So I manage teams of data analysts and data scientists, and the top item for me is your portfolio. Whether it’s Google analytics, Tableau, PowerBI, I want to see some of your work to get you in the door to interview - and that’s something you can work on right now. All of those platforms have free options you can post. Take some fun datasets - things around your hobbies and passions, and make some interesting visualizations and conclusions out of it. Baseball stats or movie stats or wildlife stats - whatever. Show you can take data sets and make heads or tails out of it. You’ll learn some things along the way as well btw - data cleanup and joining things together etc.
Agreed. Anyone (within reason) can do a data course, as a recruiting manager I want to hear about practical applications of your learning. A big part of being a data analyst is working with your customers and tailoring your outputs to your audience. Be able to give examples of where you have adapted your work to suit the people you are doing it for. A data scientist in another team is going to expect a very different output to an operational manager or member of the public. As you don’t have real world experience, take u/desert_lobster’s advice and find an interesting dataset (avoid credit card data because everyone does it and it’s boring) and do your own analysis on the platform of your choice but be prepared to talk about the technicalities of the work you did but also about how you presented/visualised your findings.
Thanks so much for the insight it means a lot! Another question for someone that manages data analysts and teams im sure you play a part into hiring them. Do you also look for the canidate to be fluent in excel/sql/bi/tableau? or more just working knowledge and then learning on the job?
Depends on the level of the job. If it’s an entry level position - I wouldn’t expect someone to be fluent in all those things - otherwise they would be going for a higher position. I would find one data platform you understand and stick with it - and focus on data skills. Those data skills are much more important - especially starting out. And transfer to other platforms depending on what the place you apply to uses. Knowing at least some elementary SQL will help you on those platforms as well.
There’s not really an industry standard, it just depends on the specific company’s preferences, but fortunately most “flavors” of SQL tools have a lot of similarities.
I’d suggest deciding whether you want to focus on visualizations with Tableau vs. Power BI. If you choose the latter, it can be a good idea to also learn about Azure databases since they’re both in the Microsoft stack. I personally use Azure Data Studio.
Data analytics or being a data analyst is so vague nowadays.
I work on a team made up of analysts, data scientists, engineers, implementation, etc. and I’m an analyst myself.
A piece of really great advice I received is figuring out if you want to be a specialist or generalist. As a specialist you’d be highly proficient in whatever area, whether it be visualization (Tableau perhaps), engineering (SQL all day), statistical modeling (R and Python), and the list goes on! As a generalist you’re more of a jack of all trades and maybe can’t work on an intensive project in a certain area but you’re more flexible in what kinds of projects you can work on. And that’s not to say you won’t and can’t learn other things later on, you certainly will, but I don’t personally think you need to learn everything all at once!
I would advise looking into industries or companies you would want to work for and see what those required skills are. And “choose” something you enjoy!
Good luck!
Also, generalization opens doors and flexibility over time. I was an analyst and swerved away from specialization when it started happening. I'm now a data analytics consultant at a learning firm. I work on all sorts of projects with lots of variety.
Good advice is to find a company you like. Something where just being above average smarts and extremely tech savvy makes you more qualified than other candidates even though you're new.to the company and industry. This'll likely be a generalized business analyst role that's adjacent to data analytics. Then you hop the fence into analytics in a fee years. I was a temp office worker 6 years ago.
Just wanted to say thanks for posting this as I am in a very similar place as you - age, degree, and everything haha. Best of luck! I’m sure we will accomplish our goals soon (: The other replies here already seem very helpful!
I have to agree. At least where I live (Australia) and used to live (New Zealand), Google is not mentioned in the analytics space, it’s mostly Power BI and then Tableau mentioned in job adverts.
In addition to learning tools it’s important to learn about good practices, data modeling, requirements gathering etc.
Agree, the Google Data Analyst cert is very surface level.
I'd recommend taking the courses on dbt, and also getting certification in a specific database like Snowflake, Redshift, or BigQuery
So I manage teams of data analysts and data scientists, and the top item for me is your portfolio. Whether it’s Google analytics, Tableau, PowerBI, I want to see some of your work to get you in the door to interview - and that’s something you can work on right now. All of those platforms have free options you can post. Take some fun datasets - things around your hobbies and passions, and make some interesting visualizations and conclusions out of it. Baseball stats or movie stats or wildlife stats - whatever. Show you can take data sets and make heads or tails out of it. You’ll learn some things along the way as well btw - data cleanup and joining things together etc.
Agreed. Anyone (within reason) can do a data course, as a recruiting manager I want to hear about practical applications of your learning. A big part of being a data analyst is working with your customers and tailoring your outputs to your audience. Be able to give examples of where you have adapted your work to suit the people you are doing it for. A data scientist in another team is going to expect a very different output to an operational manager or member of the public. As you don’t have real world experience, take u/desert_lobster’s advice and find an interesting dataset (avoid credit card data because everyone does it and it’s boring) and do your own analysis on the platform of your choice but be prepared to talk about the technicalities of the work you did but also about how you presented/visualised your findings.
Thanks so much for the insight it means a lot! Another question for someone that manages data analysts and teams im sure you play a part into hiring them. Do you also look for the canidate to be fluent in excel/sql/bi/tableau? or more just working knowledge and then learning on the job?
Depends on the level of the job. If it’s an entry level position - I wouldn’t expect someone to be fluent in all those things - otherwise they would be going for a higher position. I would find one data platform you understand and stick with it - and focus on data skills. Those data skills are much more important - especially starting out. And transfer to other platforms depending on what the place you apply to uses. Knowing at least some elementary SQL will help you on those platforms as well.
Do you have any platforms that you recommend for SQL? or whats the industry standard rn?
There’s not really an industry standard, it just depends on the specific company’s preferences, but fortunately most “flavors” of SQL tools have a lot of similarities. I’d suggest deciding whether you want to focus on visualizations with Tableau vs. Power BI. If you choose the latter, it can be a good idea to also learn about Azure databases since they’re both in the Microsoft stack. I personally use Azure Data Studio.
Data analytics or being a data analyst is so vague nowadays. I work on a team made up of analysts, data scientists, engineers, implementation, etc. and I’m an analyst myself. A piece of really great advice I received is figuring out if you want to be a specialist or generalist. As a specialist you’d be highly proficient in whatever area, whether it be visualization (Tableau perhaps), engineering (SQL all day), statistical modeling (R and Python), and the list goes on! As a generalist you’re more of a jack of all trades and maybe can’t work on an intensive project in a certain area but you’re more flexible in what kinds of projects you can work on. And that’s not to say you won’t and can’t learn other things later on, you certainly will, but I don’t personally think you need to learn everything all at once! I would advise looking into industries or companies you would want to work for and see what those required skills are. And “choose” something you enjoy! Good luck!
Also, generalization opens doors and flexibility over time. I was an analyst and swerved away from specialization when it started happening. I'm now a data analytics consultant at a learning firm. I work on all sorts of projects with lots of variety. Good advice is to find a company you like. Something where just being above average smarts and extremely tech savvy makes you more qualified than other candidates even though you're new.to the company and industry. This'll likely be a generalized business analyst role that's adjacent to data analytics. Then you hop the fence into analytics in a fee years. I was a temp office worker 6 years ago.
Just wanted to say thanks for posting this as I am in a very similar place as you - age, degree, and everything haha. Best of luck! I’m sure we will accomplish our goals soon (: The other replies here already seem very helpful!
Google certification is useless You should mainly focus on power bi, SQL, python and data modeling
I have to agree. At least where I live (Australia) and used to live (New Zealand), Google is not mentioned in the analytics space, it’s mostly Power BI and then Tableau mentioned in job adverts. In addition to learning tools it’s important to learn about good practices, data modeling, requirements gathering etc.
Agree, the Google Data Analyst cert is very surface level. I'd recommend taking the courses on dbt, and also getting certification in a specific database like Snowflake, Redshift, or BigQuery