I grew up wanting to study architecture; I always did well in math. But then I became fascinated with computers, and (not to brag) taught myself to do some impressive things with them. For example, I knew how to do minor-ly complicated things in Microsoft Excel, and could edit photos in Adobe. Because I had some soft technical skills already, I chose to take a data analytics bootcamp to push myself forward in my career.

Logistically, you will require sixty days for the actual bootcamp. That is two months for you to learn quantitative statistics, probability, data modeling. Fourteen hundred and forty hours is more than enough to learn all of those wonderful things, in my experience, if you were to study for at least seven-to-nine hundred of them. But you will probably clock half of that time just reading up on data analytics, because as you will find out, it is more than interesting.

When it comes to the time before the bootcamp, here is a list of the top three things I recommend that you focus on, when you are getting ready to start a data analytics bootcamp.

Organize your things, and your life.

Make room in your house or your apartment for a comfortable place to use a computer, and drawer-space to save notes. Also, start waking up earlier every day, to get accustomed to being able to get to class on time.

Tackle learning some topics on your own.

To give yourself confidence that you will be able to keep up with the class, try taking an online course or teaching yourself from reading one of the skills or tools that the data analytics bootcamp will cover. Reading just a few articles will help you learn better in the class.

Get to know your computer.

Download and install every program that is listed on the curriculum, and then some. Getting good at using computers takes time, just like everything else. Start early on training, and you will see earlier results.

By the end of the class I took, I was able to design and run complex algorithms, create fancy displays of useful data, explain the whole concept and value of data analysis to complete strangers, and gain senior executives’ confidence in the validity of my own data analysis.

This leg of my journey, in terms of intelligence-gathering, was one of the most trying periods of my life. Thankfully, the cheerfulness and camaraderie of the group of tech-buddies I had to get through it with was sufficient to make it enjoyable. As another bonus, besides having fun, I added to my toolbox a versatile and wonderful set of new skills, ones that have strengthened my resume, opened career paths for me, and landed me jobs.




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