A Day in the Life of a Data Analyst (2023)

Summary

This video script offers an insightful look into the daily life of a data analyst, highlighting their role as storytellers, mathematicians, coders, and business consultants. It covers their morning routines, team meetings, data analysis tasks, report production, code maintenance, stakeholder meetings, and the importance of continuous learning and documentation.

Takeaways

• 🧑‍💼 Data Analysts are considered some of the 'sexiest job titles in the 21st Century', highlighting their importance in the modern workforce.
• 📊 A data analyst's role combines skills of storytelling, mathematics, coding, and business consulting to interpret and present data.
• 🔍 Data analysts use code to extract and analyze data from various sources, aiming to find meaningful insights for the business.
• 🏠 The flexibility of remote work is common for data analysts, with the option for a hybrid model combining remote work with in-person collaboration.
• ☕ Starting the day with a coffee and a morning ritual, such as meditation or exercise, helps set the tone for a productive day.
• 📅 Prep work in the morning involves meditation, checking emails, and staying updated with the latest in data analytics and science.
• 👥 Team meetings are crucial for aligning objectives and prioritizing tasks within sprint cycles for data analysts.
• 🔬 The core of a data analyst's work involves breaking down tasks, processing data, and ensuring data quality through testing and documentation.
• 📈 Data visualization and report creation are essential for communicating findings to stakeholders in interactive dashboards or static presentations.
• 💻 Code maintenance, including writing new code, peer reviews, and tests, is a significant part of a data analyst's daily routine.
• 📚 Documentation is vital for a data analyst to record insights, lessons learned, and findings for future reference and business continuity.
• 🤝 Stakeholder meetings are an opportunity for data analysts to communicate their work and insights to various parts of the business.
• 🔍 Continuous learning and research are essential for data analysts to stay updated with new techniques and advancements in the field.
• 🌐 Networking and engaging with the wider data analytics community can provide valuable insights and collaboration opportunities.

Q & A

What is the significance of being a data analyst in a company according to Tom?

Tom suggests that a data analyst is like the most important person in the company, highlighting the crucial role they play in interpreting and presenting data to drive business decisions.

Why have data-related roles been called some of the 'sexiest job titles in the 21st Century'?

The term 'sexiest job titles' refers to the allure and demand for roles like data analysts and data scientists, which are in high demand due to the increasing importance of data in decision-making.

What are the core skills a data analyst needs to possess according to the script?

A data analyst needs to be a storyteller, a mathematician, a coder, and a business consultant. These roles require the ability to analyze data, extract meaning, and communicate findings effectively.

How does a data analyst typically start their day?

A data analyst might start their day with a morning ritual, which can include meditation, exercise, and preparing for the day by meditating, checking emails, and reading news articles related to data analytics.

What is the importance of team meetings for a data analyst?

Team meetings are crucial for aligning objectives and prioritizing tasks. They help data analysts work collaboratively within sprint cycles towards achieving specific goals.

Can you explain the concept of 'sprint cycles' mentioned in the script?

Sprint cycles refer to a time-oriented structure where data analysts and teams focus on achieving a set of goals within a short, defined period, typically one or two weeks.

What does exploratory data analysis involve for a data analyst?

Exploratory data analysis involves checking for data quality issues such as missing data or incorrect data types. It's about understanding the data to ensure it is suitable for answering the business questions at hand.

How does a data analyst ensure the data quality and code quality are maintained over time?

A data analyst ensures data and code quality by writing tests, documenting processes, and using version control platforms like Git or GitHub to maintain and roll back to previous versions if needed.

What are the different ways a data analyst can communicate actionable insights from data?

Data analysts can communicate insights through interactive dashboards using tools like Tableau, Power BI, or Looker, or by creating static presentations for one-off project findings.

Why is code maintenance an important part of a data analyst's job?

Code maintenance is important for a data analyst to ensure that new code blocks are efficient, existing code is peer-reviewed for quality, and tests are written to maintain data and code quality over time.

How does a data analyst balance working remotely with face-to-face interaction with colleagues?

A data analyst might prefer a hybrid model, where they spend most of their time working remotely but also have the opportunity to come into a central place for face-to-face interaction with peers and colleagues.

What is the role of documentation in a data analyst's workflow?

Documentation is crucial for a data analyst to record their work, insights, and lessons learned, ensuring that the wider business understands the process and findings for future reference.

Why is continuous research important for a data analyst?

Continuous research is vital for data analysts to stay updated with the rapidly changing field of data analytics and data science, learn new techniques, and adapt to new tools and methodologies.

Outlines

00:00

🔍 Introduction to a Data Analyst's Role

Tom, the data analyst, introduces his profession as one of the 'sexiest job titles in the 21st Century.' He explains the multifaceted nature of the role, which includes storytelling, mathematics, coding, and business consulting. Tom's daily routine involves pulling and analyzing data from various sources, often remotely, and he prefers a hybrid work model. His day starts with a coffee ritual, alternating between meditation and exercise for mental preparation. He emphasizes the importance of morning preparation, which includes meditation, checking emails, and staying updated with the latest in data analytics and data science.

05:01

📊 A Day in the Life of a Data Analyst

The script details the daily activities of a data analyst, starting with a team meeting to align on objectives and prioritize tasks within sprint cycles. Tom discusses the process of data analytics, from breaking down tasks, processing data, to testing and documenting findings. He uses an example of explaining customer segment discrepancies by pulling data from sources like SQL databases or Excel spreadsheets, performing exploratory data analysis, and ensuring data quality. The focus then shifts to creating tests for data quality maintenance and documenting processes for clarity and future reference.

10:02

📈 Communicating Data Insights and Code Maintenance

After addressing a business question, the data analyst's role involves creating data visualizations and reports to communicate findings to stakeholders. Tom mentions the use of interactive dashboards like Tableau, Power BI, or Looker, and the creation of static presentations for project end findings. He stresses the importance of combining charts and text for comprehensive reports. The afternoon is dedicated to report production, choosing the right visualization techniques, and ensuring the inclusion of both visual and textual elements. Code maintenance is also a key part of the job, with Tom discussing the process of writing new code, peer review, and the importance of tests and version control for maintaining code and data quality.

🤝 Stakeholder Engagement and Continuous Learning

Stakeholder meetings are an irregular but crucial part of a data analyst's day, where findings and work progress are communicated to various business departments. These meetings require clear messaging and honesty about any encountered problems. The data analyst's ultimate goal is to facilitate data-driven decision-making within the company. Post-meetings, Tom focuses on documentation, ensuring that all work is recorded for future reference and business understanding. He also emphasizes the importance of continuous learning and staying updated with new techniques in the rapidly evolving field of data analytics, suggesting various resources for ongoing education and professional development.

Keywords

💡Data Analyst

A data analyst is a professional who collects, processes, and interprets data to help businesses make decisions. In the video, Tom, the data analyst, showcases the multifaceted role, emphasizing the need for skills in storytelling, mathematics, coding, and business consulting. The script illustrates this by detailing the tasks Tom performs, such as pulling data from various sources and presenting findings visually.

💡Data Analytics

Data analytics refers to the process of examining data sets to draw conclusions about the information they contain. The video describes it as one of the 'sexiest job titles in the 21st Century' and explains that a data analyst uses coding to analyze data, extract meaning, and present findings, which is central to the video's theme of showcasing a day in the life of a data analyst.

💡Remote Working

Remote working is the practice of working from a location outside the traditional office setting. The script mentions that data analysts can perform their job from anywhere, highlighting the flexibility of the role. Tom prefers a hybrid model, which includes both remote work and face-to-face interaction with colleagues.

💡Team Meetings

Team meetings are gatherings where team members discuss objectives, tasks, and collaborate on projects. In the context of the video, team meetings are crucial for aligning data analysts with other team members and setting goals within sprint cycles, which demonstrates the collaborative aspect of data analysis work.

💡Sprint Cycles

Sprint cycles are fixed periods of time during which specific work is completed in iterations. The script explains that data analysts often work in sprint cycles, focusing on achieving particular goals within one or two weeks, which helps structure their work and align with team objectives.

💡Data Visualization

Data visualization is the graphical representation of information and data. The video emphasizes the importance of creating visual representations of data to communicate findings to stakeholders. Tom mentions using tools like Tableau, Power BI, or Looker to create interactive dashboards that summarize findings.

💡Code Maintenance

Code maintenance involves improving and updating existing code to ensure it remains functional and efficient. The script describes Tom's routine of writing new code blocks, peer reviewing, and writing tests as part of code maintenance, showing the ongoing effort required to keep code and data quality high.

💡Version Control

Version control is a system that records changes to a project's documents, programs, or other collections of information. In the video, Tom uses platforms like Git or GitHub for version control, allowing him to manage changes and revert to previous versions if necessary, which is vital for maintaining data and code quality.

💡Stakeholder Meetings

Stakeholder meetings are discussions with individuals or groups that have an interest or concern in the outcome of a project. The script describes these meetings as atypical and varying, where Tom communicates findings to different parts of the business, emphasizing the importance of transparency and actionable insights.

💡Documentation

Documentation is the process of recording information, often for future reference or to communicate knowledge. The video highlights the importance of documenting work as a data analyst, which helps in remembering what has been learned and sharing insights with the wider business, as mentioned in the script where Tom discusses the importance of this practice.

💡Research

Research in the context of data analytics involves staying updated with new techniques, tools, and methodologies in the field. The script shows Tom engaging in research by revisiting math concepts, exploring new code libraries, and networking with other data analysts, which is essential for professional growth and staying current in the rapidly evolving field.

Highlights

A data analyst is described as a storyteller, mathematician, coder, and business consultant.

Data analysts use code to pull and analyze data from various sources.

The role of a data analyst has been called one of the 'sexiest job titles in the 21st Century'.

Remote work is common for data analysts, with a preference for a hybrid model combining remote work with face-to-face interaction.

Tom's morning ritual includes meditation, exercise, and a specific coffee choice with oat milk.

Prep work for a data analyst involves meditation, checking emails, and staying updated with the latest in data analytics and data science.

Team meetings are crucial for aligning objectives and prioritizing tasks within sprint cycles.

Data analysts break down tasks, process data, and document their findings to ensure repeatable and maintainable processes.

Exploratory data analysis checks for data quality issues such as missing data or incorrect data types.

Data analysts use data visualization and reports to communicate findings to stakeholders.

Interactive dashboards like Tableau or Power BI allow businesses to analyze data as it changes.

Code maintenance includes writing new code, peer reviews, and ensuring data and code quality over time.

Version control systems like Git or GitHub are essential for maintaining code and data quality.

Stakeholder meetings are important for communicating findings and receiving feedback from various business units.

Documentation is key for data analysts to record insights, lessons learned, and findings for future reference.

Data analysts must stay updated with the latest techniques and tools in the field through continuous research.

Networking with other data analysts through meetups and webinars is part of a data analyst's professional development.

CareerFoundry offers a free short course on data analytics for those interested in learning more about the field.

Transcripts

So in a way, as a data

analyst, I'm kind of like the most important person in the company.

Ever wondered what a data analyst does from day to day?

Hi, I'm Tom, and today I'm going to show you exactly that!

So what is data analytics and why is it one of the coolest jobs around?

The roles, around data, such as data analyst and data scientist

have been called in the past some of the "sexiest job titles in the 21st Century".

So what is a data analyst?

Well, there are different ways of answering that question, but at its core,

the data analyst needs to simultaneously be a storyteller,

a mathematician, a coder and a business consultant.

The data analyst uses code to pull data in from various different sources

and then uses code again to analyze those data sources to try and extract

meaning from those data sources

and then present those findings often in a visual way to the wider business.

More and more jobs these days are remote working,

and a data analyst basically can do their job from anywhere.

I personally find I like a hybrid model where I spend most of my time

working remotely, but I'm still able to come into a central place

where I can be face to face physically with my peers and colleagues.

So that's what I'm going to show you today.

So as a data analyst, I always like to start my day

with a good cup of coffee, so let's go and do that now.

- What's your morning ritual?

Great question.

My morning ritual varies between meditation and exercise.

I try and alternate days between meditation and exercise.

- What's your coffee choice?

Oh, what's my coffee choice?

My favorite coffee is a latte with oat milk

so now I've got my coffee.

I like to spend a few minutes doing prep work.

And so what does it mean by prep work?

Well, I like to spend a few minutes every morning meditating to get my mind

in the right space for the day ahead.

I like to check emails from the previous day

to make sure I've closed off any issues that are still outstanding.

And I also like to check news articles and emails to find out what's new

and interesting in the world of data

analytics and data science to motivate myself for the day ahead.

Now I'm done with my prep work.

I'd like to spend half an hour in team meetings.

So these meetings are super important for the data analyst's role,

because often data analysts like to work in team oriented structures,

which means a data analyst will work

with other members of the business or indeed other data analysts.

And these team meetings also provide a time oriented structure.

So data analysts often like to work in what's known as sprint cycles.

That means we like to spend one or two weeks

working on achieving a particular goal or set of goals.

And this morning team meeting is super important

so that the data analysts can align with the other team members

on what objectives are crucial in order to meet those goal or set of goals.

Typically, in that meeting,

the data analysts will look at a backlog of issues that currently exist,

and those issues are related to the objectives that need to be met

and the data analysts

will work with the other team members to prioritize those tasks and

to maybe remove ones that are no longer important or add new ones.

And with the body of tasks prioritized and chunked the data on this can

then make sure that that day is as goal oriented and productive as possible.

So now that we have our team meeting out of the way and we know what goals

we need to achieve,

we're going to spend a couple of hours focusing on data analytics.

In this data analytics section, we'll be preparing the tasks.

That means breaking the tasks

that we've already identified down into subcomponents.

We're going to be processing the data

that's in those tasks required to achieve those objectives,

and we're going to be testing and documenting our processes as we go.

So let's take a specific example.

Say as a data analyst, you're charged with trying to explain a discrepancy

between two different customer segments and you need to use data

in your business to do that.

So the first thing you're going to need to do is pull data from the sources.

What does that mean?

Well, you're going to need to use code like Sequel or maybe Python to pull data

from sources, such a SQL databases or maybe Excel spreadsheets

into a code repository like a Python IDE,

where you can do analysis on the data that you've just pulled.

So now that we have data from our original sources into our code

IDE, we can perform exploratory data analysis on that data.

We might be checking are there any data quality issues?

For example, we might start by looking to see is there any data missing.

We might also check the data quality of the data.

That is that

if we have a name column in our data source,

we might check to ensure that we have no numbers in that column.

Finally, now that we have data from our sources in our code IDE

and we performed exploratory data analysis to ensure that the data quality is high,

we're going to try and answer the business question

that we've been given using the data at our fingertips.

We can go ahead and close this question off as having been solved.

Now, all that's left for us to do is test and document the process.

We have the business question answered.

We need to make sure that it's answerable every time.

So we create a series of tests which ensure that the data quality

is maintained every time we pull data from its original sources.

And we need to document the process so we don't forget what we've done.

At every stage in the process

we document an explanation,

and this will also help us when we start to speak to stakeholders

to explain the reasons behind the actionable insights that we're taking.

Of course, we need to create data visualization,

and we need to create reports which allow us to communicate our findings

to the stakeholders.

But we've been spending a couple of hours on this already

I think it's probably time for lunch

Well, now I'm

back from lunch, and before I get to sleepy this afternoon,

I want to get the main focus of the afternoon out of the way,

and that's going to be producing the reports.

There are many ways a data analyst can communicate actionable insights from data,

but producing reports is one of the core ways that data analysts will do this.

There are normally two ways you're going to want to produce reports, either

interactive dashboards or static presentations.

If you're producing interactive dashboards,

you might be using a typical dashboarding tool like Tableau, Power BI or Looker.

These are powerful apps that allow you to summarize findings from various

different sources and use a variety of different visualization techniques.

These powerful apps allow the business to continue to analyze the data

as the underlying data sources change, and so they're integral part

of any business requirement.

But you may decide to produce a presentation instead.

These are naturally static, which means the underlying data doesn't change,

and so they tend to be used for one off presentations.

When you're trying to communicate

at the end of a project, your findings to the business.

Now you've decided on the format,

you just have to go ahead and produce the report.

And that basically involves

deciding which charts and what text you're going to include in your reports.

Different data sources require different visualization techniques.

So make sure to make the right decision about which charts to build.

Just like a book with only pictures is boring,

and yet a book with only text and no pictures is less appealing.

So in our reports, we want to include a combination of charts as well as text.

This helps

the end business user to fully understand the message that you're trying to convey.

Well, let's move on to the important topic of code maintenance.

If you're a data analyst,

it's almost guaranteed you're going to need to know how to code.

And so

I like to spend the next period of time in my day working on code maintenance.

Well, obviously the first component is writing new code blocks.

I like to code in Python and as a data analyst,

I'll also find myself using a lot of Sequel,

but different companies will require different programing languages.

So don't be confused if you're asked to learn a new language

joining a new company.

If you're looking for a new job, don't forget to look at the job requirements.

They'll often

list the code language that they work in or that they require you to work in.

But my personal tip - don't be put off if you don't already know this code

language.

Learning a code language is kind of like learning a foreign language -

the more you know, the easier it is to learn new ones.

And people will be impressed

if you can already solve problems in your existing code language.

So after having written some of my own code,

I like to move on to the peer review process.

This is where I, along with the other data analysts in the company,

like to work together to check each other's code.

This is an important part of being a programmer

because you're not infallible.

And so having a second pair of eyes checking over your work

not only helps you to spot mistakes, but also helps you to become a better data

analyst in the long term.

Being a good data analyst also involves writing

lots of tests, but don't worry, they're not as boring as they sound.

Writing tests means writing little controls into your code

to help you to check,

not only that, your data quality is maintained over time,

but also that your code quality is maintained over time.

So that bugs don't get introduced into your code base

and so that data quality issues don't get introduced into your data sources.

Finally, good data analyst has to use version control in their work.

This means using platforms like Git or GitHub so that your business logic,

your data quality and your code quality can all be maintained.

And if you make a mistake and write a bug into your code,

you can always roll back to a previous version.

Hey, we've spent quite a lot of time working by ourselves with other data

related people.

Now let's try and focus a little bit on the wider business.

And for that we're going to set up some stakeholder

meetings. Stakeholder meetings aren't a consistent part of your day.

There are some weeks

where you'll find yourself having meetings every day,

and there are some weeks where you get to work on your code or your data

all by yourself for the whole week.

In fact, stakeholder meetings are pretty atypical.

No two stakeholder meetings tend to be the same,

but they do share some general characteristics.

Obviously, your communicating, your findings or your current work

to a wider part of the business

and this can range from meeting with people in the product team,

meeting with the senior management, meeting with finance,

or even meeting with other data members of staff. In these meetings

it's important to have a strong sense of what message you'd like to communicate.

It's also important to be honest with the business.

No one's expecting you to be perfect, and you'll find problems

that you encounter along the way.

So be sure to be honest and transparent with the business.

Finally, the role of a data analyst in the company is to help the business

to make data driven decisions.

So be sure to use the data that you've worked on and the reports

that you've created to make actionable insights and helpful suggestions about

which direction the business can move in OK, now I'm done with my meeting.

I'm going to head back and do some more work.

Well, we're done with stakeholder meetings for today at least.

So let's move on to the next phase of our day, which is documentation.

A good data analyst should always document their work as they're going along.

This helps you to remember what you've learned

and what you've forgotten

so that in the future, when you look back on your work,

not only you know what's been happening, but also the wider business knows

what's been happening. All of the insights that you've derived,

all of the lessons that you've learned and all of your findings can be shared

with the wider business and saved for posterity.

OK, it's 5:00.

It's not going to be the most productive hour of my day.

So instead of doing any hard core work,

I would like to close my day by doing a little bit of research.

It's super important for data analysts to stay abreast of the market of data

analytics, data science, engineering, because this market changes very fast.

There's a lot of new techniques being introduced all the time,

so we need to spend a good amount of time focusing on research.

So where do I go to find good research?

Well, from a variety of sources.

First things first.

Good data analyst needs to be strong in math.

So I will go

back over math over and over again just to help it stay fresh in my mind.

And I can use different mediums for that.

I read articles online.

I love looking at YouTube videos,

or I might try and solve some puzzles on an app on my phone.

Yeah, we're going to link all the resources

that I use in the description below.

Another part of being a good data analyst is checking out new code libraries.

So for example, I'm testing out a new product called Copilot

at the moment that helps me write code cleaner and faster.

Another important part of being a data analyst is meeting other data analysts,

so I like to carve out some time to go to meetups, to go to webinars,

meet other data analysts, and network with the wider community.

So that's a little glimpse into what the typical day of a data analyst

might look like.

Obviously, some days are more meeting heavy, some days are more code heavy, but

I hope this gives you a general insight into what a typical day might look like.

So if that sounded like

something you're interested in, or even if you'd like to find out more,

CareerFoundry has an excellent free short course on data analytics.

The link is in the description below. If you're enjoying content like this,

and you'd like to see more data analytics related stuff,

make sure you subscribe to the

CareerFoundry YouTube channel and click on the notifications as well.

And if you'd like to find out more or if any of the stuff I talked about sounded

too complex,

well, my colleague has made an excellent video,

which is an introduction to data analytics.

Check it out here to find out more information about what

is data analytics, what are the main tasks in data analytics?

And just basically understand the topic a bit better.

Check it out and I'll see you again soon.