Complete Machine Learning & Data Science Bootcamp 2022

Program Completed and Job Offer!!!

Good morning everyone! This sub has been so helpful during my time at WGU that I wanted to give back and share some advice.

My background: I currently work in the IT department of a lab. I am a laboratory information system analyst. I configure tests, profiles, and reports. I also lead projects and develop validation plans. I don't consider it "real" IT like but I use ternary operators for logic when building everything. I mentioned my project management experience and the use of ternary operators during interviews.

Education background: I already have a BS degree so all my general education classes transferred in. I took calculus through Straigherline. It took a month. I bought an older model CAS calculator off eBay for 40 dollars and it was worth its weight in gold.

WGU education: I completed the program in 11 months. I researched the courses here and in the course chatter for the best tips. The longest class was C993 (it has since been retired) and it took 5 weeks but I passed it the first time. If you have any questions about any specific classes, I will be more than happy to answer them.

Capstone: I used this Udemy course to learn about Machine Learning and Data Science. My biggest advice is to pick a data set that can easily be boiled down to numbers. I used Python, Jupyter Notebooks, and Dash Plotly to build the app and it is hosted on Heroku.

Resume: I used the resume service through WGU and while it was helpful, I found more help at r/cscareerquestions. They have a Daily Chat Thread on Tuesday where you can post an anonymized resume for people to give advice about. Here is my anonymized resume I used.

LinkedIn: LinkedIn is a very powerful tool. Have a good and professional-looking picture, have a great headline(mine was "Aspiring Software Engineer | Experience in Java, Python, and C++"), and a detailed About section that showcases your goals and languages and technologies that you know. Also, link your Github to your profile.

Github: All of your school projects should be on your Github. Give them generic names and do not mention that they are for WGU class XXXX. In the README section, I listed the key features of the repo and the languages and technologies used.

Interview prep: I used Firecode.io for studying algorithms and data structures. I enjoyed the simplistic layout. If you are going for a FAANG or Big N, you should probably go with Leetcode and use this list.

Job search: I used LinkedIn, Glassdoor, and Indeed to search for jobs. From mid-December to today, I applied to 575 jobs. I was looking strictly for remote jobs. I basically applied to every job that did not say "Senior" in the job title. My school of thought was "it is not my job to determine if I am qualified for the job...it is their job to do that". In fact, the job offer I received today was for a Software Engineer II. The main requirements were Agile development environment for 2 years (which I had at my current job) and 2 years of Java programming (I consider the 1 year of school as 1 year of experience). I was mainly looking for Software Engineering and Data Engineering jobs. This is a weird tip but when searching for remote jobs on LinkedIn, also look for Myrtle Point, Oregon. That is Remote, Oregon and I found that a lot of big-name companies posted their remote jobs like that for some reason. If I saw a job on any of those websites, I went to the website to apply to that specific job and also any other related jobs as well.

Interview: My two most promising interviews (ones that went past the initial HR screening) were more or less behavioral and very light technical interviews with the team and manager. For the basic behavioral questions, I had already planned my answers out in advance. For the technical ones, I was asked about my experience, technologies/languages, projects, and what I would do differently on the projects knowing what I know now. The biggest thing to do is show eagerness and a desire to learn and grow.

I think that covers about everything. If you have any questions, please feel free to ask!

Edit: I studied about 3-4 hours a day during the week and 8 hours a day on the weekend.

Short answer: 7 Days

Long Answer: I had a false start that began at the beginning of May. On June 21 I realized that it wouldn't be viable, so I emailed my course instructor and explained the situation. I chose a dataset off of Kaggle and was able to pull somthing together in a week.

Tips:

• take the Udemy ZTM Machine Learning & Data Science Bootcamp 2021 if you can. It's sometimes on sale for $15. This is an excellent practical introduction to Data Science and is honestly what I expected the AI course to be. WGU failed us here.

• Go to Kaggle and pick a dataset. Find something with ten or so features and couple thousand data points.

• Use JupyterLab to develop your product. Make use of the markdown feature to document the code for yourself. Use as many notebooks as you need to figure things out, then create a final notebook for your dashboard.

• Use Voila to generate your dashboard.

• Host the dashboard on Heroku. There's good, easy-to-follow instructions on hosting through Heroku.

• Look at the Capstone archive for examples of how the report needs to be written.

Avoid_Calm basically said everything I was going to. I'm working on the capstone right now and it really isn't that bad. You only need the most basic understand of how to apply machine learning to a problem. I'm using this course: https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/ You can check out WGU's capstone excellence archive or whatever its called to see what a couple people have done.

The capstone is really weird, the machine learning part of it is super minimal. The majority is about project pitching, documentation, and success analysis. It feels like they took a business degree PA, said "fuck it just throw some ML in there", and made it the Csci capstone.

Capstone machine learning question

So, I have pretty much 0 experience with ML. I have been through most of the ML class on Udemy everyone seems to like and recommend. It's been great but I am having trouble grasping the prediction part of this.

So the way I understand everything is essentially: you train a model on a training dataset, then you have it predict your dependent variable with the testing dataset. During the testing phase the algorithms answers are compared to the actual answer and that is how the accuracy is determined (drastically simplified obviously). Please correct me if I am wrong so far.

From there I am having trouble making the jump from the testing dataset to truly predicting something without any data. For instance, if I were to predict a stock price, or a weather pattern, how exactly do you just produce that data? Unless I missed that part in the Udemy course, and the tons of other material I have read and watched on the subject, none of it actually seems to explain how to perform the prediction, or return the results, of a true prediction based on, say, user input of symptoms to return a probable disease diagnosis.

I am sure I am just missing something that is keeping this from clicking in my head, but if someone could offer some insight I would really appreciate it.

Thanks.

I don't know if we're allowed to put links to courses, but I'm doing this course and it seems pretty good so far as an introduction to Machine Learning and actually making your own ML programmes: https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

This course uses Python, the basics of which I learnt in about a month, a few hours a day. I had some some previous intro level experience in other languages. Let me know if you have any questions about it.

I found this course to be very well rounded: https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

I had the same thing man. We have a free subscription from school and in my previous semester I used it A LOT. When the courses get more advanced I noticed that I'm not learning anything and could not remember what I learned 3 days ago. I stept away from DataCamp and used many other MOOCs. I found Udemy one of the most usefull sources to keep learning. Coursera in my opinion is also a great resource, when I finish my Udemy Course (Complete Machine Learning and Data Science: Zero to Mastery) I'm going to start at the machine learning specialization from the University of Washington on Coursera. My advice for all of you is to quit DataCamp and find other resources to learn. The introductions are great to learn but after the introductions you won't learn much.

I'm halfway through this course and it isn't bad, includes a section on Python for those unfamiliar to it.

From what I've learnt from all of these courses, the two things they can't really teach is coming up with new ideas and getting people to practice making stuff relevant to themselves. Especially with ML and DS, it's hard to see how you apply that to the real world with prior knowledge of using data on a regular basis.

I did this one this week as a complete beginner to ML. It was really good. https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

I am doing a course with the same motive. It's super comfortable with the practical application of code.

Its on Udemy btw - https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

You can also go for - https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/

I have heard good stuff about this one!

I do not know if this is the "best" course, but I have been liking it so far a lot. Not too theoretical, but really practical approach on how to get things done. On sale now too to new Udemy users

https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

Yes, many will say that they alway have sales, and they regularly do ... but I dont know how to ALWAYS find them so its good to get them when they are good. You might enjoy the below.

https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

Yup sure, for ML https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/ I took this course to brush up some of the things, and they explained everything in a very easy way. You will just get hooked with them.

For DL I prefer Tensorflow 2.0, ZTM has a course too for DL but with Tensorflow 1( I think so as they never mentioned it to be 2.0). However there are other tutors you can check them. Also for DL you can opt to choose pytorch library instead of Tensorflow. https://www.udemy.com/course/pytorch-for-deep-learning-with-python-bootcamp/ I have never used pytorch so can't really comment on that.

For statistics youtube can help you a great way.

Yes, follow the ultimate capstone guide for the paper and you should pass the paper part of the task. For the actual ML model, use this course, https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/ , it's easiest to build a predictive model, build out your model on jupyter notebook and then port your model onto a website in Heroku using flask. Should be able to pass doing that.

https://amazon.force.com/ProfileWork?refURL=https%3A%2F%2Famazon.force.com%2FDashboard%3Fsetlang%3Den_GB

Amazon are hiring remote customer service reps with minimal accreditation if you're interested.

If you're looking to pick up a skill that would enable a higher paying WFH role I'd suggest data science https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/?couponCode=MAR2020

https://www.udemy.com/course/numpy-python/ is a good free course

I personally got started with https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/ This has dedicated sections to pandas, matplotlib etc since it's for absolute beginners

They also offer their courses on their own website if your prefer not to use Udemy

https://zerotomastery.io/

https://lazyprogrammer.me/

Sure.
Computer Architecture, DM1, and Advanced Data Management I transferred in, so I can't help you there. Search Reddit for tips :)

DMII was the first course I knocked off. Before I started, I did some learning on my own to learn Modulo and RSA. You need to be able to do all the steps in the extended euclidean algorithm. (I had no idea what that was, but if you spend some time on it you can learn) After I learned the above, I just went through the course material. I think i spent just over a week on this one. Use a whiteboard and go through lots of problems. There are problems in the material, and extra problems available from the instructors. There's a number of posts on Reddit with more information on supplemental material. You need to learn the material, but the OA (and PA) are really not that difficult if you understand the concepts.

OS for Programmers has a long, dense textbook. I read the abridged version that's linked in the course chatter. It's not perfect but shorter than the full version. I actually found the concepts quite interesting and would like to go back and read the full textbook sometime. Because of the length of the material, I've seen some people recommend buying the physical textbook on Amazon to save the eye-strain. Not a bad idea.
After reading, I did a bunch of the quizzes. And also tried to master the questions on http://www.quizsail.com/. It will take quite a few hours to get through them but really helps to nail down the concepts.

Software QA - I went through the course material, did the quizzes, and used http://www.quizsail.com/ again. If you do well on those I think you will be good on the OA.

IT Leadership Foundation - super easy course. But I really enjoyed it and I learned some things about myself that are super insightful. Just go through the material and you will be good.

Capstone - I spent my time on this one because A) I have a lot of time left in my term and B) I wanted a really good project. If you were assigned Task 3 in Intro to AI you can probably use that as a starting point for the paper. Also, you will need to understand Machine Learning for the project. I used https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/ which I thought was great for what I wanted to learn. (Make sure you get it on sale for \~$15 if you are going to purchase it)
There are other courses on LinkedIn learning and Pluralsight included with your tuition, but I didn't find any that suited me.

Good luck!