4 Steps to learn Python for Data Science | Python for beginners | Python Tutorials | Analytics Leap
4 Steps to learn Python for Data Science
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Learning Python for Data Science is an exciting journey.
However it can be overwhelming especially when you have no experience with Python or Coding. I will list down steps which will not only make this journey easy but also keep you on the right track with some well-placed milestones.
There are tonnes of tutorials/courses/blog-posts available online which will teach you the basics of Python. One of the best ways to start is refer to official Python documentation and follow along the concepts mentioned in the documentation.
Official Documentation: https://docs.python.org/3/tutorial/in...
Don't worry about learning everything in there. Focus on things that you seem to pick up well. You can always come back to the documentation for the harder stuff once you have mastered the easier topics.
Basic Milestones:
After this step, you should be able to:
Install Python & Start python interpreter
Understand the String Data type in Python
Create and modify Lists in Python
Execute for loops, Understand if else statement
Read text files & Write to text files
Good to have Milestones:
Understand functions and parameters
Write your own functions!
Bonus Milestone:
Give yourself a high-five if you can write object-oriented code (Python Classes)
Step 1 - It's time to up your game!
Congratulations for reaching Step 1!
One of the first things you should do in Step 1, is to start using Jupyter Notebook.
Jupyter Notebook is a web based editor which runs in the browser. You can execute python code in the browser and see the output as well in the browser. It is one of the most used tools by data scientists to quickly test their code and share findings with other data scientists.
Jupyter - https://jupyter.org/install.html
Next, you should focus on understanding the Dictionary data type and when to use this data type. A sound understanding of Dictionaries is sure to go a long way in helping you become good at Python and eventually to be a good data scientist.
While at Step 1, also try to learn about Errors & Exception handling in Python. Get yourself familiarized with packages & modules including how to import modules.
Official Documentation: https://docs.python.org/3/tutorial/in...
Milestones:
After this step, you should be able to:
Use Jupyter notebook to execute Python code
Understand the Dictionary Data Structure
Understand Errors and Exceptions in Python
Understand packages, modules, import statements in Python
With these milestones accomplished, You are now on your way to learning the Data science libraries in Python.
Bonus Tip:
Google & Stackoverflow are your friends. Just search for how to do a particular thing in Python and you will be presented with 100 different Ways of doing the same thing! It is one of the easiest ways to take your python learning to next level once you have understood the basics.
Step 2 - Load your armory with Numpy & Pandas.
The Python libraries - Numpy and Pandas - provide the advanced data structures required for working with large scale data. These libraries are also well equipped with functions which can easily operate on the data and do complex numerical calculations in a jiffy!
Both these libraries form the base of data wrangling operations in Python. There is hardly any numerical operation that you won't find in these libraries. Numpy Library provides for a data structure named as 'numpy array' where as Pandas provides for a table like data structure named 'Data Frame'. Your challenge will be to master the Data Frame data structure and the tonnes of operations that can be performed on this data structure. Operations could involve simple stuff like calculating mean, reshaping of data or complex operations involving joining two or more data frames together and filtering values or doing group-by.
To get started, you can refer to the official pages of these libraries:
Numpy - https://www.numpy.org/devdocs/user/qu...
Pandas - https://pandas.pydata.org/pandas-docs...
Milestones:
After this step, you should be able to:
Understand the data structures & operations available in Numpy
Use Data Frame data structure to load data from different sources (excel, text, json)
Perform analysis on loaded data in Data Frame
Index, slice, filter, group-by data in Data Frame
Merge & Join Data Frames
Watch the video for more......
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