Update on Ember’s Data Science Journey – August 1st, 2018

One of the struggles for any well-employed person is to find time for further training and educating themselves. Thus I find that I make a lot of progress toward my data science learning goals on airplanes and from airports. I don’t travel a lot for work – maybe once a month this year or a bit less. But when I travel, the time in airports and on airplanes is some of that precious “me” time. Work generally does not expect me to be accomplishing much, and is slow enough that I’m not desperately logging into every WiFi I can find to get off one more email. There is no house to be cleaned or dinner to be cooked, or children or husband to play with or help or talk to.

While this may feel lonely to some, as an introvert, I can really be alone enough in the crowd to focus on things like working on learning more about data science.

Formal Learning Paths

In my quiet moments over the last few weeks, I have found myself stalking Master’s Degree Programs in Data Science. My parents were academics – college professors from the time I was 5 years old. I grew up on or near college campuses. The things that appeal to me about a master’s degree program are the measured approach with immovable deadlines (an advantage because I can’t find something more important to do in the moment) and the credential that comes at the end. I very much enjoyed college and have often thought that if I ever won the lottery, I would be a perpetual student – learning all kinds of things just for the sake of learning.

The biggest disadvantage of a Master’s Degree program for me is that it would cost a large amount of money, and given my already quite reasonable salary for my current expertise, likely would not lead to much of an overall increase in salary, even in the long run. I do feel I can learn what I need to in the field without a degree program, so it is hard to justify the cost financially.

I also worry that my mathematical background is not quite sufficient to undertake many data science degree programs. For my bachelor’s degree, the highest math class I took was “Calculus for Business and Social Science Majors”. I might have to augment my mathematical skills before moving forward with a Master’s Degree. The flip side of that is that maybe I really need that extra math.

Another obstacle is finding a Data Science Masters that I can really agree with all of the coursework for. I definitely want to focus more on Python than R. The Data Engineering programs often include a required course or two that I could arguably teach on relational databases. One program near me seems to use Java as a focus, which seems quite odd to me. Another has some really mixed reviews online that it focuses more on theory than practice. Local state colleges, which would be much cheaper have programs that either sound dated with a focus on “Big Data Analytics” using expensive corporate tools, or offer courses that are primarily computer science with a couple of statistics courses thrown in.

One of the reasons to engage in a Master’s Degree program is to have the guidance of what to study, but as a professional in a closely related field, I have preconceived notions and want a practical hands-on approach. I found this “MicroMasters” program that really appeals to me from MIT and edX. I like the math/statistics and python focus of it, and the much lower cost of it as compared to a full masters. It will let me get a credential for my resume while shoring up some of my weaker areas. It is missing a class on the visualization or visual display of data, but I can likely find a separate class to cover that more thoroughly. It has been a long time since I’ve done real college level math, so for the next month, I’m aiming to spend 1-2 hours every day brushing up on my pre-calculus and calculus skills.

Here’s the stack of books I’m using for that. I haven’t always been a fan of the “For Dummies” series, but I’m mostly reviewing concepts I understood 18 years ago, so they’re hitting me about right at the moment. I’m working through the pre-calculus and calculus workbooks, referring to the the non-workbook ones when I need more explanation of a concept. I’m also seeking out extra practice problems online in the areas where I feel I need a bit more practice. I’m only a couple of chapters into the pre-calculus workbook, so focusing mostly on recalling the concepts and practice of Algebra 2. Ah, quadratic equations, I have not missed you.

Informal Learning and Enthusiasm

One of the ways I am keeping myself enthusiastic about data science is by listening to more light-hearted, mainstream audio books and podcasts that are related. This month, that has taken the form of listening to the audio books Freakanomics, Think like a Freak, and Super Freakanomics. These really engage my curiosity and passion for the field, yet are light enough that I can listen to them while doing chores or driving. It is interesting to me how the authors speak of what I call Data Science as really a branch of economics. I never had thought of data science that way. I had always thought of economics as strictly focused on the economy of financial transactions, be it local or global. The econometrics that they discuss seems to be an odd marriage of what I would have thought of as economics and psychology. I also find some of the topics challenging the way I think about some things, which is a good thing overall.

In my offline time, I had been reading “Getting Started with Data Science: Making Sense of Data with Analytics” by Murtaza Haider. I was first introduced to some excerpts from this book in a course on CognitiveClass.ai – Introduction to Data Science. I was really just getting started with the book, but the premise seems to be that it’s data science from a very practical standpoint. I’ll be spending less time with it as I shore up my math skills and embark on the MicroMasters. If I weren’t focusing on the MicroMasters, this book would be a primary focus for me, along with the data science courses on cognitiveclass.ai.

I’m adding a tag on the blog for my data science journey, so if you want to follow along, keep any eye out for posts with the “Ember’s Data Science Journey” tag. I hope that my journey and progress helps others out there learning similar stuff and would love to hear any comments or suggestions in the comments section below!

Ember Crooks
Ember Crooks

Ember is always curious and thrives on change. She has built internationally recognized expertise in IBM Db2, spent a year working with high-volume MySQL, and is now learning Snowflake. Ember shares both posts about her core skill sets and her journey learning Snowflake.

Ember lives in Denver and work from home

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  1. “but as a professional in a closely related field”
    This is the first time I hear that a dB developer or dba is very close to data science field. Interesting!!!

    Would be interesting to know why you thing so.

      • SQL is consistently one of the top languages I see listed in the languages that data scientists use.
      • We’re used to thinking about data in sets (tables, views, etc) instead of individual values or files.
      • We are used to the security and performance concerns that go along with handling data.
      • Concepts like data integrity can be very close to the idea of data cleansing.
      • “Data Science” is often actually a collection of different roles, and the data engineering role in data science has a lot in common with a DBA – how to store data that is being manipulated for data science in a consistent, secure, high-performing, and reusable way.

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