Interactive Course

Intermediate Python for Data Science

Level up your data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas.

  • 4 hours
  • 18 Videos
  • 87 Exercises
  • 370,166 Participants
  • 7,400 XP

Loved by learners at thousands of top companies:

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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.

  1. 1

    Matplotlib

    Free

    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.

  2. 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.

  3. 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!

  4. 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.

  5. 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.

  1. 1

    Matplotlib

    Free

    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.

  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.

  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.

  4. 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.

  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!

What do other learners have to say?

Devon

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

Louis

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

Louis Maiden

Harvard Business School

Ronbowers

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Filip Schouwenaars
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.

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