This course is part of these tracks:
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 MoreCollaborator(s)
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Vincent Vankrunkelsven
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Patrick Varilly
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Florian Goossens
Prerequisites
Course Description
The intermediate python course is crucial to your data science curriculum. Learn to visualize real data with matplotlib's functions and get to know new 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're 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 will learn to build various types of plots and to customize them to make them more visually appealing and interpretable.
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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, manipulating and accessing the information you need from these data structures.
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Logic, Control Flow and Filtering
Boolean logic is the foundation of decision-making in your Python programs. Learn about different comparison operators, how you can combine them with boolean operators and how to use the boolean outcomes in control structures. You'll also learn to filter data from Pandas DataFrames using logic.
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Loops
There are several techniques to repeatedly execute Python code. While loops are like repeated if statements; the for loop is there to iterate over all kinds of data structures. Learn all about them in this chapter.
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Case Study: Hacker Statistics
This chapter blends together everything you've learned up to now. You will use hacker statistics to calculate your chances of winning a bet. Use random number generators, loops and matplotlib to get the competitive edge!
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