Course Details
Course Title: Python Essential Training
Instructor: Bill Weiman
Course Level: Beginner to Intermediate
Published Duration: 4h 45m
Actual Duration: 1 week (at about 2 hours a day)
Platform: LinkedIn Learning
In a Nutshell
PROS
- Comprehensive, Well Organised Content In Under 5 Hours
- Quick Overview Of Python
- Ready To Use Codes
- Good Pace, Pronunciation And Diction
Skip to “What’s Good About “Python Essential Training””
CONS
- Slightly Technical – Good for those who wants to learn Python as a programmer
- Tend to Use Technical Jargons Casually
Skip to “What Can Be Improved for “Python Essential Training””
Recommended Highlights
- Opening And Editing Files
- Manipulating Strings, Numbers, Containers And Modules
- Database
Why I Chose “Python Essential Training” by Bill Weiman
My selection process for taking courses on LinkedIn Learning was multi-stepped. I started off with the intention of learning Data Analysis. Through my interaction with experts during my course of work for the past few years, I learned that not only does one need to be good in Statistics, but Data Analysts also rely a lot on software, and “R” and “Python” gets thrown up in conversations regularly From an outsider sitting in the discussions, I knew enough, but not deep enough.
After signing on to LinkedIn Learning, I went through an introductory course on Data Analytics, called “LinkedIn Learning Highlights: Data Science and Analytics”. It was a 42-minute session going through the various aspects of Data Analytics and courses that the LinkedIn Learning Curated.
The course had suggested courses for using Python and R for data analysis, and I chose to start with Python first. However, the suggested courses were pegged at “Intermediate” and recommended having basics of the programming language as a prerequisite. It was then I decided to search for, and picked this course “Python Essential Training” by Bill Weiman.
What’s Good About “Python Essential Training”
Quick Overview Of Python
“Python Essential Training” by Bill Weiman was one of the more recent and comprehensive Python basics course on LinkedIn Learning. Since its commencement, it had been viewed by over 280,000 members and Liked by more than 10,000 members.
Admittedly, I was looking for an easy way out. I only wanted to be sure I know enough about Python to start my Data Analytics learning. At the same time, I studied C++ 10 years ago, so I believed all I needed was a crash course, rather than a hand holding session. Therefore, the published time of under 5 hours tipped me to “Python Essential Training”.
When I looked at the Content page, 15 chapters of lessons sprung up. It was by far the most content-intensive course I had chosen. However, the information was well-organised and categorised into distinct topics, like “Types and Values”, “Functions” and “Structured Data”. Based on my previous knowledge of C++, the arch of the content also looked comprehensive enough.
Comprehensive, Well Organised Content In Under 5 Hours
With so much content under 5 hours, I was expecting a very quick and brief overview of the features and uses of Python codes. And I must say that expectation was well met.
Ready To Use Codes
Bill uses exercise files to go through the meaning and uses of the codes. Most of the time, he edited the code onscreen to demonstrate the flexibility of Python and to weave through the idea that different aspects of Python are not standalone pieces. Instead, a knowledge used in one function can also be applied in another function, or in a loop, for example.
Of course, if the code is too complicated, we can always copy the code wholesale, as long as we know what to use it for. One useful code I expect to use is the number generator.
Good Pace, Pronunciation And Diction
This may sound trivial, but having sat through quite a number of LinkedIn Learning and Udemy courses, I must say the speech of the instructor is often overlooked when people are giving reviews. I understand they might want to avoid sounding politically incorrect or avoid being seen as giving personal attacks, but again, if I can’t tolerate an instructor’s diction and thick accent, it will be a very tortuous learning process.
Bill’s speech was far from neutral – I assumed he was an American based on the speech. However, his diction and pronunciation was very good and there were very few times when he slurred over words. He spoke at a slow pace, good for beginners who are trying to grasp new concepts. I watched the video at 1.25x to 1.5x speed. Compared to someone who speaks at a fast pace, this means that whenever I need to slow down to review or re-view the content, I could easily set the video back at 1x speed without making the sesion sound like a dread.
What Can Be Improved for “Python Essential Training”
Slightly Technical
In short, the course content can still be too technical for someone who wants to learn Data Analytics. For many parts of the course, I was thinking in the back of my mind that “this is most likely not needed in the future”. On the contrary, this is a good course for those who are looking to dive deep into Python programming. After all, based on the course title, this is what the course is for.
Tend to Use Technical Jargons Casually
That being said, there were many occasions when Bill used jargons without explaining further what those mean. This is definitely a bane for beginners. Even someone like me who had basic knowledge of C++ had a hard time catching up.
For example, halfway through, he started mentioning “Methods” when covering “Functions”, and it was about an hour later that he explained what “Methods” really are and how they are a part of “Functions”.
Similarly, I had difficulty in the part on Class Inheritance. 2 minute into the video, I found myself gasping for breath to understand what Class Inheritance is and how it was related to the previous session on Class. When I restarted the video (Bill has a good habit of saying what the session was for as an opening), I realised his explanation in the first 10 seconds didn’t exactly define Class Inheritance and link it to the previous session. I had to re-view the video and study the codes to digest the information before I could make the link myself.
Overcoming Learning Challenges
One of the reasons that made this course a good one was also its weakness. One cannot be detailed enough when the objective was to give a quick overview. This is most obvious in the fact that there were no “quizzes” and project assignments.
I will recommend that anyone who wants to get the most out of this course to do the following:
Disregard the time limit. Or expect to put in 3x the amount of time published.
After each session, study the code and try to associate their uses and places in the code. This should be done fairly quickly in the first few chapters. In the later chapters, more time has to be invested, so that you can associate the current codes with those learned in the previous chapters and try to figure out how to put them together. Re-viewing the parts where Bill tried to demonstrate how the same outcome can be achieved using different codes will also be useful.
Give yourself little tasks or projects to apply the use of the codes. This is more difficult than it sounds, especially without an instructor giving you the tasks. Imagine trying to reverse-engineer what uses loops can be applied for, and then telling yourself to use loops to solve a problem. If all else fail, since Bill’s codes were formulated based on real life applications (like deciding whether a password is valid), try replicating Bill’s codes without referencing his work.
What to Pay Special Attention On
As I mentioned at the start, I took up this course for the sake of proceeding to Data Analysis. However, I’ve already identified the areas of the course that I think I will use in my future learning.
- Opening and editing files
- Manipulating strings, numbers, containers and modules
- Database
I made that conclusion based on my previous experience crunching data using Microsoft Excel. I believe I will only know exactly what is useful after I start the course on Data Analytics proper. Perhaps I will return to this part at a later date after I have a good grasp of using Python for Data Analytics.
Reflection
For those who have C++ background like me, we had to spend a lot of time trying to reconcile our old knowledge with the new ones. This process of comparing existing (or whatever I still remember) knowledge and deciding whether to equate them with the new ones or to “delete” the old ones was what slowed me down.
And yes, despite the published 4h 45 minutes of learning, I took 6 days to finish this course. I started by devoting 4 – 5 hours, but the topic was so dry that I was dozing off and had to keep replaying the videos. Halfway through, I even stopped the course to take a break.
I re-started on the later half of the course by reducing the hours to 3 – 4 hours per day, and consciously taking frequent small breaks to go have a sip of water and to take a walk. I also set my timer to 25 minutes. No matter how many breaks I took, I will definitely give myself a full 15 minute break to ease the pressure on my mind.
Programming is a topic that requires a lot of brainpower for logical thinking. People who are “NFP” in Myers-Briggs Type Indicator test must be mentally prepared for this course.
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