Course Framework

steve@thoughtsociety.org Amateur Data Science, Course Leave a Comment

Amateur Data Science Course

Our approach is to build this course as a set of accessible resources.  Since recently going through our own online learning path, it was apparent that there are many different – and valid ways to design a course so it helps the most people. In our case, we are targeting a smaller segment of a very large group, within which, there are the coders, ops I.T. folks and technical product managers who are not trying to change jobs to become a data scientist. You are looking to expand your knowledge, broaden your toolbox and contribute in ways that you were always interested in but didn’t have the time to go back to school to learn.

That comes down to assembling resources, trying things out and building experience with these new ‘toys’ in the data science realm.  We call them toys because for one, they are lot’s of fun.  Really. You will see how easy it is to build a sophisticated machine learning model that actually works on real world data and then do it repeatedly.  I think that is awesome and two, they are open source, enhanced by a worldwide community of enthusiasts and data scientists. Three, you actually can learn this and get things done better and faster than if you had to wait to provision data science services within your company or through a consultant. I’ve been there and my first instinct was to try and learn to do this myself. Staff data scientists are extremely busy and rarely available to perform day to day analysis.

Course Outline

The Amateur Data Science course is made up of 5 topical tracks.

  1. Getting Started with Python – Setup your system to work in Python
  2. Dealing with Data – Wrangling/Munging and general data manipulation essential to data science pursuits
  3. Visualization – Several approaches to visualize data – Static graphs, interactive graphs and dashboards and ready-made workbenches 
  4. Analysis – Crunch time. Statistical models and methods, correlation and causation, insights from data
  5. Machine-Learning – Fun stuff. Regression, Classification and natural language processing

The topics get deeper and more involved as you progress up the tracks. To get to the end, you need to get through the early topics. 

Challenges and Exercise Philosophy

In our view, exercises are something you motivate yourself to do. Drill, practice and hack.  Once you start interacting with other learners, you will see where your gaps are, what you need to strengthen and how you might get there. With time as precious as it is, we think you know how to manage that. You should spend as much time moving ahead in the course and not getting stuck on a crazy-hard problem we dream up to stump you. If there were exercises, you would look at the solutions anyway so we skipped those for now.

You will find ways to practice your freshly acquired knowledge against some of the demos included in the course. This is the fastest way forward in our opinion.

The Framework

The course outline can be experienced by navigating the hierarchical accordion folders associated with the course and it’s tracks. Within a track, sections are accessed by clicking a drop-down bar with the mouse.

 

Here’s how you get to a course track:

The track is a post with sections provided by the accordion element.

Each track has an overview video which summarizes the track.

Sections

Sections of a track are like individual lessons. They usually contain what could be found in a blog post but structured more like a course.  There will always be a demo video at the top of a section which takes the viewer through most of what is covered within that section.  If there are code modules to share, those can be found in the ‘code’ section of the track. Code will be marked to associate them with the relevant section. Sometimes there will be files, links and other documents placed in the ‘resources section.

 

Just as a nudge, you should sign up right away with the Discord chat Channels for the course:

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Also, don’t forget that all of the course videos are on the Thoughtsociety YouTube Channel.

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