- Identifying the end goal is the first step in identifying great training.
- Look at job listings for your desired end goal to identify the common technologies and qualifications needed.
- Evaluating classes involves looking at the class itself and the reviews of the class.
- Reviews should be taken with a grain of salt and compared to testimonials that show actual achievement of the end goal.
- Course objectives should align with the end goal and cover most or all of the necessary technologies.
It’s easy for someone in the training industry to identify great training. They live, eat, and breath it. What about everyone else? How can you identify great training even if you aren’t in the training industry? In a previous post, I raised the specter of bad training completely wasting your time and money. You definitely don’t want to waste your time and money; let me show you how to identify great training, no matter the field.
End Goal
Our first step is to decide on our end goal. This could be as simple as getting basic concepts or as advanced as becoming a Data Engineer. It could be implement a Big Data solution at a company. Identifying our goal sets us to thinking about what the smaller objectives of becoming a Data Engineer. What are the smaller things we’ll need to learn or master? How do you know what you need to learn if you don’t know the subject already? Take a look at job listings for your desired end goal. Here’s a start for Data Engineer. Take a look at 10-20 of the positions. Notice the common technologies listed. Notice the common qualifications that are listed. I suggest you actually write down the list of technologies and qualifications you’ll need. We’re going to use this later on to validate our training meets our end goal.
Evaluating Classes
Our next step is to start looking at classes. We do this by looking at the class itself and the reviews of the class. This may surprise you, but learning companies are jumping on the Big Data bandwagon. They’re willing to put whatever materials out there.
It’s up to you to separate the bad training from the good training.
Reviews
We’ll start off by looking at the reviews. For a baseline, look at some comments from one of my YouTube videos:
- just great explanation !
- really nice video and explain the terms in a simple way…
- Just wow…very nicely explained
The video is pretty good from the comments. It’s 12 minutes long. They learned some Big Data concepts passively. What the people didn’t say is what to pay special attention to. They didn’t say “I’m a Data Engineer now.” or “I can pass an interview now.” They definitely didn’t say “I got a data engineering job now.” Compare these quotes with the reviews from your online training provider of choice. They’ll look about the same. Someone paid $800 and passively learning a few concepts. They definitely didn’t achieve their end goals and become a Data Engineer. Otherwise, they would have said that. Now, compare these quotes to my testimonials. Here’s just one:
It’s amazing to me that I got a job at [a leading Hadoop vendor]. I owe that 100% to Jesse.
Greg’s expressed goal was to get into Big Data and he did that with my course.
Course Objectives
Here is another tricky one. You have to generally know what you need to learn to achieve your end goal. For example, if the course promises mastery and every lesson says “Introduction to…”, you won’t end up being a master. You’ll have passively learned a few concepts, but you will not be a master. This is a common mistake my students will make before they take my classes. “I took all of these courses, but I still can’t pass an interview.” It’s obvious to me that the course is useless to achieve their goal, but they don’t know that.
Now, look over the courses’ technologies and objectives. This is where you need to take out the list of technologies and objectives from the job boards. Does the course cover most or all of the technologies? Ask yourself an honest question: after this course, could I get a job? Could I really create the solution? If the answer is no, you’ll need to find a different course.
Another tip off is the length of a course. Some courses are legitimately short and others have to be long. Most Big Data subjects are too complex to teach in a short amount of time. When you see a two or three hour course on Big Data, you’re getting the basics. You’ll have learned enough to be a beginner. If that isn’t your end goal, you’ll need a different course. The final look is at the course writer or instructor themselves. Is the person recognized as an expert by their peers? What is the company that published the material’s reputation? I’ve seen Big Data courses created by absolute novices. I’ve seen other courses that copied and pasted the Apache documentation and read their slides to you.
Having written many courses and instructed thousands of students in Big Data, I can say the people make a massive difference. I take your time and money seriously with my training. My reputation in training precedes me. Get my awesome Big Data engineering training.
This post is the second one in a series talking about costs in training and identifying good training. This first post considers the hidden costs of bad training. The third post shows the ROI of the right training.
