Intermediate Python
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
- 4 hours
- 18 Videos
- 87 Exercises
- 511,137 Participants
- 7,400 XP
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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.
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1
Matplotlib
FreeData 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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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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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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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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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.
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1
Matplotlib
FreeData 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.
-
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.
-
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.
-
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.
-
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!
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's Banking Group
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Harvard Business School
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Decision Science Analytics @ USAA
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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Collaborators
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Filip Schouwenaars
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Vincent Vankrunkelsven
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Patrick Varilly
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Florian Goossens