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Intermediate Python for Data Science
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
- 4 hours
- 18 Videos
- 87 Exercises
- 381,768 Participants
- 7,400 XP
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Course Description
Intermediate Python for Data Science is crucial for any aspiring data science practitioner learning Python. 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
“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
Filip Schouwenaars
Data Science Instructor at DataCamp
Filip is the passionate developer behind several of DataCamp's interactive courses, covering both R and Python. Under the motto 'Eat your own dog food', he has used the techniques DataCamp teaches its students to perform data analysis for DataCamp. Filip holds degrees in Electrical Engineering and Artificial Intelligence.
See MoreCollaborators
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Vincent Vankrunkelsven
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Patrick Varilly
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Florian Goossens