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You have been asking for this one for a while. Here it is.
Today we are talking about Python. Why it matters for data people and the basic concepts you need to understand before anything else makes sense.
No prior experience needed. If you have never written a line of code in your life, this is for you.
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We hired one colleague for every department.
Last Tuesday, marketing asked Viktor to write the weekly campaign recap, pull performance from Google Ads and Meta, and format it as a PDF for the exec team. Done in four minutes.
That same afternoon, engineering asked Viktor to review three open pull requests on GitHub, cross-reference with the Linear sprint board, and flag anything blocking the release. Posted to private channel before standup.
At 9pm, ops asked Viktor to draft a vendor contract summary from three Notion docs and send it to the team. It was in #ops by morning.
None of them knew the others were using it.
Same colleague. Three departments. That's what changes when your AI coworker lives in Slack, where your whole company already works. It's not a tool one person logs into. It's a teammate everyone messages.
5,700+ teams. SOC 2 certified. Your data never trains models.
"Viktor is now an integral team member, and after weeks of use we still feel we haven't uncovered the full potential." - Patrick O'Doherty, Director, Yarra Web
Why Python?
A few years ago, SQL and Excel were enough to get a data analyst job. Now Python is showing up on almost every job description — and for good reason.
Python lets you do things SQL simply cannot: build machine learning models, automate repetitive tasks, create visualisations, connect to APIs, process thousands of files in seconds.
It is also free, beginner friendly, very readable (some syntax is is just simple English words) and has the largest data community in the world.
And honestly — it is genuinely fun. There is something satisfying about writing 10 lines of code that does something that would have taken you an hour manually in Excel.
Where to write Python
Before anything else — you do not need to install anything.
Go to colab.research.google.com. Sign in with your Google account. Click "New notebook."
You are now looking at Google Colab — a free, browser-based Python environment. You can write and run code right there.
The basics
Variables
A variable is just a name you give to a piece of data so you can use it later.
name = "John"
age = 30
salary = 75000.00
is_analyst = True
print(name) # John
print(age) # 30You are telling Python: remember this value, call it this name. That is it.
Data types
Python has four basic types you need to know:
# String — text, always in quotes
job_title = "Data Analyst"
# Integer — whole number
years_experience = 3
# Float — decimal number
conversion_rate = 0.045
# Boolean — True or False only
has_python_skills = TrueWhy does this matter? Because Python treats them differently. You can do maths with numbers. You cannot do maths with text. Knowing your data types saves you a lot of confusing error messages.
Lists
A list is a collection of items in a specific order.
skills = ["SQL", "Python", "Power BI", "Excel"]
print(skills[0]) # SQL — Python counts from 0, not 1
print(skills[2]) # Power BI
print(len(skills)) # 4 — how many items in the listThere is a lame joke ‘My boyfriend asked me if I love him more than programming. I said 'he is my number 1’, but he doesn’t know that programming in my heart occupies position 0.’
This will help you to remember that counting starts from 0 not 1.
In data analytics you will use lists constantly — to store column names, to loop through values, to filter data.
If statements
If statements make decisions.
salary = 80000
if salary > 100000:
print("Senior level")
elif salary > 60000:
print("Mid level")
else:
print("Junior level")Read it exactly as it sounds: if this is true, do this. Otherwise, do that.
Loops
A loop repeats something. Instead of writing the same thing ten times, you write it once and tell Python to repeat it.
skills = ["SQL", "Python", "Power BI", "Excel"]
for skill in skills:
print("I know", skill)
# Output:
# I know SQL
# I know Python
# I know Power BI
# I know ExcelThis is one of the most powerful ideas in programming. Anything repetitive becomes a loop.
For example, you are working with a text, and asking python to check every word and make a specific changes - that can be a loop.
Functions
A function is a reusable block of code. You define it once, use it many times.
def greet_analyst(name):
print("Welcome to the team,", name)
greet_analyst("Josh") # Welcome to the team, Josh
greet_analyst("Alex") # Welcome to the team, AlexIn data work you will write functions to clean columns, calculate metrics, process files — anything you find yourself doing more than once.
Your first real task
Try this in Google Colab right now.
# Create a list of your top 5 skills
my_skills = ["SQL", "Excel", "Power BI", "Python", "Data Visualisation"]
# Loop through and print each one
for i, skill in enumerate(my_skills, 1):
print(f"{i}. {skill}")If that runs and prints your list — congratulations. You just wrote Python.
What comes next
These are the building blocks. Everything else — pandas, machine learning, automation — is built on top of them.
Speaking of data cleaning: I just published a YouTube video walking through my Python data cleaning tips.
Watch it here → link
And if you want to go deeper with structured lessons and hands-on projects, check out my Python masterclass: ‘Data Analysis with Python’
Keep pushing 💪,
Karina
Data Analyst & Data Scientist
