Introduction to Python for Data Science

Start your journey in data science and learn the basics of Python in this uniquely engaging, hands-on course from Microsoft.

Starts on January 19, 2016

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About this course

Master the basics of Python using fun videos and interactive in-browser coding challenges on the DataCamp platform.
Python is a very powerful programming language, used for many different applications. Throughout time, the huge community around this open-source language has created many tools to efficiently work with Python. In recent years, many tools specifically for data science have been built. Analyzing data with Python has never been easier.
Starting from the true basics, such as basic arithmetic and variables, you will learn how to handle data structures such as Python lists, Numpy arrays and Pandas DataFrames. Along the way, you will be introduced to Python functions and control flow. You will also take a first dive into the world of data visualization with Python and create your own stunning visualizations on real data.
Fun tutorial videos will explain the concepts and you will continuously practice your newly acquired skills through interactive in-browser coding challenges on the DataCamp platform. You'll solve real data problems while receiving instant and personalized feedback that guides you to the solution. This learn-by-doing approach combined with a focus specific for Data Science makes this Python course unique.

What you'll learn

Python language fundamentals: basic syntax, variables, and types
Create and manipulate regular Python lists
Use functions and import packages
Build Numpy arrays and perform interesting calculations
Create and customize plots on real data
Supercharge your scripts with control flow and get to know the Pandas data frame

View Course Syllabus

Course Syllabus

Section 1:
Python Basics Take your first steps in the world of Python. Discover the different data types and create your first variable.

Section 2:
Python Lists Get the know the first way to store many different data points under a single name. Create, subset and manipulate Lists in all sorts of ways.

Section 3:
Functions and Packages Learn how to get the most out of other people's efforts by importing Python packages and calling functions.

Section 4: Numpy Write superfast code with Numerical Python, a package to efficiently store and do calculations with huge amounts of data.

Section 5: Matplotlib Create different types of visualizations depending on the message you want to convey. Learn how to build complex and customized plots based on real data.

Section 6: Control flow and Pandas Write conditional constructs to tweak the execution of your scripts and get to know the Pandas DataFrame: the key data structure for Data Science in Python.