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Intermediate Python

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

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4 Hours18 Videos87 Exercises883,299 Learners7400 XPData Analyst TrackData Scientist TrackPython Fundamentals Track

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Course Description

Improve Your Python Skills

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matpltolib’s functions and get acquainted with data structures such as the dictionary and pandas DataFrame. This four-hour intermediate course will help you to build on your existing Python skills and explore new Python applications and functions that expand your repertoire and help you work more efficiently.

Learn to Use Python Dictionaries and pandas

Dictionaries offer an alternative to Python lists, while the pandas dataframe is the most popular way of working with tabular data. In the second chapter of this course, you’ll find out how you can create and manipulate datasets, and how to access them using these structures. Hands-on practice throughout the course will build your confidence in each area.

Explore Python Boolean Logic and Python Loops

In the second half of this course, you’ll look at logic, control flow, filtering and loops. These functions work to control decision-making in Python programs and help you to perform more operations with your data, including repeated statements. You’ll finish the course by applying all of your new skills by using hacker statistics to calculate your chances of winning a bet.

Once you’ve completed all of the chapters, you’ll be ready to apply your new skills in your job, new career, or personal project, and be prepared to move onto more advanced Python learning.
  1. 1

    Matplotlib

    Free

    Data visualization is a key skill for aspiring data scientists. Matplotlib makes it easy to create meaningful and insightful plots. In this chapter, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.

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    Basic plots with Matplotlib
    50 xp
    Line plot (1)
    100 xp
    Line Plot (2): Interpretation
    50 xp
    Line plot (3)
    100 xp
    Scatter Plot (1)
    100 xp
    Scatter plot (2)
    100 xp
    Histogram
    50 xp
    Build a histogram (1)
    100 xp
    Build a histogram (2): bins
    100 xp
    Build a histogram (3): compare
    100 xp
    Choose the right plot (1)
    50 xp
    Choose the right plot (2)
    50 xp
    Customization
    50 xp
    Labels
    100 xp
    Ticks
    100 xp
    Sizes
    100 xp
    Colors
    100 xp
    Additional Customizations
    100 xp
    Interpretation
    50 xp
  2. 2

    Dictionaries & Pandas

    Learn about the dictionary, an alternative to the Python list, and the pandas DataFrame, the de facto standard to work with tabular data in Python. You will get hands-on practice with creating and manipulating datasets, and you’ll learn how to access the information you need from these data structures.

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  3. 3

    Logic, Control Flow and Filtering

    Boolean logic is the foundation of decision-making in Python programs. Learn about different comparison operators, how to combine them with Boolean operators, and how to use the Boolean outcomes in control structures. You'll also learn to filter data in pandas DataFrames using logic.

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  4. 5

    Case Study: Hacker Statistics

    This chapter will allow you to apply all the concepts you've learned in this course. You will use hacker statistics to calculate your chances of winning a bet. Use random number generators, loops, and Matplotlib to gain a competitive edge!

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In the following tracks

Data Analyst Data Scientist Python Fundamentals

Collaborators

vincentvankrunkelsven
Vincent Vankrunkelsven
filipsch
Filip Schouwenaars
Hugo Bowne-Anderson Headshot

Hugo Bowne-Anderson

Data Scientist at DataCamp

Hugo is a data scientist, educator, writer and podcaster at DataCamp. His main interests are promoting data & AI literacy, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. If you want to know what he likes to talk about, definitely check out DataFramed, the DataCamp podcast, which he hosts and produces: https://www.datacamp.com/community/podcast
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