the more useful sections of the nanodegree.

## Term 2 Term 2 consisted of Exploratory Data Analysis using R,  Data Wrangling, and Data Story Telling using Tableau. Upon completion of these three sections, you’re awarded the coveted nanodegree

Exploratory Data Analysis using R was the first section up in Term 2. It started off by going through the fundamentals of R, which aren’t that different from a programming language like Python, and learning about R Studio, the environment for development. This was my first exposure to R, and I thought it was fairly easy to pick up. There are a lot of similarities in syntax when analyzing/cleaning data between R and Python+Pandas. I found the main advantage of using R was the graphing tools. I was particularly impressed by the simplicity and power of `ggplot`. I found this section interesting, but not incredibly useful as it was essentially a repeat of section 2 of Term 1 except it used a different programming language (one that is declining in usage).

The second section of Term 2 focused on Data Wrangling/Cleaning. Nearly all of this was covered in section 2 of Term 1. The project required gathering, assessing, and cleaning a user’s twitter dataset. Really, nothing new here.

The last section of Term 2 was about Data Story Telling by using Tableau. I was really excited about this one but ended up pretty disappointed. I’d heard about Tableau many times when reading or discussing data analysis but had never had the chance to check it out. Its drag-and-drop interface was extremely easy to use and I really think this application could open up data analysis to more people, but I found this to be my biggest problem with it: this taught an application user interface instead of continuing, or reinforcing, data analysis knowledge that goes beyond an easy-to-use interface. I personally would have loved to see this section replaced by a more difficult SQL section or more advanced plotting with Python.

## Additional Notes * Udacity does provide nanodegree students with a “mentor.” I didn’t have any real need to use it, but I appreciated knowing that if I did get stuck there was someone I could talk to directly. * Term 1 would be really difficult for anyone that hasn’t done any computer programming. I’d highly recommend a foundations course before starting the nanodegree if you haven’t programmed before. * The timelines and due dates were generous. I rarely felt rushed and was able to accomplish nearly all of the course just on weekends. * Jupyter notebooks are incredible. This was my first time using them and dang do they make learning programming SO nice.

## My final advice: It’s worth it. Term 1 has better material, Term 2 has the certificate. If you’re exclusively looking to improve your data analysis skills, then only taking term 1 is sufficient. If you’re looking to improve your resume and go into the Data Analysis profession, then it’s hard to turn down Term 2 and the nanodegree certificate.

[blake-anderson]

New Grad / Data Analyst / SF CA / June 2018

[blake-anderson]

10 years and nothing’s changed / SF CA / June 2018

[blake-anderson]

Fam / Florida / May 2018

[blake-anderson]

It never gets old / Marin Headlands CA / April 2018

[blake-anderson]

Mornings in March / SF CA / March 2018

[blake-anderson]

If not now when? / Tulum Mexico / Feb 2018

[blake-anderson]

1 week in Boulder / Trail running and beers / Boulder CO / Feb 2018

[blake-anderson]

The Moth Live / Castro theater / San Francisco/ Jan 2018

[blake-anderson]

Trail Running & My Favorite Animals (by my incredibly talented friend, Monica) / San Francisco / Jan 2018

[blake-anderson]

Goals for 2018

📚 Read 30 books

🏃‍♂️ Set a Marathon PR

🏃‍♂️ Run another 50 miler

🧘‍ Meditate weekly

[blake-anderson]

2017

As I lean back in my slightly broken chair with a slightly broken smile on my face in a dimly lit coffee shop (who also graciously serves craft beer), I look across the table to a perfectly poured Fieldwork IPA. I lean in for a quick sip of the hazy beer. I reflect on the year 2017. It has a little bite, it’s taken it’s time, it’s refined, not a reinvention, but it’s better, the finish is strong and smooth, and overall, I like it.

Let’s revisit my new year’s resolutions for 2017:

Journal weekly

Ready 36 books

Run a 50 miler

/I take another drink/

Journaling - What a great idea this was! *high 5s December 2016 self* I threw down 75 journal entries this year. I really came on strong towards the end of the year. It’s started to be part of my morning routine and I love it. It’s helped clear my mind, given me a space for self-reflection, and has undoubtedly aided in personal growth. Much more journaling (and writing) to come in 2018.

36 books in a year - is that even possible?! Well, it wasn’t this year! I finished a solid 9 books short of my goal (if I’d only read “Deep Work” earlier in the year!).

Here’s what I read:

The Power - Naomi Alderman

Principles - Ray Darío

Learned Optimism - Martin Seligman

The Coaching Habit - Michael Bungay Stanier

The 48 Laws of Power - Robert Greene

Siddhartha - Herman Hesse

Quiet Leadership - David Rock

The Manager’s Path - Camille Fournier

Eleven Rings - Phil Jackson

The Vanishing American Adult - Ben Sasse

Homage to Catalonia - George Orwell

The Product Book - Product School

Slaughterhouse-Five - Kurt Vonnegut

Deep Work - Cal Newport

Nudge - Richard Thaler and Cass Sunstein

Difficult Conversations - Douglas Stone, Bruce Patton, Sheila Henn, and Roger Fisher

The Tiger: A True Story of Vengeance and Survival - John Vailant

The Score Takes Care of Itself - Bill Walsh

Tribe: On Homecoming and Belonging - Sebastian Junger

The Obstacle is the Way - Ryan Holiday

Nothing is True and Everything is Possible - Peter Pomerantsev

Blood Meridian - Cormac Mccarthy

Bird by Bird - Anne Lamott 

Gratitude - Oliver Sacks

The Sixth Extinction - Elizabeth Kolbert

Eating Animals - Jonathan Safari Foer

United - Cory Booker

And here are my 5 favorite from the bunch (in no particular order):

Homage to Catalonia - George Orwell literally fights for what he believes in and shows us a unique side of war (that most would consider boring, but I found endlessly fascinating).

Deep Work - I mean, after reading this book I’ll never work the same.

The Score Takes Care of Itself - A masterclass on developing and implementing a leadership philosophy.

Quiet Leadership - The best book I’ve read on effectively leading, coaching, and improving individuals.

The Power - An absolute page-turner that reconstructs theology and sexuality in the modern age in a unique and entertaining way: giving women the ability to electrocute on demand.

Run a 50 miler - My proudest moment of the year was crossing the finish line at the North Face 50 mile race. Preparing for this distance required a level of dedication and mental toughness that I’d never experienced, and the journey was 100% worth it. I might be the most naive person this side of the coffee shop’s register, but completing this race makes me believe that I can do nearly anything.

/It goes down easy. It’s finished. Cheers, 2017/

[blake-anderson]

California Christmas / Orange County CA / Dec 2017