Before you start
What you will learn
- Basics of the Python programming language, and how to use it as a tool for data analysis
- How to use computation to help your data tell a story
- Fundamental principles and methods of visualization
- How to use tools widely used by data scientists, such as Jupyter Notebooks
We live in an era of unprecedented access to data. Understanding how to organize and leverage the vast amounts of information at our disposal are critical skills that allow us to infer upon the world and make informed decisions. This course will introduce you to such skills.
To work with large amounts of data, you will need to harness the power of computation through programming. This course teaches you basic programming skills for manipulating data. You will learn how to use Python to organize and manipulate data in tables, and to visualize data effectively. No prior experience with programming or Python is needed, nor is any statistics background necessary.
The examples given in the course involve real world data from diverse settings. Not all data is numerical – you will work with different types of data from a variety of domains. Though the term “data science” is relatively new, the fundamental ideas of data science are not. The course includes powerful examples that span the centuries from the Victorian era to the present day.
This course emphasizes learning through doing: you will work on large real-world data sets through interactive assignments to apply the skills you learn. Throughout, the underlying thread is that data science is a way of thinking, not just an assortment of methods. You will also hone your interpretation and communication skills, which are essential skills for data scientists.
BerkeleyX's Foundations of Data Science Professional Certificate
Earn a Professional Certificate in 2-4 months if courses are taken one at a time.View the program
- Foundations of Data Science: Computational Thinking with Python
- 20–30 hours of effort
Learn how to use inferential thinking to make conclusions about unknowns based on data in random samples.
- 24–36 hours of effort
Learn how to use machine learning, with a focus on regression and classification, to automatically identify patterns in your data and make better predictions.
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