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 leverages the techniques DataCamp teaches its students to perform data analysis for DataCamp. Filip holds degrees in Electrical Engineering and Artificial Intelligence.
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.
Data 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.
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.
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.
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.
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!