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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 Exercises787,234 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 the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

  1. 1



    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
    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
    50 xp
    100 xp
    100 xp
    100 xp
    100 xp
    Additional Customizations
    100 xp
    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


vincentvankrunkelsvenVincent VankrunkelsvenfilipschFilip 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:
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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA