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Beginning your data science exploration
00:55

Demystifying data science
03:55
The value of data science
01:52
Defining the data science life cycle
00:54

Reducing bias with probability sampling
04:00
Using non-probability sampling
02:32

Comparing Python and R
02:16
Setting up your Jupyter environment
03:32

Defining tabular data
02:41
Reading tabular data
06:13
Interpreting tabular data
02:29
Gathering insights
05:53
Answering specific questions
02:45

Defining exploratory data analysis
00:51
Recognizing statistical data types
03:22
Distinguishing properties of data
05:08

Explaining data cleaning
01:22
Questions to guide data cleaning
04:07

Demystifying data visualization
01:02
Visualizing your qualitative data
05:17
Visualizing your quantitative data
08:08

Defining inference
00:43
Designing a hypothesis test
05:13
Creating a permutation
04:13
Conducting a permutation test
06:35
Bootstrapping a confidence interval
07:56

Defining prediction for data science
01:46
Navigating classification
02:07
Recognizing the k-NN algorithm
03:13
Implementing k-Nearest Neighbors
07:29
Navigating regression
02:52
Checking assumptions of regression
02:20
Implementing linear regression
06:09

Next steps
01:05

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00:00

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