Jessica asks, "When it comes to marketing data science, I've got very good business knowledge, but lack of the technical side. any advice?"
The first question you have to ask is whether you need the hands-on skills or just knowledge of what's possible. The second question is what skills you already have. Remember that in marketing data science, technical skills go hand in hand with mathematical and statistical skills. One without the other is a disaster waiting to happen.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode, Jessica asks, When it comes to marketing, data science, I've got very good business knowledge, but lack of the technical side any advice? So, there's a couple of questions to unpack here.
Remember that data science data science is all about the intersection of four skills, business skills, technical skills, mathematical skills, and scientific skills.
And to be a data scientist, you have to have aptitudes in all of them.
You don't have to be expert, but you've got to be competent in all those areas.
So that you know what you're doing, why you're doing it, how you're going to do it, and what to do with what's what tech tools and techniques you will need.
The technical side is the part that people spend an awful lot of time on.
Do I need to do you know, be doing R or Python trouble using SQL servers? Should I be using an NO SEQUEL environments, what should the what are the tools and for data science in particular, I would actually say that the mathematical and statistical side is more important to start with, because the tools are how you do something, the math is what to do and why you would choose a specific technique.
Even something as really simple as the difference between average and median is important because they have different uses use cases, you would use an average for example, on a normal distribution, you would use a median on a non normal distribution like a power law curve.
And so knowing the techniques will then guide you as to which technical skill you need, what functions you know and which language to use.
If you go about it the wrong way.
Well, it's not wrong.
If you go about the technical first mathematical Second, you will find yourself at getting caught up in shiny object syndrome and be choosing techniques that may not be appropriate for the problem you're trying to solve.
So when it comes to Jessica's question in particular, the first question is, do you need the hands on technical skills you may not, depending on your company, depending on the environment you're working in, if you have data scientists and such on staff already, you may be able to get help with the technical and mathematical and you need to be versed in what's possible what the right choices are understanding the theoretical frameworks, you may not necessarily need to get your hands out to start writing code.
If you have people who can help do that.
We have an agency that will help you do that.
But you need to know what to ask for.
It's like it's the difference between going to a restaurant and knowing what you want off the menu and in what order they go.
versus going into the kitchen and cooking it yourself.
Right You can have somebody else cook it for But you still need to tell them what you want and whether it's available and a good choice.
And somebody says, You know what you want it for your appetizer, you're like Boston Cream Pie.
These situations where that's not necessarily appropriate.
And the same is true in data science.
So that's the first question, do you need the technical skills? Or do you just need the know how, what's possible so that you can orchestrate the project as more of a project manager? And then the second question, really, is that assessment of what skills do you have? Do you have the mathematical and statistical background? If you don't, again, I firmly believe that you'll be a better data scientist in the long run.
If you are versed in the statistical first, and then in the technical second.
Take a course there's a good jillion and a half courses out there and you know, in the recording this in the middle of 2020 A whole bunch of them are free right now, and will probably be free for a little while longer.
So go and take courses that will help you get the knowledge that you want.
Right on the statistical side, then go take some technology courses again, many, many many for free out there, the big fork in the road that you're gonna run into is going to be on the technical side, there's really two major languages R and Python.
Python you see used a bit more on the machine learning side are you see a bit more used on the statistical and data science side, but both are very capable.
Both are great at covering 96 97% of use cases out there.
And there are packages in each language that allow you to interoperate in other in the other language.
AR has a package called particularly which allows us Python code inside of our notebook environments like the Jupiter notebook and environment allow you to run multiple languages simultaneously as long as you know them.
And you can manipulate data in them.
And so there's a lot that can you can do in those environments to interoperate.
So pick whichever one works better with your brain, because they are very different languages from a syntax perspective.
And start with that, I personally lean towards our I'm a little older, I got a little gray here, hair here.
And I grew up in languages like C and Java, which are more restrictive languages, so are feels more natural to me.
There are no shortage of people, including my kids, for whom Python is a lot more natural.
It's it's easier for them and I can hack around in Python, but it still is not as intuitive to me as our either way.
The languages themselves are secondary to learn how to think as a programmer.
One of the reasons horses that I think is actually a really powerful and useful resource is a of a fun game language called Scratch by MIT.
If you go to scratch.mit.edu.
It lets you play around with little colored blocks that help you understand the concepts of programming with again without having to write code.
And that kind of environment really gets you thinking about the What am I doing and why am I doing it not necessarily the how of the implementation.
So it's a really good intro to programming as a whole.
And then you can use that knowledge and graduate to things like IBM Watson Studio, for example, which has the SPSS modeler inside, which is again those little colored blocks that you drag and drop in and connect them in sequence.
If you're thinking about how to program and you learn in an environment like scratch, you can graduate to a professional environment and do the same things again without having to learn how to code.
So when it comes to marketing, data science, learn the statistical then learn the technical and on the technical side, choose path dabble around at first, see which one's more comfortable.
Take like an intro to R and an intro to Python, see which one feels better to you.
If neither feels better, you know that you're going to be on the project management route because you're not you're not going to enjoy the technology.
One of the things and we'll talk about this in soft skills at some point is that if you don't love the doing it part, don't make yourself do it.
Yes, the salaries in data science are great and all that stuff.
But if it doesn't make you happy, you're not going to do a good job and you'll be miserable.
I don't particularly enjoy doing finance.
I'm not good at it.
So it's it's a you pick.
If you're going to consider this as a career option.
Make sure you love doing it.
Make sure that you want to do it.
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