176: I Asked 7 Data Analysts What Tools They ACTUALLY Use

[00:00:00] If you're learning data analytics, don't waste time on the wrong tools. What tools should you actually learn? Well, it's quite simple, the ones that analysts are really using in the industry, in the field. So I went straight to the source. I got seven real data analysts from companies like Google, seven 11, Tesla, and Apple, and I asked them. what tools do you actually use every day? And first up is a Google data analyst, and her name is Sundus Khalid. She's been a data analyst, a data scientist, and a data engineer for both Google and Amazon.

[00:00:30] And let's hear what her favorite data tool is. But before we do, as a reminder, this episode is brought to you by Julius ai, your AI data analyst companion, connect to your databases and or business tools. Pull insights in minutes, no coding required. Thanks Julius, for supporting my work and this episode.

[00:00:49] Avery: If you had to choose one tool you've used the most in your career, what tool is it?

[00:00:53] Sundas Khalid: Okay. I would have to pick a coding language and it's going to be SQL. And I don't think it's a surprise to anybody listening to this [00:01:00] SQL is regardless if you're a data engineer, you're a data scientist or you're a data analyst.

[00:01:05] Sundas Khalid: You have to learn SQL and you have to. Not even know it, the basics. You actually have to know that vast level if you really want to grow in these roles. In terms of the tools, I would say like each role uses different set of tools and they don't have anything in common. So like, I'll stick with the coding language.

[00:01:22] Avery: I like it. I think, yeah, maybe that's not a surprise that, uh, SQL, it's like the most in demand data skill in, and honestly, all three job families. It seems like, you know, I think Python gets close for, for data scientists, but It's, it's really SQL. Okay. So SQL is the tool you've used the most. Do you, do you, do you have a tool that you like to use more than, than SQL?

[00:01:43] Sundas Khalid: I think the tool that I really, really enjoy is Google Collabs, um, notebooks, uh, because they are like so, uh, dynamic, like you can like code in R, it's like similar to like Jupyter Notebook, but I guess like I never really, really got the hang of Jupyter Notebooks, I've always been like a Google Collab [00:02:00] person, so I really love using Google Collab as like part of my job, and what I love about it is like you can write any language, like you can have one notebook and write so many different languages, to produce the results and you can share that code with just literally a link with somebody else that who's going to like take over your work or like scale it and apply it.

[00:02:18] Avery: That's huge in, in the workplace, because like, like you said, like sometimes maybe you're the data scientist and you're writing the code, but you're not necessarily the person who's going to put it to scale, or maybe you just need to share it with your manager or some other product owner or something like that.

[00:02:30] Avery: Uh, but it's also big for those of you who are listening. Who haven't landed a data job yet, because if you ever do any projects in Python, if you do it like in Jupyter notebook, you're not going to be able to share it very easily and like doing it in Google collab allows you to like have a link that you can send to a recruiter or hiring manager and it just makes like your life easier in terms of sharing the work that you've actually done.

[00:02:50] Alright, there you have it. Straight from an expert's mouth, SQL SQL. It's an incredible data tool and probably the most used data tool in industry. By the way, that [00:03:00] clip of Sundus was actually from my full 35 minute interview I did with her. And you can watch the full thing by clicking here on YouTube or checking the description down below for the actual link.

[00:03:10] Okay, now let's go straight from a Google data analyst. To an Apple data analyst, meet Jen Hawkins. She was literally a delivery driver before going through my accelerator program, and afterwards she landed a six-figure data analyst job at Apple. Now, I'm guessing most people think analysts at Apple would be in the weeds with Python scripts and machine learning models every day, but that's actually not really the case.

[00:03:34] The tools she uses every day are probably going to surprise you.

[00:03:38] Avery Smith: What type of tools are, are you using? People might assume it's fang, so you're doing like, I don't know, AI programming in like secretive assembly code language or something like that. But what, what tools are you actually using on a day-to-day basis?

[00:03:51] Jen Hawkins: Um, surprisingly, you know, I thought exactly what you said.

[00:03:55] Jen Hawkins: I'm like, oh my gosh, what am I getting into? But yeah, I use Excel all day [00:04:00] and Tableau. So that's it. Um, I do have the opportunity to use Python, you know, to maybe do automation, but there's another person that is like the go-to. So I just talked to him about what I wanna do and, you know, still learn from him.

[00:04:14] Jen Hawkins: Because I want to be the one that, you know, knows how to do it. But yeah, just Tableau and Excel.

[00:04:20] Avery Smith: It's amazing how much you can do with, with Tableau and Excel. Mm-hmm. Like those are are really great tools and you can do so much with them. And, and to me it's not a huge surprise.

[00:04:28] Avery Smith: 'cause I also worked for a big corporation, I worked for, for Exxon. Um, and like the companies of America are built on Excel. Like it's, there's a lot of Excel, um, and Tableau's. Awesome. So that, that makes a, a lot of sense. And what you

[00:04:42] Jen Hawkins: can do with Excel. Like I made all these macros and I used to cut three hours of time by doing like just macros.

[00:04:51] Jen Hawkins: And they're like, how did you do that? You're amazing.

[00:04:53] Avery Smith: That's, that is awesome to hear and I'm glad to hear the macros alive. I wasn't sure. Yeah, so that's perfect. And I also like that you're learning, [00:05:00] like you're, one of the things we talk about in the accelerator program is getting your foot in the data door, like getting, getting your, just any job we can in the data world.

[00:05:07] Avery Smith: And then getting paid to learn, because right now you're getting paid a fairly handsome salary. And like you said, you're doing new things weekly. Uh, yeah, weekly. And you're doing new things in Excel. You're doing new things in Tableau, new things in Python. And that knowledge grows with you, you know, so you can get a promotion at your job, like you could potentially become the Python person in, in your group.

[00:05:26] Avery Smith: And basically those skills you'll always have with you and they'll compound the, the rest of your career and you're getting paid to learn them now. So that's a great option for you.

[00:05:34] Jen Hawkins: It is. I agree. I agree a hundred percent. And, um, and I love it. I love the work. To me it's like, it feels like it's too easy.

[00:05:42] Jen Hawkins: I'm like, okay, where's the hard stuff coming? You know? But it's, it's really not as difficult as you think. I mean, there, there are some jobs. That are very difficult. But again, what gives you an indicator is how long that job description is. That's how you know how much you'll [00:06:00] work. Pick the small one like I did.

[00:06:02] Tableau and excel, even at a tech giant like Apple. And I'm hoping that gives you some serious confidence that you're learning the right tools right now. Excel, sql, Tableau. These things are really important. You can catch the full episode with Jen using the show notes down below. Okay, next, let's get into someone else who just pivoted into data analytics, and his name is Ryan Ponder.

[00:06:23] Ryan went through my accelerator program as well and was able to pivot from a mortgage guy to becoming a data analyst, despite not having any degree, no bachelor's degree whatsoever. Let's hear what he is currently using.

[00:06:35] Avery Smith: What type of tools are you using on the job?

[00:06:37] Avery Smith: Yeah, primarily right now, um, I'm using SQL Server and, and Tableau in most of my work. I'll, I'll start in sql, uh, get queries, run, get my data structure the way I want, and then I'll take it over to Tableau and then and visualize there. But very soon we are actually migrating everything to Snowflake.

[00:06:54] Avery Smith: So I've gotten to do a lot of work, uh, in Snowflake. The team that I'm on is directly [00:07:00] responsible for the entire Veterans United Company moving to Snowflake. So, uh, kind of gotten to test some of that stuff, which has been really exciting as well.

[00:07:07] Avery Smith: Super fun. I love that. Sql, Tableau, they're always popping up.

[00:07:10] Avery Smith: Those are, those are two of the most used, uh, data skills out there.

[00:07:14] Ryan is living proof that SQL plus a BI tool like Power BI or Tableau is pretty much all you need. You don't need like a hundred different tools, and I love that he's now getting paid to learn Snowflake on the job. I mean, how freaking cool is that? So. That's the tools they use in the mortgage industry, but what about the Slurpee industry?

[00:07:32] Alex Sanchez went from a math teacher in high school to business data analyst after my bootcamp, and he uses a somewhat ancient data tool and a tool that's probably the data tool king. Let's listen

[00:07:44] Avery Smith: What tools are you using specifically?

[00:07:45] Alex Sanchez: So, I mainly use Excel and Access.

[00:07:49] Alex Sanchez: There's another software we use, and I'm using it more now, that I've, That have gained more responsibilities and then we're just about to start using.

[00:07:59] Alex Sanchez: I don't even remember what it's [00:08:00] called, but it's an agile.

[00:08:02] Alex Sanchez: I mean, that's how much like we're in the beginning of it.

[00:08:04] Alex Sanchez: And even even the senior analyst that I'm working with and the manager above there.

[00:08:08] Alex Sanchez: It's like, we're going to learn this together and you're going to be a part of the creating these new processes, like these, this new system.

[00:08:16] Alex Sanchez: So that got me excited because one, you know, I want the responsibility, but two, like, this is going to be used forever now.

[00:08:21] Alex Sanchez: Like, whatever we figure out, like, people along the line are going to be using this.

[00:08:26] Alex Sanchez: That's kind of cool.

[00:08:27] Alex Sanchez: And yeah, so, so I definitely used Excel when I was a teacher, right?

[00:08:31] Alex Sanchez: Like, we use data grades the TEKS, like we call them here in Texas.

[00:08:35] Alex Sanchez: Like, there's a bunch of data that we use on top of teaching.

[00:08:40] Alex Sanchez: So when I'm over here and they tell me to do something, right, like get this report change the formatting send it out to other groups, it's like, I used to do that on top of teaching, like, nobody ever thinks about, hey, this teacher is, is working with all these spreadsheets, they just think like, oh, they're in front of these, the students, they grade, that's [00:09:00] it, you know, it's like, so now I get to focus my entire time on just So that, yeah, that's pretty cool.

[00:09:06] Avery Smith: Yeah, that's great.

[00:09:07] Avery Smith: I think that's awesome.

[00:09:08] Avery Smith: And I'm so excited that they're trusting you more and I'm sure you're going to, to give them a lot of dividends.

[00:09:13] Now after listening to that and you're like, man, I've never even heard of access. It's basically just an old crappy version of SQL that Microsoft used to use. But I used it a decent amount when I worked at Exxon. So it still exists, especially in like these large companies. But yeah, other than that, access and Excel, that's how you analyze Slurpees and gas sales.

[00:09:32] You can catch my full interview with Alex by clicking on the YouTube card or going to the show notes and clicking the link down below.

[00:09:37] Now this next analyst has worked for a lot of companies and he's basically a healthcare analytics expert. So if you're interested in analytics and healthcare, please take notes because you're gonna get some great insights that might be key for your data career, especially if you want to get really advanced technically.

[00:09:51] Avery Smith: I wanted to ask you something you mentioned earlier, uh, in terms, in terms of tooling, data, tools that you guys use.

[00:09:57] Avery Smith: You know, you mentioned fabric, you mentioned [00:10:00] Snowflake. We've, we've talked about power, bi, sql, Tableau. Do you guys do, like, do you guys have like a data stack that you kind of stick to? Or, or do you kinda have to be flexible for what your, your customers and clients want?

[00:10:10] Jason Bryll: We typically recommend Microsoft, and so historically it's Azure, Azure Data Factory, the tool set.

[00:10:18] Jason Bryll: They've rebranded a lot of it as fabric a a one data stop analytics platform. Um, we're a big fan of fabric. We've been able to do a lot of really impactful things with it every, and we do everything from the data engineering side, adjusting the data, whether it be. Connecting and doing like snowflake mirroring is available now, stuff like that.

[00:10:34] Jason Bryll: But connecting to different data sets, piping that data over, storing it, analyzing it, um, writing SQL code against it, and then the BI application itself. Power bi. Power BI has really taken a big leap. In the past, I'd say even like two years, maybe three years, like a massive leap in popularity. I think it's 'cause so many people have, you know, Microsoft Office.

[00:10:55] Jason Bryll: But we've done a lot of work with Tableau as well. So we're also Tableau experts and we've [00:11:00] implemented Tableau at many other customers and clients love Tableau, love Power bi. I highly recommend those two simply because of the opportunities that you can find, people are using those applications. That doesn't mean that I don't think there's value in other bi applications and tools.

[00:11:17] Jason Bryll: There's a a bunch of 'em out there, but I would say the majority of what we see in the industry is Microsoft Fabric, power bi. Tableau. Those are the two that we see the most of the other things that we see in the industry right now, we do work with Snowflake and we do work with Databricks. Um, those are two cloud platforms that I, I think are also worthy of, of note, but for the most part we're recommending Microsoft Fabric with our customers who don't already have something.

[00:11:41] Jason Bryll: And if they do, usually Microsoft, I would say. Probably 80% of our clients, if I think about it, uh, are using the Microsoft BI platform.

[00:11:50] Avery Smith: It, it makes sense. A lot of, a lot of people already have Power BI included in like their giant enterprise Microsoft deal. Mm-hmm. When I was at ExxonMobil, we had [00:12:00] Power bi, uh, available I think to, uh, a majority of customers just from the Microsoft deal that we had, but we still used a fair amount of Tableau as well.

[00:12:08] Avery Smith: Although those licenses were a little bit harder to come by as, as an analyst. So yeah, I, I think either of those are, are great things to, to build upon and, and learn as as well.

[00:12:18] So, like I said, Jason is a little bit more advanced, so that's why you hear him talking about Azure and Fabric. That's not necessarily the case for entry-level data analysts, but he's also dealing with a large enterprise clients all the time that are super entrenched in the Microsoft system. So that.

[00:12:32] Kind of makes sense that he's using these tools. A lot of these companies, you need to get the data all in order. First, using like a data pipeline. That's a lot of data engineering. You gotta get it organized and cleaned and everything. And then once it's there, you can just show the trends in Tableau or Power bi.

[00:12:46] Simple enough. Now this next financial analyst went from musical therapy, which is something I had never heard about before. When she joined my accelerator program to coming out the other end with a financial data analyst job, and she was recently promoted to senior data [00:13:00] analyst at Humana.

[00:13:00] So congrats, Erin. Let's listen to what she uses at Humana.

[00:13:04] Avery: what type of tools are you using on a day-to-day basis?

[00:13:07] Erin: Yeah, so biggest one is sql. Um, we use SQL Server, um, and kind of the whole like Microsoft Suite, all of that.

[00:13:16] Erin: Um, lots of excel for the kind of like financial part of it. Um, but most of my analysis and most of the testing that we're doing is within sql. Um, and yeah, that's been, it's been really fun to kind of. Take, uh, the skills that I know, like, uh, just in my own little, like, simple projects into, you know, actual like millions and millions of rows of data.

[00:13:43] Erin: Um, and, you know, see, see how it translates.

[00:13:47] Avery: Yeah, I'm sure some of it is very similar, like, like you kind of have the base for it, but it's probably like you're doing things you might not have necessarily expected. Um, and using things kind of in a new way with this, with this new application.

[00:13:59] Erin: Yeah, there and [00:14:00] there's a lot of, um, kind of logical, like analytical thinking.

[00:14:05] Erin: Um, and you know, that's part of the learning curve of, of. Going into, you know, this specific industry, um, like healthcare. I thought, you know, being in the hospital every day, I thought I knew all of the acronyms, um, that came with like the medical, you know, field. Um, but apparently I didn't. Health insurance is like totally different.

[00:14:25] Erin: So, um, yeah, lots of acronyms. Um, lots of kind of the, the logical analytical thinking to get from point A to point B and then figure out how to get there in sql. So,

[00:14:37] Avery: Yeah. Okay. I love that.

[00:14:39] Woo woo, SQL and excel for the win, even at a Fortune 50 company. I hope you're seeing a pattern here. There's a trend, whether it's a Fortune 5, 50, 500 or 5,000 company. You're using these similar tools, right? You're using Excel, you're using Tableau. Let's get to the seventh. This is a data analysis Tesla, and I'm hoping I'll give you a little glimpse into what [00:15:00] it's like to actually work at Tesla. You know, what tools are you actually going to be using?

[00:15:04] Avery Smith: while you're at Tesla, what tools did you use the most?

[00:15:07] Lily BL: Um, I think I used Jira the most and excel for the validation.

[00:15:11] Lily BL: Um, I got heavier into the administrative side of, of the software because for Tableau and Jira I was bringing in add-ins to make them more functional for analytics. Uh, so for companies, uh, you have to connect the Tableau software to what, wherever your data is in the company. When you use Tableau as an individual user, you just connect it to your worksheet.

[00:15:34] Lily BL: You can't connect it to something else if you have it, but typically you just use a worksheet. So that was different. And it was a full host of security clearances. Um, so I did a little bit of the administrator stuff, but, uh, JIRA and Excel round my validations.

[00:15:49] Avery Smith: That's awesome. I think that's true. And, and you mentioned JQL.

[00:15:52] Avery Smith: Is that kind of like SQL or how, how are those related?

[00:15:55] Lily BL: Yeah, so it's very similar to the commands in sql, so that's why I was able to [00:16:00] learn it pretty quickly. Uh, but then, um, some things are specific. It uses a lot of, uh, a lot more keywords than you would expect, and they're different. Um, the software itself does try to help you.

[00:16:12] Lily BL: Like it lets you click on buttons and produces the code for you. Uh, to an extent, but then you have to have modifications. So I would allow the software to allow me to click to build some of the stuff, but then I would review it and determine, oh, it still needs this functionality, or this, or this other group of people.

[00:16:30] Lily BL: And you would have to manually put that into the existing code to make it function

[00:16:34] Avery Smith: super neat. So it's basically SQL for Jira, and they try to make it a little bit easier for you to actually write the code.

[00:16:41] And there you have it folks.

[00:16:43] Seven real world data analysts and the tools they are using every day. Now, if you want an exact list with percentages of the top five, most in mandated tools based on real data, where I scraped literally 3000 data jobs and did all the Python myself to figure this out. It's been viewed over 73,000 [00:17:00] times already, and I think you're really gonna enjoy it.

[00:17:02] So you can click right here on YouTube, or you can find the link in the show notes down below. I think it'll be a perfect companion to this episode. Thank you guys for listening, and we'll see you in the next one.