Python for Data Analysis Data Wrangling with Pandas
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Data analysis in Python: a step-by-step approach
Python is a very flexible and versatile language that, over the years, has gained more and more credence in the data analysis community. Unlike other languages, such as R, Scala, Matlab or Julia, Python was not designed to perform data analysis and in general scientific and numerical tasks, but this can be seen as an advantage, because with Python you can do...anything.
Statistics show that in 2020, about 66% of data scientists use Python daily and 84% use it as their primary language. It's also interesting to note that around Python a huge and very active community has developed, so if you have a problem or want to collaborate, it's pretty easy to find someone to work with. But how do you do data analysis in Python? Is there anything specific (besides Python of course) that you should master? Let's get right into it step by step in this quick guide.
Basics first: if you don't know Python and/or any data science, start from here
Surely, if you don't know Python but you can program, you should spend some time learning the basics of the language. Python is a fairly easy language to understand, it doesn't have complicated syntax and if you have some knowledge of coding, you can learn it very quickly.
Being a widely used language, there are many tutorials, exercises, books (even free ebooks), videos, that you can use to learn what you need. Keep in mind that, to do data science with Python, you don't need to be a Python pro: unless you need it for other purposes, you won't need to go really deep into its intricacies. Here are some basic courses and resources to learn all the Python you need:
The Hitchhiker's Guide to Python is also
available in tangible book form
available in tangible book form
The official python.org repository where you can download everything about Python
Python tutorial for beginners: a very easy step-by-step course, no basic experience required
Of course, you definitely need to build your data science skills, because otherwise it would be like having a tool and not knowing what to do with it. So you'll need to develop skills in statistics and data visualization, and gather a certain amount of knowledge about the domain you'll be exploring and analyzing.
If you need an introduction to statistics and data analysis (not tied to a programming language), try the Probability Theory, Statistics and Exploratory Data Analysis course.
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