You Ask, I Answer: Learning Marketing Data Science for Free?

In today's episode, Sean asks, how can I develop data science skills without spending lots of money? Fair question. You don't have to spend any money. To learn data science and be able to do it well today, because of all the massive skills shortages and talent shortages, there are companies that are just giving away resources for you to become a data scientist because frankly, we need a pipeline of talent that just isn't there. So, how do you get started?

It's a four-step process. It's uh download what you need, take some courses, uh, go practice, and then do an internship. So let's let's walk through these. I'm gonna go at this from the perspective using the R programming language for data science. Uh, you can also take the Python route.

Either is fine. But you're gonna learn uh to code and to build stuff in one of these two programming languages. First place you want to go is to get tools. If you have a relatively modern computer, download the open source free R programming language and then the uh RStudio IDE, which you can find at RStudio.com. That would be uh the first place I would go.

Uh you can also, if you want to uh have access to uh a bigger suite of tools, use the uh free to try uh IBM Watson Studio. Either one will be great. Uh I would suggest for beginners start with R Studio. And if you have a computer that is not say the you know, uh a modern computer, if it's more than a couple of years old, I would go and uh e again sign up for IBM Watson Studio. Uh, you can get a free uh account there and like 50 hours a month of of compute time.

And you can also go to uh RStudio.cloud and get a free uh R Studio Cloud account, which allows you to run RStudio just in a web browser, and all the computation is handled uh remotely on somebody else's servers. So those would be the tools that you would need um to get started. I would strongly recommend uh installing what's called the tidyverse, uh T-I-D-Y tidiverse inside of R Studio. And when you install R Studio and R, you will be able to uh to install a lot of these things from the graphical user interface. It's not you don't have to write a whole bunch of programming code just to get up and running.

Next, start learning just the basics of how to use R. And there's a couple of different places that you can go. Uh education.rstudio.com has a ton of resources, including uh some tutorials and things that I think are important. There's some really good books here, like R for Data Science, which is available for free online. You don't have to spend any money as long as you don't mind reading uh off the screen.

And then once you've gotten the basics of the R environment down, uh, I would go over to IBM's cognitive class.ai. Cognitive class.ai is a portal for tons of free classes, free courses, and certifications, little badges that you get uh from IBM. Again, totally for free, uh, completely for free. And the one I would look at would be well, it would be looking at two. One is Data Science Foundations, and then applied data science with R.

And in Applied Data Science with R, you'll see R101 with databases, data visualization, and you get a badge at the end of it. Data Science Foundations is more conceptual, it is a lot more on what is data science, the the tools, the methodology. Um, I would do this as well. Start with foundations, then go to applied data science with R. And uh as you saw uh in the course catalog here, uh, there are uh ones for p uh Python as well.

So those would be the step two. Take some courses, take these courses. You can see, you know, they're gonna require probably anywhere from 10 to 15 hours of your time to go through these courses and to uh to take the classes and and and stuff like that. That will get you tools and knowledge. The next step after you have tools and knowledge is to start to apply the knowledge.

Because again, all this theory and all these this example code is good, you need it, but it's not enough. Now you need to go uh and do some practical application. And the place I would suggest for this is uh go to Kaggle.com, sign up for a free account there, and what you will see are uh once you get in, is looking at there's exercises, there are uh all sorts of discussions, but most importantly there are these competitions. These competitions are open, and what it allows you to do is go through and pick uh some challenge here, and you can join a team if you want or just tackle it on your own. Uh, but look at the major challenges that are available, particularly at your skill level when you first sign up, it'll be a beginner, and it will give you some suggestions of uh exercises to try to apply your knowledge, try out your skills and see how do you uh how do you do how well do you do with the challenge?

And you can obviously see other people's discussions, see shared examples of code. A lot of folks in the Kaggle environment, fair warning, are uh very Python oriented. Uh so if you are looking for uh some R stuff, you'll have to find that subcommunity within here in order to find folks who can answer questions about your code specifically. Also, essential, sign up for a free account over at StackOverflow.com because you're gonna be copying and pasting other people's examples and ideas uh and then tuning them and tweaking them and eventually you know making your own versions of this code uh for a long time. So that's step three, taking your knowledge and applying it to real-world examples, because that's what's on Kaggle, there's a lot of real world data sets.

You can see there's tons of competitions on here, uh, you know, Santa's workshop, NFL, big data bowl. Uh so you can find something that's fun to work on and uh challenging at you whatever skill level you come into it at. Step four is do an internship or a project with any company uh that and see what problems you can solve. Easiest way to do it would be an unpaid internship. Companies are generally happy to have free labor with the understanding that you know you're gonna be working on something that's probably not a top priority for them.

Uh I would especially look at uh something like a nonprofit and be able to use some of the techniques that you've learned in these courses to solve a pro a challenge for them. Uh real good example for a nonprofit would be uh doing something like regression or classification. What makes a good donor or what increases donations and you see if the you can apply all the knowledge you've gained to help a company build some tests and see if they can and generate a useful outcome. The other thing you can do, obviously, i if you want to go to the practical application is start a side hustle, right? Start a a a boutique consultancy, and again, try and find a couple of companies that want the help.

You'll be doing some free work up front because you've got to get case studies. You've got to get examples that you know what you're doing. The certifications are important and useful, but they're not enough by themselves. You've got to have a portfolio of usable stuff that shows that you you're competent. So do some projects, and then who knows, maybe your side hustle becomes your main hustle down the road.

That's certainly was the path that I took. So that's the the answer to how do you develop data science skills without spending lots of money. Free tools, because almost everything is happening in data science and machine learning and AI is happening at the open source level first, and then companies will commercialize what works. So free tools, free courses, free challenges, and then you'll be giving away free work in the beginning with those internships or projects just to get your feet wet in in practical application. Expect this to take, depending on how much time you're willing to commit to it.

Expect it to take anywhere from six months to you know five years. If you if you go all in and you sit you know put your Netflix subscription on pause and and stuff, and you you're working two or three hours a night, you can become proficient in data science in six months pretty easily. Um if you like Netflix and having friends and stuff like that, then expect it to take you know a couple of years if you spend like an hour a week, uh, for example. Um it will take a lot longer, but you will get there one way or the other. Uh and again, all these tools are free, so don't spend money.

Invest your time. That is the part that will that will cost you. Invest your time. Uh if you have follow-up questions, leave them in the comments box below. Subscribe to the YouTube channel and the newsletter.

I'll talk to you soon. Take care. Want help solving your company's data analytics and digital marketing problems? Visit trustinsights.ai today and let us know how we can help you.