Zipfian on Nyghtowl

Delayed Zipfian Week 12 Wrap-up

Sun, 27 Apr 2014 17:10:44 +0000

A little delayed for good reason but here we go. Zipfian’s 12th week focused on interview practice, a conference and the graduation celebration.

Interview Practice

Most of the 12th week was spent going back through previous materials and drilling/simulating interviews. Data science has a different interview flavor from software engineering. Companies vary in what they are looking for and what they will ask and the content for data science covers a broader range of topics.

Zipfian Hiring / Demo Day – How it Works

Sun, 06 Apr 2014 16:14:46 +0000

This past week was the big hiring/demo day for Zipfian Academy where we presented our projects to a number of companies seeking data scientists. Zipfian’s hiring day is a lot like Hackbright’s career day. So the nice thing about going through it was that I knew what to expect.

Companies:

Sixteen companies attended hiring day which was a great turnout, and I was thankful there wasn’t another 9 because talking to 16 was still exhausting. Some companies were hiring their first data scientist who would build the strategic direction as well as put it into action. While other companies have teams that they want to grow. So there was a mix of start-ups to large organizations.

Jeeves is Talking!

Sun, 30 Mar 2014 14:57:47 +0000

Coolest moment this week was when I figured out that I just needed to add one line of code to my program to get my computer to talk in my Jeeves project (thus video above).

This past week was all about fixing stuff, fine tuning, iterating and putting together a presentation and peripheral stuff for demo day which will be Thurs. Of course there is always more I could and would do, but I’ve been continually shifting priorities based on time and end goal.

Zipfian Project Week 1 & Closing the Loop

Sun, 23 Mar 2014 21:15:18 +0000

One week down for our final projects and one week left to go. This has definitely been the most fun and rewarding weeks so far because I’ve been connecting the dots in more ways than one.

Everyone talks about getting your minimum viable product done (mvp) when we do final projects like this. Thankfully I had enough experience with Hackbright to know what that meant as well as how to approach it. I got my full product pipeline built between last Sat and yesterday. I’ve tested that it works (well pseudo works) and feel really good to see the emails get pulled in, analyzed and if the condition is true then I get a text on my phone. Now I really need to fix my actual classifier because it is just not that accurate.

Begin with the End

Sun, 16 Mar 2014 22:18:20 +0000

So we are officially kicking-off personal projects for the next 2 weeks. It’s been a bit of a process for all the students figuring out what each of us wanted to do and finalizing it but we got there.

Last WeekIt was a review week. We went back over some concepts that will be valuable for projects as well as when we go through interviews like classifiers and distributions. We also worked to determine and finalize our projects and then we spent two full days on final assessments. We had 2 different data science case studies that required working through the full process from getting the data to providing some type of recommendation/report. We also worked in teams on sample interview questions to review over additional class content. The last day there was a little bit of mutiny and we really didn’t get much done on the assessment. Most people were starting to think projects at that point.

Quick Update on Zipfian and Week 7

Sat, 08 Mar 2014 19:32:11 +0000

Another short one on what was covered this week at Zipfian. Also, for anyone interested, below is a photo of the classroom to give context on where we are working from.

Its a good space. Far in the front is the work and lecture space and the area closest to the camera is more where we take breaks and eat.

This week was about diving into complex machine learning algorithms. Topics we covered:

Half-way Mark – Numb Brain

Sat, 01 Mar 2014 15:02:59 +0000

At the pace we were going, the class showed signs of weariness in week 5 and officially hit the wall this past week. The instructors eased up on us in exercises this week, and gave us a pseudo free day to recharge yesterday.

The week was about assessing where we were and covering MapReduce, Big Data, Flask and to think about projects. It may not sound like they eased up on us but they did.

One third of the way..

Sat, 15 Feb 2014 08:09:00 +0000

I’m keeping this a bit brief because there is a lot to do. Good week and busy as usual. Lots of Naive Bayes. Best part was my team won the simulated Kaggle competition about Stumbleupon.

Below is a summarization of concepts and tools covered this week.

Web Scraping
ReST APIs
Tolkenization
Natural Language Processing (NLP)
Vectorization (Count, Tf-idf)
Ngrams
Feature Engineering
Classification – Naive Bayes (Mulitnomial, Bernoulli, Gaussian)
Confusion Matrix
ROC Plots & Area Under the Curve
Deep Learning

New Tools:

Machine Learning Starts with Linear Regression

Sat, 08 Feb 2014 10:27:42 +0000

We wrapped up our statistic deep dive on Monday with an exercise around Multi-Armed Bandit (MAB) and focused the rest of the week on regression.

Main Topics Covered in Class:

Multi-Armed Bandit
Linear Regression
Gradient Descent
Cross Validation
Final Project Overview
Kaggle Competition

We also added in using the scikit learn data package which is primarily used for machine learning algorithms.

MAB / More Stats

MAB was actually interesting to learn about because its goal is to address some of the shortfalls in AB testing. For example, AB testing only compares two options at once and there is potential for bias when showing an old version against a new version. MAB allows testing multiple options at the same time while generating and updating performance scores. There are a couple different algorithm variations in MAB, but it basically is about showing the best performing option most of the time (ex. 90%) and providing some amount of randomization to show a lower performing option to give other options the opportunity to increase in performance (e.g. popularity). How often you randomly show an option can impact how long it takes the performance to change. The MAB algorithms typically beat out AB in picking the best option to use with the lowest error. This article gives some insight into MAB but beware that the code in the article is a little wonky.

Oh Math

Fri, 31 Jan 2014 23:54:23 +0000

This week was all about statistics and learning more python packages. It was a tough week and we covered the topic that intimidated me the most, the math.

StatsI actually grew up loving math and I know I can understand it with enough focus and time spent studying. Still it was a lot of grad level content that we pretty much squeezed into a few days. There is no way to fully learn all the concepts in a week and that is a common theme throughout this class (and probably most bootcamps). Additionally, several people in the class have PhDs in STEM (science, tech, engineering & math) and understand the math at a whole other level. It is definitely helpful to have students to learn from while also making it hard to keep up in the exercises at times.

Zipfian First Week Rundown

Sun, 26 Jan 2014 09:32:49 +0000

First week of Zipfian is already done and it does remind me how during Hackbright it felt like it went so fast. The focus for the week was about exposing us to core tools we will use as well as the main activities/processes around working with data.

Week Summary

The main tools used this week were Python, iPython, Git and Bash, and we went through three different exercises where we were gathering, cleaning, exploring and sometimes reporting data. A large part of our exercises throughout the program will be done in Python and we spent 4 of the 5 days using it. This is a bit of a shift for the school because they split more time with R in the last session, and it has to do with the growing popularity of using Python for data science. There’s a great article I read recently on the subject at R-bloggers. We will still use R but the emphasis is more Python.

Try: Data Science Except: Monty’s Bayes Example

Mon, 20 Jan 2014 21:45:19 +0000

Great first day at Zipfian. Definitely a different experience starting from Hackbright but some similarities. Granted there are the obvious differences of the content focus on data science vs. web application development as well as 20% women in the class vs. 100%. Plus I’m not the oldest or the youngest of the group. We have a really nice mix of people from various parts of the country and a myriad of backgrounds. Though there is a lot PhDs and/or engineering backgrounds. It was a much quieter energy to the start of the class even though you could tell there was some nervousness.

Wrap-up & Kick-off in One

Sat, 18 Jan 2014 18:14:01 +0000

It’s been over 8 months since I graduated Hackbright (HB). It also has been a while since my last post, but the last one was a bit difficult to follow. Plus, things have been …. busy.

Tips on Life After Bootcamp:
It has been an interesting several months since I started this journey and a good portion of that being what took place after HB. I know I’ve talked about this a little before but I’ve watched 2 classes since mine go through the HB withdrawal as well as met many from other bootcamps who have struggled with the “what’s next”.