Dan asks, "When looking for contract work or even full time jobs in analytics where do you folks recommend starting? I'd like to pass this on to the new students in the UNH MS in Analytics program."
This is a fun question, depending on the skills of the student. If they're being taught things like how to interface with APIs and how to write code, there are some incredible options available. Watch the video for full details.
Some of the steps mentioned:
- Learn Beautiful Soup and Scrapy in Python
- Learn httr and rvest in R
- Build code to extract data from job boards
- Post code and examples on Github
- Participate in other repos on Github
- Network on sites like Github, TDS, Kaggle, etc.
- Join relevant Slack groups like Analytics for Marketers
- Earn certifications for free at CognitiveClass.ai from IBM
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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, Dan, from our analytics for marketers slack group asks, When looking for contract work or even full time jobs in analytics, where do you Where do you folks recommend starting? I'd like to pass this on to the new students in the UNH ms in analytics program.
This is a fun question, because it depends on the skills of the student.
But if you're, if you're getting a master's in analytics, I'm going to make some assumptions about the kind of training you're getting, I'm going to assume that you're getting training in one of the modern programming languages like our or Python, that you're getting training in statistical tools like SPSS that you're getting training in understanding things like how databases work, how API's work, all these things.
Now, you may not be a coder, per se, but you'd better be if you're getting a master's degree in analytics, you'd better be familiar with all the tools of the trade for them modern day high tech analyst.
So if that's the case, if you're being taught things like how to interface with API's, how to write code, how to scrape data, and process it, there are some incredible options available for your career.
Now, let's start at the basics.
The number one way to get work of any kind is of course referrals to get someone else to refer you into worked for you to have that professional relationship with people, you know, hiring capacity.
For graduates who are with it just have a bachelor's degree and just starting out on their journey, that may not necessarily be something they have a lot of.
But for people with master's degrees who have been in the workforce, presumably, who have not just been you know, camping out in school for six years.
There, you should have some level of a personal network that you can rely on.
And the basic things like LinkedIn and such are important.
If you want to do contract work, you want to check out LinkedIn pro Finder.
So just search for LinkedIn pro finder, you can set up your LinkedIn profile to say I'm available to do freelance work.
Pro tip, you can use that it's a regular employee to for your side hustle.
So that's the basics, I'm going to assume that's all taken care of.
Now, let's talk about the skills you should be absolutely positively networking, which means is a polite way of saying or a fancy way of saying, just getting know people within relevant communities, our slack group analytics for marketers, for example, GitHub, being an active participant on GitHub, towards data science, capital, all of the major analytics hubs where people are talking about the stuff that you know, you should be publishing, absolutely positively publishing on your own blog, you should be submitting content to sites like Katie nuggets, if you're doing some original academic research search, you should be publishing it in like analytics for diet, if you're, if you are, have content ready for those sites.
But you absolutely should be publishing your work, you should be publishing your code.
So your code should be on your GitHub profile.
And if you're graduating with a master's degree in analytics, and you don't haven't GitHub profile, get one set one up, it's totally free.
But set up a repo there is set up a profile, make sure that you're publishing your work so that you can get feedback from other people, you should be absolutely participating in analytics discussions in places like Reddit analytics channel, you should be participating on Stack Overflow, stack, exchange, all those sites, there are analytics forums, code forums for analytics tools like our that you can demonstrate your expertise and connect with people who have other expertise, and see how they work.
Now, again, this is all stuff that, to me, seems like table minimum, right, you should be doing these things to interact to be known to do publishing work, you should have a blog, you should have a YouTube channel, you should be demonstrating the work that you do.
And if you don't have any work to do get some.
If you don't have a paying job, that's fine.
Go volunteer to nonprofit, go volunteer at a local small business, fix their analytics problems, fix their data problems, come up with interesting insights.
And you use that as the fuel to publish papers your own, you could absolutely publish and be known for a cutting edge paper on the analytics of local pizza sales.
Right? You could do it absolutely good do it.
So that's an example.
Now, let's take it to the next level.
If you have focused and studied and you've learned Python, or are Python, there's two libraries, you need to know three really beautiful soup, selenium and scraping these three libraries will give you the ability to say grape and and extract data from other websites.
In our it would be our vest and HTC.
Er, you should absolutely be building interfaces to various API's.
For things like the indeed, com job boards, and then be harvesting that data with permission and adherence to their terms of service, for analytics jobs, in the places you want to live in, in the roles and the companies in the industries and have that data available so that you know where you want to work, you have the data you can apply to those positions that make this make the most sense for you.
There are tremendous opportunities.
And if you think ahead, you can also look at just companies that are hiring the analytics space in general, even if they don't have your role available.
who's hiring? And then can you make a connection of some kind through LinkedIn or through some of these other networking sites, say like, hey, do you need a a new analyst on the team, you know, somebody who may not necessarily have you know, 10 or 20 years of experience in the industry, but be willing to work, willing to, you know, either contract out side hustle or full time at your company.
If a company is hiring, and they're publishing jobs, on public job boards, they're desperate.
Because they haven't been able to fill that with referrals, they have not been able to fill it with internal hires, they have had to go outside and publicly admit, we need help with this stuff.
So those would be my suggestions for how new students can be getting into the analytic space quickly and in a in a way that that pays decently Follow these tips from the basics, do all the basics for sure.
Love, love those skills.
If you don't have those skills, and for some reason you went into the program that doesn't have the coding skills, go to cognitive class.ai and take some of these courses on R and Python today Get those skills.
It is a totally free system from IBM.
You can get some certifications for free, and it will really benefit you.
So great question Dan.
As always, please leave your comments in the comments box below.
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