Python in Data Analytics: What I’ve Learned So Far
Over the past few weeks in our Python class, I have really started to understand why everyone says Python is the best language for data analytics. Before this course, I thought coding was very hard and only for computer science graduates. But now I see it’s actually beginner-friendly and super useful. Let me share what I’ve learned in a simple way.
What is Python?
Python is a programming language that was created by Guido van Rossum in 1991. The name comes from the comedy show 'Monty Python'. Unlike other programming languages that look very complicated, Python code is easy to read because it’s almost like writing normal English.
For example, in class we started with very simple things like:
That’s it. Just one line and it works. This simplicity is one of the main reasons why Python is so popular.
Why Python is Popular in Data Analytics
From what our instructor showed us and what I’ve seen online, Python has become the favourite tool for data analysts because:
i. It is easy to learn even if you don’t have a tech background.
ii. It has many ready-made libraries (tools) that do the heavy work.
iii. You can do everything in one language - cleaning data, analysing it, and drawing graphs.
iv. It has a very big and helpful community.
In our class, we started with basic things like lists, tuples, and dictionaries. For example:
student = ("Amina", 22, "Data Analytics", "Nairobi")
and converting that into a dictionary. These small exercises helped me understand how data is stored and organised, which is the foundation for analytics.
Important Python Libraries for Data Work
The real magic of Python comes from its libraries. The ones I have come across so far and heard about in data analytics are:
- Pandas – This one is very important. It helps you work with data in rows and columns, just like Excel but much more powerful.
- NumPy – Good for working with numbers and calculations fast.
- Matplotlib and Seaborn – These are used to create charts and graphs to show data visually.
- Scikit-learn – Used when you want to do machine learning (predicting things).
How Python Helps Clean and Analyse Data
In real life, data is usually messy - missing values, wrong formats, repeated information. In class we learned that cleaning takes most of the time in data work.
With Python (especially Pandas), you can remove duplicates, fill missing values, change text to lowercase, or fix dates easily. Once the data is clean, you can then analyse it by finding averages, totals, trends, or comparisons.
We also practised writing simple functions like:
def add_numbers(value1, value2):
return value1 + value2
This kind of practice is helping me think logically and build reusable code.
Real-World Uses of Python in Data Analytics
I have seen that many companies in Kenya and around the world use Python. For example:
- Businesses analyse sales data to know which products are doing well.
- Hospitals use it to study patient records.
- Banks use it to detect fraud.
- Even in agriculture, people use data to predict good planting seasons.
During our lessons, we also looked at sample JSON data from APIs like dummyjson.com. This showed me how real data is collected from websites and turned into useful information.
Why Beginners Like Me Should Learn Python
After just a few weeks, here’s why I believe every beginner should learn Python:
i. It gives quick results. You start seeing what you can do in a short time.
ii. It is versatile, you can use it for automation, web development, or even personal projects.
iii. The learning resources are plenty (Google Colab notebooks we use in class are very helpful).
My Advice to Fellow Beginners
Practise every day, even if it’s for 30 minutes.
Don’t be afraid of errors,they actually help you learn.
Use Google Colab like we do in class because you don’t need to install anything.
Try small projects, for example analysing your own monthly expenses.
Conclusion
Python has really opened my eyes to the world of data. What I like most is that it is not just about coding,it is about solving real problems and making better decisions using data.
I still have a long way to go, but I’m excited about this journey. From the basic tuples and dictionaries we did in the first week to functions and now working towards APIs and CSV files, everything is building up nicely.
If you are thinking of starting, I would say go for it. It may feel difficult at the beginning, but with consistent practice, it becomes enjoyable.
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