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Google Data Anlysis Course - Introduction

March 15, 2022

alt text I recently started the Google Data Analysis Course on Coursera. I’d been struggling through the 365 Data Science platfrom which, while being a great source of material, encompassing the full range of the data science field, didn’t fit my learning style. Also, their content is somewhat uneven, with a cumbersome (and time-consuming) process of downloading exercise files, and uneven PDF availability. We’ll assume these are teething problems, becuase in other ways 365 is a very impressive platform (especially for the price).

However, I struggled with the mathematics and the statistics, neither of which I had any background in, and 365 is not ideal for starting from scratch with either. Even after I tried a couple of Kaggle competitions, I struggled to even understood how to properly process the data. So I thought I should try beginning at the beginning, with data analysis, and hopefully learn how to read and be able to properly visualize data and, eventually, ‘tell stories with data’.

I decided to try the Google Data Analysis course in part because of the certificate, though my motives are at least as much personal interest as professional - I’ve long admired how people like Malcolm Gladwell, Paul Krugman and many other writers have been able to interpret data and write about what they’ve found, and want to be able to do the same. I was inspired as well by Murtaza Haider, in his book Getting Started with Data Science which, while somewhat dated in terms of the technology, is unique in the author’s emphasis that storytelling is a key component - perhaps THE key component in learning Data Science. But most of all I want to understand how to take data sets, clean them up for processing, and draw accurate conclusions from them.

I’d already tried Coursera’s IBM Data Science which, after a promising beginning (featuring the above-mentioned Murtaza Haider) it began to fall flat and I became discouraged. It also seemed somewhat out of date. So when I found the Google Data Analysis course recommended on a random YT video, I thought I’d give a try.

And so far … it's been great. Billing itself as a course for complete beginners to both programming and data analysis, I wouldn’t say it’s quite that. I think if I was a total beginner I’d struggle quite a bit. But despite starting with the basics, right from the beginning the course guides its students into how to think about data, how to see data we digest every day in a new light. This alone is invaluable.

There are 8 sections in all, with the final section being a Capstone Project. The section are as follows:

  1. Foundations: Data, Data, Everywhere
  2. Ask Questions to Make Data-Driven Decisions
  3. Prepare Data for Exploration
  4. Process Data from Dirty to Clean
  5. Analyze Data to Answer Questions
  6. Share Data Through the Art of Visualization
  7. Data Analysis with R Programming
  8. Google Data Analytics Capstone: Complete a Case Study

Some have criticized the course for using R rather than Python, but I consider that a benefit. 365 uses Python heavily and I already have reasonable fluency in the language. Scientist friends use R over Python in their work, and recommend it, so I’m looking forward to learning the R language, when that component comes along.

I’ll be writing about each section as I finish it. Next up: Foundations: Data, Data, Everywhere