First off, welcome to Quantopian!

Look again at the tutorials. Look especially at lesson 2 "Creating a Pipeline" (https://www.quantopian.com/tutorials/pipeline#lesson2). There are two distinct steps in using a pipeline 1) define and instantiate it and 2) run the pipeline. You are missing step 2.

One first creates a pipeline. That's what is done in the following statements

from quantopian.pipeline import Pipeline

def make_pipeline():  
    return Pipeline()

my_pipe = make_pipeline()

One now has instantiated a pipeline object and created a reference to it 'my_pipe'. The most important thing to understand about pipelines, and the DataSets they use, is that they do not hold actual data. A pipeline simply defines how to fetch the data and the computations one wishes to perform. That is why the following doesn't work

results = make_pipeline()  
print results.head()

The pipeline object isn't a dataframe. It's not the results yet. It doesn't have any data to print. The output of the make_pipeline method is a pipeline object.

So, the second step is to execute run_pipeline. The output of that method is a dataframe which does hold the data. That will be the results.

from quantopian.research import run_pipeline

result = run_pipeline(my_pipe, '2015-05-05', '2015-05-05')

result.head(5)

The first step of creating and instantiating a pipeline only needs to be done once. After that, step two, running the run_pipeline method, can be done as many times and across as many timeframes as desired. The lectures and associated notebooks should make this two step process clear but there are a number of post in the forums here which also help to explain the pipeline concept.

https://www.quantopian.com/posts/need-help-with-pipeline-just-a-beginner
https://www.quantopian.com/posts/pipeline-and-history-function

Hope that helps.