Welcome to the wonderful world of Data Analysis in Python! In this chapter, you'll learn the basics of Python syntax, load your first Python modules, and use functions to get a suspect list for the kidnapping of Bayes, DataCamp's prize-winning Golden Retriever.

Dive into Python

Importing Python modules

Correcting a broken import

Creating variables

Creating a float

Creating strings

Correcting string errors

Valid variable names

Fun with functions

Load a DataFrame

Correcting a function error

Snooping for suspects

Getting Started in Python

In this chapter, you'll learn a powerful Python libary: pandas.  pandas lets you read, modify, and search tabular datasets (like spreadsheets and database tables).  You'll examine credit card records for the suspects and see if any of them made suspicious purchases.

What is pandas?

Loading a DataFrame

Inspecting a DataFrame

Selecting columns

Two methods for selecting columns

Correcting column selection errors

More column selection mistakes

Selecting rows with logic

Logical testing

Selecting missing puppies

Narrowing the list of suspects

Loading Data in pandas

Get ready to visualize your data! You'll create line plots with another Python module: Matplotlib.  Using line plots, you'll analyze the letter frequencies from the ransom note and several handwriting samples to determine the kidnapper.

Creating line plots

Working hard

Or hardly working?

Adding text to plots

Adding a legend

Adding labels

Adding floating text

Styling graphs

Tracking crime statistics

Playing with styles

Identifying Bayes' kidnapper

Plotting Data with Matplotlib

In this final chapter, you'll learn how to create three new plot types: scatter plots, bar plots, and histograms.  You'll use these tools to locate where the kidnapper is hiding and rescue Bayes, the Golden Retriever.

Making a scatter plot

Charting cellphone data

Modifying a scatterplot

Making a bar chart

Build a simple bar chart

Where did the time go?

Making a histogram

Modifying histograms

Heroes with histograms

Recap of the rescue

Different Types of Plots

Begin your journey into Data Science! Even if you've never written a line of code in your life, you'll be able to follow this course and witness the power of Python to perform Data Science. You'll use data to solve the mystery of Bayes, the kidnapped Golden Retriever, and along the way you'll become familiar with basic Python syntax and popular Data Science modules like Matplotlib (for charts and graphs) and pandas (for tabular data).

The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

Plongez dans la science des données avec Python et apprenez à analyser et visualiser avec Pandas et Matplotlib.

Introduction to Data Science in Python

Valid variable names

Introduction to Data Science in Python

Exercice interactif pratique