This learning path providing two learning experiences in one: it explores the world of data science while at the same time giving you hands-on, practical tutorials on how to use Python at an advanced level.
You will learn what data science is and the methods used for data analysis and statistical inference. A large number of guided walkthroughs show you how to use Python and its features. You will learn how to set up Anaconda and Jupyter Notebook and learn, using real-world examples, how to write Python code in Jupyter, with useful tips within the context of data science.
The learning path then moves on to explore a variety of Python features including loops, classes, variables, stringification, and dictionaries and how to create them — all of which is explained through the use of practical demonstrations.
You will also have the chance to put your Python programming skills into practice. This learning path includes a hands-on lab that gives you a guided tutorial in Python, as well as a lab playground for you to try out anything you feel like in Python. Finally, a lab challenge sets you a task that you will have to complete on your own and without any help — the ultimate test of your programming abilities!
- Understand the fundamentals of data science
- Enhance your programming knowledge with Python
- Know how to analyze data through summary statistics
- Use a range of Python features for numerical analysis
- Explore and visualize data using Python
This audience is intended for:
- People starting their journey in the world of data science
- IT professionals wishing to learn about data analysis and data science
- IT professionals wanting to advance their Python programming skills
To get the most from this learning path, you should already be familiar and comfortable with logical and mathematical thinking, as well as having existing knowledge of programming (variables, scope, functions).
For any feedback, queries, or suggestions relating to this learning path, please contact us at email@example.com.
Learning Path Steps
This course explores what data science is, before moving onto how to use Python in a data science context.
This course explores a variety of Python features in a hands-on way, including functions, loops, dictionaries, and flow control.
This lab provides you with a Jupyter notebook that introduces you to basic concepts in Python by explaining concepts and letting you write and run Python code.
This course delves into the theories across the topics of statistics, distributions, and standardization, as well as the NumPy library.
This Python development playground provides you with a ready-to-use in-browser Python integrated development environment (IDE) to easily play with Python.
This course explores Pandas and shows you how it can be used as a powerful data manipulation tool.
You will put your basic knowledge of Python to work in this lab challenge in order to perform a simple form of data cleansing on text.
In this course, we cover Python Visualization Libraries and Tools, focusing particularly on Marplot and the Seaborn plotting library.
Try cleansing and rationalizing data in this exercise.
Tips for the Python exercise.
Exercise text sample.
Text sample - rationalized version.
Knowledge Check: Practical Data Science with Python
Head of Content
Andrew is an AWS certified professional who is passionate about helping others learn how to use and gain benefit from AWS technologies. Andrew has worked for AWS and for AWS technology partners Ooyala and Adobe. His favorite Amazon leadership principle is "Customer Obsession" as everything AWS starts with the customer. Passions around work are cycling and surfing, and having a laugh about the lessons learnt trying to launch two daughters and a few start ups.