Day in the Life of a Data Analyst (Work From Home) | *Realistic*

Summary

This video offers a glimpse into the daily routine of a data analyst. Starting with morning coffee, the analyst dives into a project involving client data to answer specific questions about service and insurance revenue. Using Excel for initial data inspection, the analyst then employs Tableau for data visualization, focusing on revenue generation and payment speed. The process includes exploring data, creating charts, and crafting a detailed report with findings and insights, which is then shared with the client via email. The video provides a clear understanding of a data analyst's work, especially client interactions.

Takeaways

• ☕ The day starts with a coffee ritual to kickstart the data analyst's workday.
• 📅 The analyst is working on a Saturday, dedicating time to a data analytics project.
• 📝 The project involves revisiting an old dataset to answer new client questions.
• 💼 The client's business involves services that can be paid for by customers or insurance.
• 💵 The client seeks to identify the most profitable services and the best insurance providers in terms of payment speed and volume.
• 📊 The analyst uses Excel for initial data examination, ensuring no nulls or empty values.
• 📈 Tableau is the tool of choice for data exploration and visualization due to its user-friendly drag-and-drop interface.
• 📊 The analyst prefers Tableau over coding for quick analyses and dashboard development.
• 📑 The client requires a static report rather than an automated dashboard.
• 📊 Data exploration in Tableau involves creating charts to identify trends and patterns.
• 📝 The final step involves creating data visualizations to answer the client's specific questions and documenting findings in an email.

Q & A

What is the data analyst's routine on a typical Saturday?

The data analyst starts their day by making coffee and then heads to their office to begin work on a new project involving data analysis.

What does the data analyst need to do for their client?

The data analyst needs to analyze a dataset to answer questions about which services and insurances bring in the most money and which insurance pays the fastest.

How does the data analyst ensure the dataset is complete and accurate?

The data analyst checks every column in the dataset for nulls or empty values and contacts the client for clarification if there are any columns they do not understand.

What tools does the data analyst use for data exploration?

The data analyst uses Tableau for data exploration, a popular data visualization tool that allows for quick analysis and dashboard development.

Why does the data analyst prefer Tableau over other tools?

The data analyst prefers Tableau because of its user-friendly drag-and-drop interface and the ability to connect directly with Excel files or cloud data warehouses.

What is the difference between Tableau and Power BI according to the data analyst?

Tableau requires a paid license, while Power BI is mentioned as potentially being free, although the data analyst does not use Power BI in this scenario.

What does the data analyst do after completing data exploration?

After data exploration, the data analyst creates data visualizations to answer the client's questions and then compiles these into a document to be sent to the client.

How does the data analyst handle the client's request for a static report?

The data analyst develops visualizations in Tableau and puts them into a document, rather than creating an automated dashboard or report.

What kind of insights does the data analyst provide to the client?

The data analyst provides insights such as which services are most profitable, which insurances bring in the most money, and which insurances pay the fastest.

How does the data analyst communicate their findings to the client?

The data analyst screenshots the charts from Tableau, writes their thoughts and findings below each chart, and constructs an email to send to the client detailing the answers to the questions.

What does the data analyst do after sending the report to the client?

After sending the report, the data analyst spends the rest of the day relaxing, possibly watching TV.

Outlines

00:00

📊 Day in the Life of a Data Analyst

The video script describes a day in the life of a data analyst starting at 8:00 AM with coffee and heading into the office. The analyst begins a new project, which is actually revisiting an old project with new questions. The client is seeking answers about which services and insurances bring in the most money and which insurance pays the fastest. The analyst plans to use Excel for initial data examination, ensuring no nulls or empty values, and Tableau for data exploration and visualization. The analyst prefers Tableau for its user-friendly drag-and-drop interface and because it can handle both local Excel files and cloud data warehouses. The client is not requesting a dashboard or automated reports; instead, they want straightforward answers to their questions. The analyst will develop static visualizations and compile them into a document to send to the client. The script also mentions the possibility of using Python for automated reports if needed.

05:01

📈 Creating Data Visualizations for Client Insights

The second paragraph details the process of creating data visualizations to answer the client's questions. The analyst focuses on creating simple visuals to determine which service brings in the most money and which insurance is the most lucrative. The visuals include bar charts with labels showing the total amount per service and the total invoice count and payment speed for each insurance. The analyst emphasizes the importance of understanding not just the total revenue but also the popularity of the service and the average invoice amount. After creating the visuals, the analyst takes a break before constructing an email to the client with screenshots of the charts and written notes on findings, trends, and patterns. The email also includes next steps and considerations for the client. The script concludes with the analyst sending the email and planning to relax for the rest of the day, having completed the task in about six hours.

Keywords

💡Data Analyst

A Data Analyst is a professional who collects, processes, and performs statistical analysis on data to help businesses make informed decisions. In the video, the narrator is a Data Analyst who is working on a project to analyze a dataset and answer specific questions for a client. This role is central to the video's theme as it showcases the daily activities and responsibilities of a data analyst.

💡Dataset

A dataset is a collection of data, often organized in a structured format such as a table or spreadsheet. In the script, the Data Analyst refers to a dataset provided by the client, which they need to analyze to answer questions about the client's services and insurance payments. The dataset is the primary material the analyst works with to provide insights to the client.

💡Excel

Excel is a widely used spreadsheet program for organizing, analyzing, and presenting data. The video mentions that the Data Analyst opens the data in Excel to check for nulls or empty values, indicating that Excel serves as a preliminary tool for data cleaning and inspection before deeper analysis.

💡Tableau

Tableau is a data visualization tool that allows users to create interactive and shareable dashboards. In the video, the Data Analyst uses Tableau to explore the data and create visualizations to answer the client's questions. Tableau is highlighted as a user-friendly alternative to coding for data analysis and visualization.

💡Data Exploration

Data Exploration is the process of analyzing data to discover patterns, trends, and relationships. The script describes the Data Analyst's data exploration phase where they use Tableau to look for insights in the dataset. This phase is crucial for understanding the data and formulating the answers to the client's questions.

💡Dashboard

A dashboard is a user interface that displays the most relevant information on a single screen, often used for monitoring and analyzing key performance indicators. Although the client in the video does not request a dashboard, the Data Analyst mentions it as a tool they might use for automated reporting, showcasing its relevance in data analysis.

💡Python

Python is a high-level programming language widely used for data analysis and automation. The Data Analyst mentions using Python for automated reporting in the past, indicating its utility for creating dynamic and automated data analysis reports, which contrasts with the static report they are creating in the video.

💡Insurance

In the context of the video, insurance refers to companies that provide coverage for the client's services. The Data Analyst is tasked with analyzing which insurance companies pay the most and fastest, which is a key concern for the client's business operations and financial planning.

💡Visualization

Visualization in data analysis refers to the graphical representation of information to communicate findings effectively. The Data Analyst creates visualizations such as bar charts to represent the data and answer the client's questions. Visualizations are essential for making complex data easily understandable.

💡Report

A report in this context is a document that presents the findings of the data analysis. The Data Analyst constructs a report by screenshotting charts and writing their findings and recommendations in an email to the client. The report is the final deliverable that communicates the insights derived from the data analysis.

💡Client

A client in this video is the business entity that hires the Data Analyst to perform data analysis services. The Data Analyst's work is centered around answering the client's questions, which include identifying the most profitable services and the fastest paying insurance companies. The client's needs drive the entire analysis process depicted in the video.

Highlights

Starting the day with coffee at 8:00 AM before beginning data analytics work.

Beginning a new project involving revisiting an old dataset to answer new questions.

The client's service can be paid by the customer or insurance, or a combination.

Client seeks to identify the most profitable service and insurance provider.

Client also wants to know which insurance pays the fastest.

First step is to open the dataset in Excel and check for nulls or empty values.

Using Tableau for data exploration due to its user-friendly drag-and-drop interface.

Tableau allows for quick connections to cloud data warehouses.

The client prefers a static report over an automated dashboard.

Python would be used for automated reporting, but a static report is needed for this task.

Data exploration in Tableau involves creating visualizations to identify trends and patterns.

Tableau's calculated fields allow for creating custom columns similar to Excel formulas.

Taking a break to play with dogs before creating data visualizations for the client.

Creating simple bar charts to answer the client's questions regarding services and insurance.

Analyzing both the total amount billed and the number of invoices to understand service popularity.

Presenting findings in an email with screenshots of charts and written notes.

Discussing insurance schemes that pay quickly and bill a lot, as well as those that do not perform well.

Highlighting services with high average invoice amounts and those with lower ROI.

Wrapping up the workday after sending the report to the client.

Transcripts

It is a Saturday.

I have a lot of data analytics work, so I thought I'd do a day

in the life of a data analyst.

Okay, so it's about 8:00 AM and I am just going to make some coffee.

before I start my day.

Okay, coffee's done.

I'm just going to go into my office.

So I am starting a new project today, technically a new project.

It's actually an old project, but answering some new questions.

So on my notepad here, I've written the stuff that I need to do.

and in a nutshell, what I need to do is that the client of mine is

asking to answer a few questions.

I have seen the dataset before, and what I need to do is just have

a look at the dataset, analyze it and answer the questions.

What this client does is the client provides a service to the customers.

And the service can either be paid by the customer themselves or by

the insurance or maybe a bit of both depending on their insurance cover.

So what the client wants to know is they want to know what service brings in the

most money, and they also want to know what insurance brings in the most amount

of money and what insurance actually pays the fastest, because obviously they

would want to work with that insurance provider if they bring in the most

money and if they pay quite quickly.

There's some other questions that I need to answer, but for the purpose of

this video, I'm not going to cover it.

So the first step here is that I am going to open the data and

just have a look at it on Excel.

So what I generally do is I actually go through every column in the data set

to make sure that it's fine, make sure that they are no nulls, no empty values.

If they are any columns that I don't understand, then I will just send an

email to the client to contact them just to ask for more information.

So, now that I'm done, what I'll do is I'll actually pop the data into Tableau,

which is what is on my screen here.

And I am going to start my data exploration.

So in terms of data exploration, I use Tableau.

Tableau is a popular data visualization tool in data analytics.

And whenever I want to do a quick analysis, develop dashboards, do my

own data exploration, I use Tableau.

You can also use Power BI.

I think Power BI is free.

Tableau is not.

You do have to pay for a license.

There is obviously a free trial as well.

And mainly why I like it is because I can just drag and drop the Excel file in here.

Or if I am working on a cloud data warehouse.

I can just quickly connect to a cloud data warehouse as well.

So the client doesn't want a dashboard.

They don't want anything automated.

They just want their questions to be answered as it is in this point in time.

if I wanted to do something automated, I would use something called Python,

which I have done an automated report for this client before.

That's if I wanted something automated, but I don't, so I'm

just doing a static report.

So I'll just develop the visualizations on Tableau, probably put them in

a document and send to the client.

so I'm just going to do some data exploration now.

So, like I said, the reason why I like Tableau is it is quite drag and drop.

So it's quite user friendly.

Sometimes I don't want to code because it is a bit too technical.

Whereas Tableau, you just have to drag and drop elements on your page and

you know, your charts are created.

the only thing in Tableau that you do.

Coding is almost formulas, like Excel formulas.

You have calculated fields in Tableau, and that's where you can create your

own custom columns based on mathematical operations, et cetera, of other columns.

So now what I'm going to do is play around a bit with this and try

and answer the client's questions.

so my data exploration generally looks like this.

And I'm just showing you example charts.

I can't actually show you the actual data and ideally why I do data

exploration and the charts are quite simple is because I myself am trying

to look for trends and patterns.

Once that is done, then I can actually choose the charts that I

like to show to the client, and then I'll format the charts, et cetera.

It is terrible and simple because it's just for my eyes.

And then when I'm ready, I will choose data visualizations to show the client.

Now I am going to take a break, play with my dogs, and then I can

build some data visualizations now that I've done my data exploration.

Okay, so I have done my data exploration and now what I am going to do is start

to create the data visualizations to actually show the client.

So the two I'm going to create are quite simple to answer the

questions that I need to answer.

And then from there, I will create a document that details

the answers that I have.

Okay.

So that is done.

I now have two visuals that I will use to answer the questions

that the client has given me.

The first one being what service brings the most amount of

money, which I can show you now.

right.

So something like this over here, just a simple bar chart.

with some labels.

So per service, we have the corresponding amounts So I think it was also

important to actually understand the amount of invoices they get as well as

the average amount per invoice, just so that they can see what services

bring in the most amount of money, but also how popular that service is,

you know, because we can't have a service.

That is billing 10, 000, but you know, you may have only done it two clients

or to customers, whereas you can have a service billing 50, 000 and you've done a

hundred invoices or a hundred customers.

But I feel like this chart sort of gives a good representation

of what services does well.

So they can have a look at it and pick out the interesting ones.

The next question, which is what insurance brings the most amount of money.

Also done over here.

So similar method here.

I just have the actual, insurance ID.

I then have the total invoice count and then the actual amount and the

bar chart just represents how long each insurance takes to pay in days.

So that very top bar is about 127 days, which is not good.

So ideally what the client will do is have a look at this and see what

insurance bulls a lot in terms of amount, but whether or not they take quite long

to pay in terms of the actual bars.

Um, again, all of this is randomized.

So I'm sorry if the results don't make sense.

I just multiplied it by a random factor because this is actual client data.

so now what I'm going to do is actually screenshot these charts and put them

in a document and actually construct an email to send to the client.

So,

What I do in the email is I screenshot the chart and then below it I just

write my thoughts and my findings.

So that includes the question I'm answering, what I

found, and some next steps.

So that's what I always do.

I can't actually show you that because it's obviously real data, but my email

for it was about a page and a half.

Of written notes on it, just including things like useful trends, patterns.

I spoke about the insurance schemes that do really well.

So they pay quickly and bill a lot.

And then I spoke about the insurance schemes that don't do very well.

I spoke about the different services that seem to be quite popular.

So where they are getting good numbers.

And the average amount per invoice is high, but I also spoke about services

that don't seem to get that great ROI.

So where maybe they're doing big numbers, but the average

amount per invoice is quite low.

So those are the things that you would need to consider

when you write your report.

And now that that's done, all I'm going to do is actually press send.

And it has now been sent.

About six hours in, which I know doesn't feel like it on a YouTube video,

But the rest of the day, I'm probably just going to chill and maybe watch some TV.

Thank you so much for watching.

I hope this helps and gives you a bit more of a clear idea of what

a data analyst does, especially when you're working with a client.

Thanks so much.

Please like comment and subscribe.

Bye.