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Introduction to Data Science in Python

4.6+
157 reviews
Beginner

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

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4 Hours13 Videos44 Exercises439,328 Learners

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Course Description

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).
  1. 1

    Getting Started in Python

    Free

    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.

    Play Chapter Now
    Dive into Python
    50 xp
    Importing Python modules
    100 xp
    Correcting a broken import
    100 xp
    Creating variables
    50 xp
    Creating a float
    100 xp
    Creating strings
    100 xp
    Correcting string errors
    100 xp
    Valid variable names
    50 xp
    Fun with functions
    50 xp
    Load a DataFrame
    100 xp
    Correcting a function error
    100 xp
    Snooping for suspects
    100 xp
  2. 2

    Loading Data in pandas

    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.

    Play Chapter Now
    What is pandas?
    50 xp
    Loading a DataFrame
    100 xp
    Inspecting a DataFrame
    100 xp
    Selecting columns
    50 xp
    Two methods for selecting columns
    100 xp
    Correcting column selection errors
    100 xp
    More column selection mistakes
    100 xp
    Selecting rows with logic
    50 xp
    Logical testing
    100 xp
    Selecting missing puppies
    100 xp
    Narrowing the list of suspects
    100 xp
  3. 3

    Plotting Data with Matplotlib

    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.

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    Creating line plots
    50 xp
    Working hard
    100 xp
    Or hardly working?
    100 xp
    Adding text to plots
    50 xp
    Adding a legend
    100 xp
    Adding labels
    100 xp
    Adding floating text
    100 xp
    Styling graphs
    50 xp
    Tracking crime statistics
    100 xp
    Playing with styles
    100 xp
    Identifying Bayes' kidnapper
    100 xp
  4. 4

    Different Types of Plots

    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.

    Play Chapter Now
    Making a scatter plot
    50 xp
    Charting cellphone data
    100 xp
    Modifying a scatterplot
    100 xp
    Making a bar chart
    50 xp
    Build a simple bar chart
    100 xp
    Where did the time go?
    100 xp
    Making a histogram
    50 xp
    Modifying histograms
    100 xp
    Heroes with histograms
    100 xp
    Recap of the rescue
    50 xp

In the following tracks

Data Skills for Business

Collaborators

Mona Khalil
Hillary Green-Lerman HeadshotHillary Green-Lerman

Lead Data Scientist, Looker

Hillary is a Lead Data Scientist at Looker. She is an expert in creating a data-driven product and curriculum development culture, having previously built the Product Intelligence team at Knewton from the ground up. She enjoys explaining data science in a way that is understandable to people with both PhDs in Math and BAs in English.
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*4.6
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  • Santiago N.
    about 1 month

    good starting point for intermediate pyton course :D

  • Johanna S.
    2 months

    The course is well done and easy to understand. The exercises and examples help you practice what was learned in each section.

  • Devon S.
    2 months

    I appreciated the overarching goal used to complete tasks in this module. The instruction was very clear and easy to follow.

  • chetanand m.
    2 months

    WAs nice specially the daily pratice

  • Martinus P.
    2 months

    Great course

    • Loading ...

"good starting point for intermediate pyton course :D"

Santiago N.

"The course is well done and easy to understand. The exercises and examples help you practice what was learned in each section."

Johanna S.

"I appreciated the overarching goal used to complete tasks in this module. The instruction was very clear and easy to follow."

Devon S.

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