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..
