Python is the most used language in data science for a reason. Simple yet versatile, its functionality is perfect for statistics, scientific functions, and mathematics. With digitalization of, well, everything, data is overflowing. In order to understand and form thoughtful analyses of all this data, data scientists need a language that's both powerful and easy to use. Enter: Python for Data Science. In short, Python is:
- An open source language that lends itself well to object-oriented programming
- Easy to use/simple syntax
- Highly adept to work with statistics, scientific functions, and mathematics
- Perfect for quick prototyping
Reasons data scientists love Python for data science are seemingly endless. For example, Python has comprehensive libraries to make data scientists' lives easier when tackling mathematical tasks, data manipulation, statistical visualization, and more.
Great, right? But that’s not all. The reason why The Father of Python Guido van Rossum is passionate about this language, and the whole of programming, is because of the ability to share ideas.
“Typically, when you ask a programmer to explain to a layperson what a programming language is, they will say that it is how you tell a computer what to do. But if that was all, why would they be so passionate about programming languages when they talk among themselves? In reality, programming languages are how programmers express and communicate ideas!” - Rossum
We've rounded up resources to explain Python for data science in full and to help you best communicate your ideas. Check them out below.
If you haven’t yet begun your career as a data scientist, set yourself up for success by learning Python. Python is the most loved programming language among data professionals, and is projected to stay in that position for years to come.
A Byte of Python, written by Swaroop CH, is a book specifically geared towards beginners with no prior experience. Swaroop self-describes this audience by prefacing “If all you know about computers is how to save text files, then this is the book for you.”
It covers the fundamentals of Python like:
- Operators and expressions
- Control flow
- Data structures
- Problem solving
Download your free copy here.
The Data Science Prep Course was created for those looking for an introduction to data science. In 4-6 weeks, expect to learn Python for data science, probability, computer science basics, and data visualization alongside a personal mentor.
No prior coding experience is required, but to be successful, it’s recommended that students are already proficient in high-school level mathematics and are eager to learn more advanced concepts where necessary.
Topics covered include:
Topic 1: Introduction to Data Science & Python
Topic 2: Intermediate Python for Data Science
Topic 3: Foundations of Probability
Topic 4: Computer Science Primer
Topic 5: Exploring Data
Topic 6: Python Case Study
Building a strong backbone in scientific computing is made easy with VanderPlas' comprehensive Python library explainer.
This e-book by Jake VanderPlas is best suited for those with a familiarity with Python. Read this if you're looking to better understand core libraries like IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages.
GeeksForGeeks compiled the end all be all guide to what you need to know about Python for data science. Written for practical use and easy implementation, bookmark this incredibly well-rounded Python resource for later use.
For a condensed overview of the table of contents, expect to go over topics like:
- Python IDEs
- Decision trees
- Forest regression
- Machine learning
- Linear regression
- And much more
Pandas is a software library for Python. Pandas’ particular strength lies in its data structures and operations for manipulating numerical tables and time series. Keith Galli is a recent MIT graduate who loves creating educational YouTube videos about Computer Science, Programming, Board Games, and more. This video by Galli walks you through real-world tasks solved with Python Pandas.
The infinite resources available online don’t always measure up when compared to meeting up with other professionals that share your same passions. With Pydata, you can experience community, conversation, and share ideas. Pydata hosts events across the globe featuring first class data speakers and networking opportunities.
If large events aren’t your thing, Pydata will get you in touch with small meetups in your area. These meetups are centered around topics like:
- Data Science
- Data Science using Python
- Open source Python
- Data Analytics
- Data Visualization
- Machine Learning
- Data Mining
- Big Data
- Statistical Computing
- High Scalability Computing
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