Galvanize Data Science Prep, pt 2

Hey, so guess who forgot to write an update last week? The course has been great, I highly recommend it. Part 2 was released yesterday (Wednesday, the course concludes Friday). [edit: and an update to Part 1, see the list below] Pt 2 is intended as a primer for students who are enrolling into the data science intensive, and looks like it expands on the topics covered in the prep course. The instructor won’t be available after Friday, but the slack team will still be active, and the other students have been helpful whenever there’s a question posted. Here’s what’s additionally on offer:

  • Git Primer
    • Primer, Concepts, submitting work to the instructor
  • In-terminal Editors
    • Vim
    • Nano
  • Object Oriented Programming
    • Terminology
    • Classes
    • Initialization and ‘self’
    • Methods
    • Magic Methods
  • Pandas
    • Pandas Series
    • Panda DataFrames
    • Merging DataFrames
    • Split, Apply, Combine data
    • Visualize Data with Pandas
    • File I/O
    • intro to Exploratory Data Analysis
  • Interpret functions as integrals and derivatives
    • Mathematical Limits
    • Derivatives and rates of change
    • Integrals of functions
    • Connection between derivatives and integrals
  • Linear Algebra 1
    • Matrix Inversion
    • Systems of Linear Equations
    • Vector Similarity
  • Linear Algebra 2
    • Linear Algebra from a Geometric Perspective
    • Linear Transformations Overview
    • Rotations
    • Changing Dimensions
    • Eigenvectors and Eigenvalues
  • Statistics and Probability
    • Random Variables
    • Distributions
    • Estimation
  • Back in Part 1 SQL was added some time this week
    • Database Structure
    • Populating a database
    • Writing simple queries
    • Writing aggregate queries
    • Joining tables
    • SQL style conventions

So wow, that’s a lot of ground to cover. It looks like a really good expansion on the topics in Part 1, and I’m looking forward to going through that material.

The modules I outlined in my previous post have been generally good. There are some aspects where there isn’t a clear line between A and B. In a lot of ways that mimics my experience in engineering school; a primary difference being that I now have the web to look for explanations of things that aren’t making sense. Back then I could reread the textbook, review my crummy notes or try the math tutoring center (if it was open and if I could get over my anxiety about asking for help). I’m much better about using the resources that are available to me.

Now I have a plethora of examples to look for one that makes sense to my brain about how a particular math operation should work. There are some aspects of using NumPy that simply need practice and repetition, but fortunately I’m comfortable and experienced with googling my code problems.

So far there’s only been one challenge (end of module) problem that I’ve called shenanigans on. It required using a technique that was discussed and we were given two basic practice problems to see how it works. The question in question required recalling this technique several modules later, then applying said technique to a new method that behaves very differently from anything we’d encountered previously. I was able to figure it out, but it felt like the learning was less about how to use the method and more about deep-diving on problem solving. This question was either a success or not, depending on their intended outcomes.

Having the instructor has been really helpful. I’m new-ish to Python and how the syntax parses. Having someone to pair with to review my code and tidy it up was fantastic.

I’m looking forward to finishing this first probability module today. As I’m able to continue with the new material, I’ll make some new posts to compare these “deep dives” with the modules in the first section.

Galvanize Data Science Prep, pt 2

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