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Statistical Thinking in Python (Part 1)
Build the foundation you need to think statistically and to speak the language of your data.
- 3 hours
- 18 Videos
- 61 Exercises
- 62,338 Participants
- 4,550 XP
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Course Description
After all of the hard work of acquiring data and getting them into a form you can work with, you ultimately want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically, to speak the language of your data, to understand what they are telling you. The foundations of statistical thinking took decades upon decades to build, but they can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up to speed and begin thinking statistically by the end of this course.
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1
Graphical exploratory data analysis
FreeLook before you leap! A very important proverb, indeed. Prior to diving in headlong into sophisticated statistical inference techniques, you should first explore your data by plotting them and computing simple summary statistics. This process, called exploratory data analysis, is a crucial first step in statistical analysis of data. So it is a fitting subject for the first chapter of Statistical Thinking in Python.
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Thinking probabilistically-- Discrete variables
Statistical inference rests upon probability. Because we can very rarely say anything meaningful with absolute certainty from data, we use probabilistic language to make quantitative statements about data. In this chapter, you will learn how to think probabilistically about discrete quantities, those that can only take certain values, like integers. It is an important first step in building the probabilistic language necessary to think statistically.
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Quantitative exploratory data analysis
In the last chapter, you learned how to graphically explore data. In this chapter, you will compute useful summary statistics, which serve to concisely describe salient features of a data set with a few numbers.
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Thinking probabilistically-- Continuous variables
In the last chapter, you learned about probability distributions of discrete variables. Now it is time to move on to continuous variables, such as those that can take on any fractional value. Many of the principles are the same, but there are some subtleties. At the end of this last chapter of the course, you will be speaking the probabilistic language you need to launch into the inference techniques covered in the sequel to this course.
-
1
Graphical exploratory data analysis
FreeLook before you leap! A very important proverb, indeed. Prior to diving in headlong into sophisticated statistical inference techniques, you should first explore your data by plotting them and computing simple summary statistics. This process, called exploratory data analysis, is a crucial first step in statistical analysis of data. So it is a fitting subject for the first chapter of Statistical Thinking in Python.
-
Quantitative exploratory data analysis
In the last chapter, you learned how to graphically explore data. In this chapter, you will compute useful summary statistics, which serve to concisely describe salient features of a data set with a few numbers.
-
Thinking probabilistically-- Discrete variables
Statistical inference rests upon probability. Because we can very rarely say anything meaningful with absolute certainty from data, we use probabilistic language to make quantitative statements about data. In this chapter, you will learn how to think probabilistically about discrete quantities, those that can only take certain values, like integers. It is an important first step in building the probabilistic language necessary to think statistically.
-
Thinking probabilistically-- Continuous variables
In the last chapter, you learned about probability distributions of discrete variables. Now it is time to move on to continuous variables, such as those that can take on any fractional value. Many of the principles are the same, but there are some subtleties. At the end of this last chapter of the course, you will be speaking the probabilistic language you need to launch into the inference techniques covered in the sequel to this course.
What do other learners have to say?
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Lloyd's Banking Group
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Harvard Business School
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Ronald Bowers
Decision Science Analytics @ USAA
Justin Bois
Lecturer at the California Institute of Technology
Justin Bois is a lecturer in the Division of Biology and Biological Engineering at the California Institute of Technology. He teaches nine different classes there, nearly all of which heavily feature Python. He is dedicated to empowering students in the biological sciences with quantitative tools, particularly data analysis skills. Beyond biologists, he is thrilled to develop courses for DataCamp, whose students are an excited bunch of burgeoning data scientists!
See MoreCollaborators
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Yashas Roy
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Hugo Bowne-Anderson
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Vincent Lan