Python has rapidly risen to become the most used general purpose programming language for data analysis, data engineering, and data science. But why?
PYTHON IS BEGINNER FRIENDLY
Python was designed with readability and ease of use in mind. With a handful of lines of code, Python programmers can read in data from a database, manipulate data, calculate metrics, feed them into a machine learning model, and send the outputs to wherever they might need to go – a spreadsheet, a dashboard – really – just about anywhere.
Becoming a Python expert takes years, but if you focus on learning the foundational building blocks of the language, you can become a productive analyst in a few months.
PYTHON'S ANALYTICS LIBRARIES ROCK
It’s critical to learn the syntax of Python first. Once you do, you’ll be able to effectively utilize Python’s amazing data analysis libraries.
Do you need to aggregate, pivot or summarize data? Try Pandas.
Need to explore your data visually? Perhaps with custom charts that aren’t easy to create in your current visualization tool? Try Matplotlib, Seaborn, or Plotly!
Need to build predictive models, like linear regression or random forests that can help automate decision making? scikit-learn has you covered.
This is just the tip of the iceberg. There are also libraries for scraping data from the web, accessing SQL databases, and many others that can help you automate your analytics workflow.
PYTHON'S COMMUNITY IS AWESOME
Thanks to Python’s massive userbase – you won’t be alone. More people are learning Python today than any other programming language. When there’s a bug in your code – odds are someone else has encountered the same problem and asked for help on a community forum.
Want to explore a new library? You’ll likely find a step by step guide on how to implement it on Towards Data Science, KDNuggets, or one of many other analytics and Python communities.
And of course, there are many great platforms that offer focused classes that will equip you with the tools you need to start analyzing data with Python.
PYTHON IS EXTREMELY FLEXIBILE
The capabilities of Python extend well beyond analytics. One of the most underrated aspects of Python as a tool for analysts is its ability to glue together different tools in our analytical workflow. Yes, the libraries I mentioned above are exciting – but imagine how much time you could save if you could write SQL queries, pass them to a database, retrieve your data, clean it, and push it into an Excel sheet with a single click!
Python can read and write to SQL databases, flat files, and even create Excel sheets complete with charts and formulas, without even needing Excel installed on your machine. It also has integrations with tools like PowerBI and Tableau.
Do you need to collect more data for your analysis? Want to see if there are any bargains on cars in your area? Scrape the data from the web with Python.
Once you get comfortable with the basics, the possibilities are endless.
PYTHON WILL ACCELERATE YOUR CAREER
In the data world, demand for Python skills has never been higher. Long a staple of Data Engineering and Data Science, more and more analyst roles require Python. If you are looking to change companies, or to a new data role, having Python in your stack will open many doors.
RECAP - WHY YOU NEED TO LEARN PYTHON
Python is a true end-to-end analytics tool. Once you learn the basics, you will quickly turn into an analytics powerhouse as you begin to master its syntax and build proficiency with a few key analytics libraries. Whatever your analysis needs, it can be done with Python.
And if that wasn’t enough – did I mention it’s free?
Ready to learn Python with Maven?
Check out of first course and our full path below:
Python Foundations - the best place to start.
Python Specialist Path - continue here if you're loving the first course.
Happy learning!
-Chris
