Udacity CS101: Intro to Computer Science, a review

I’ve been trying to learn how to code in Python for a while now. Of all the beginner resources I’ve tried, Udacity’s Intro to Computer Science (UD CS101) has been my favorite.

To clarify: I’m not learning Python with the intention of becoming a software developer. Rather, I like analyzing data, and I hear Python can help with that. R too, but Python is 1: recommended for beginners, and 2: has more applications outside of big data.

I do have some programming experience, though never anything formal, never to this depth, and never in Python.

 

THE STRUCTURE

UD CS101’s premise is for you to create “The Next Google” by teaching you how to build your own search engine.

The self-paced course is broken down into 7 modules*. Each module introduces a new concept to help improve on your search engine.

Each module contains:

  • Videos. Here the instructor explains the theory behind the concepts and demonstrates how to use them on the search engine.
  • Q&As. These help nail down the concepts. These aren’t too difficult and are usually similar to the demonstrations.
  • Problem sets. These are machine problems that build on the concepts you’ve learned so far and are more challenging than the Q&As.

At the end of the course you would have built a search engine with a similar algorithm to AltaVista–what was once the #1 search engine in the 90s before Google took over.

For your class project you then build a mini social network based on the concepts you learned from the course.

*As of writing Udacity has revamped their classrooms so this modular approach may no longer apply.

 

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On the bookshelves this second week of November

Once a week I make a trip to the local bookstore.

I never go there to buy anything. Sometimes I leave my wallet behind, so I can avoid making an impulse buy. I just browse, browse, and browse the aisles.

Sometimes I talk to the sales staff, and ask if they’re carrying a new book I’ve heard about. Or if they have the so-and-so book of an author or series they’re already carrying. Or to ask why they decided to change their display this month.

It hurts a bit to read about Neil Gaiman’s librarians as I don’t have those. I don’t have any book gurus to recommend me books and encourage my reading. The bookstore sales staff are probably the closest I can get. Public libraries are hard to come by where I live, and even the few ones that exist are in a pretty dismal state.

But I’m happy with what I have. The bookstore to me is what the library must have been to the young Neil: A solace. A happy place. I never schedule my trips there, and yet I always end up going mid-week, either on a Tuesday or Wednesday. Always right when I feel like the corporate world is going to gobble me up then spit me out. It’s my hump day reward.

It must be what retail therapy is like, except I don’t have to spend for anything.

When I was making my rounds last week, I thought it’d be interesting to share the books that caught my eye. If others manage to discover a great book through my book haunts then I’d be so happy to be of service! The books below are my finds, along with what it was about them that sparked my interest:

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Microsoft DAT206x: Analyzing and Visualizing Data with Excel Review

You never actually analyze and visualize data, but this course is worth taking as it’s a good introduction to using Power Pivot and Power Query–both of which are useful for managing large amounts of data in Excel. Just make sure you manage your expectations.

Update: To follow my progress in this program, check the Microsoft Professional Program tag.

 

Context

For those who are following this blog for my data science updates, it might be of interest to you that I am still working on Microsoft’s Professional Program for Data Science  (on beta). I have recently completed my second course, Analyzing and Visualizing Data with Excel.

This was my gateway course to the program. Excel enthusiasts at work had recommended it as a good introduction to PowerPivot, and it was only later that I found out the course was part of a larger data science program.

My primary purpose for taking the course was increasing my proficiency in Excel. I currently manage a large-scale project with an equally large-scale tracking spreadsheet. The spreadsheet easily gets out of hand due to the sheer number of assets involved and because it pulls data regularly from multiple data sources. I was hoping the course would help me clean up the data and make it sustainable to maintain in the long run.

Because of this, I’m reviewing the course from a more practical Can I use this at work? perspective rather than its relation (or lack of) to data science.

It took me about a month to complete, starting September 2016. You can follow my progress in the MS Data Science Program by using my tag Microsoft Professional Program.

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Microsoft DAT101x Data Science Orientation Review

At $25 (beta price), this orientation course is overpriced for what it offers.

Update: To follow my progress in this program, check the Microsoft Professional Program tag.

 

I’ve mentioned in my Getting Started with Data Science tips that I’m currently taking the Microsoft Professional Program for Data Science.

The program is still in beta, so:

1. Microsoft needs the feedback, and

2. Potential students would want to know if the program will be worth their time, money, and effort.

The program is pretty extensive, so I thought it best to break my reviews by course as I take them. This review is on the orientation course, DAT101x Data Science Orientation.

For context, I took the course around late September 2016, and got my certificate early October.

 

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