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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 Exercises850,300 Learners7400 XPData Analyst TrackData Scientist TrackPython Fundamentals Track

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Course Description

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib’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.

You'll discover how dictionaries offer an alternative to Python lists, and why 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.

As you progress, 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.

    Play Chapter Now
    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.

    Play Chapter Now
    Dictionaries, Part 1
    50 xp
    Motivation for dictionaries
    100 xp
    Create dictionary
    100 xp
    Access dictionary
    100 xp
    Dictionaries, Part 2
    50 xp
    Dictionary Manipulation (1)
    100 xp
    Dictionary Manipulation (2)
    100 xp
    Dictionariception
    100 xp
    Pandas, Part 1
    50 xp
    Dictionary to DataFrame (1)
    100 xp
    Dictionary to DataFrame (2)
    100 xp
    CSV to DataFrame (1)
    100 xp
    CSV to DataFrame (2)
    100 xp
    Pandas, Part 2
    50 xp
    Square Brackets (1)
    100 xp
    Square Brackets (2)
    100 xp
    loc and iloc (1)
    100 xp
    loc and iloc (2)
    100 xp
    loc and iloc (3)
    100 xp
  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.

    Play Chapter Now
    Comparison Operators
    50 xp
    Equality
    100 xp
    Greater and less than
    100 xp
    Compare arrays
    100 xp
    Boolean Operators
    50 xp
    and, or, not (1)
    100 xp
    and, or, not (2)
    50 xp
    Boolean operators with NumPy
    100 xp
    if, elif, else
    50 xp
    Warmup
    50 xp
    if
    100 xp
    Add else
    100 xp
    Customize further: elif
    100 xp
    Filtering pandas DataFrames
    50 xp
    Driving right (1)
    100 xp
    Driving right (2)
    100 xp
    Cars per capita (1)
    100 xp
    Cars per capita (2)
    100 xp
  4. 4

    Loops

    There are several techniques you can use to repeatedly execute Python code. While loops are like repeated if statements, the for loop iterates over all kinds of data structures. Learn all about them in this chapter.

    Play Chapter Now
    while loop
    50 xp
    while: warming up
    50 xp
    Basic while loop
    100 xp
    Add conditionals
    100 xp
    for loop
    50 xp
    Loop over a list
    100 xp
    Indexes and values (1)
    100 xp
    Indexes and values (2)
    100 xp
    Loop over list of lists
    100 xp
    Loop Data Structures Part 1
    50 xp
    Loop over dictionary
    100 xp
    Loop over NumPy array
    100 xp
    Loop Data Structures Part 2
    50 xp
    Loop over DataFrame (1)
    100 xp
    Loop over DataFrame (2)
    100 xp
    Add column (1)
    100 xp
    Add column (2)
    100 xp
  5. 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!

    Play Chapter Now
    Random Numbers
    50 xp
    Random float
    100 xp
    Roll the dice
    100 xp
    Determine your next move
    100 xp
    Random Walk
    50 xp
    The next step
    100 xp
    How low can you go?
    100 xp
    Visualize the walk
    100 xp
    Distribution
    50 xp
    Simulate multiple walks
    100 xp
    Visualize all walks
    100 xp
    Implement clumsiness
    100 xp
    Plot the distribution
    100 xp
    Calculate the odds
    50 xp

In the following tracks

Data Analyst Data Scientist Python Fundamentals

Datasets

GapminderCarsBRICS

Collaborators

vincentvankrunkelsven
Vincent Vankrunkelsven
filipsch
Filip Schouwenaars

Prerequisites

Introduction to Python
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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