diff --git a/.gitignore b/.gitignore
index cc5c9d120..5747a1fc0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -61,3 +61,4 @@ cache/
 
 docs/_build/
 docs/_autosummary/
+docs/normal_data.csv
diff --git a/.travis.yml b/.travis.yml
index 630648fcd..3ec57d10c 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -26,7 +26,7 @@ addons:
 before_install:
   - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
   - chmod +x miniconda.sh
-  - ./miniconda.sh -b -p $HOME/miniconda
+  - ./miniconda.sh -b -f -p $HOME/miniconda
   - export PATH=/home/travis/miniconda/bin:$PATH
   - conda update --yes conda
 
@@ -35,14 +35,18 @@ install:
   # TODO(sam): Add --upgrade flag when it works again
   - python3 setup.py install
 
+# https://docs.travis-ci.com/user/gui-and-headless-browsers/#Using-xvfb-to-Run-Tests-That-Require-a-GUI
+# sam: Not exactly sure why we need to initialize a display for this but it
+#      helps the tutorial plots build on Travis
+before_script:
+  - "export DISPLAY=:99.0"
+  - "sh -e /etc/init.d/xvfb start"
+  - sleep 3 # give xvfb some time to start
+
 script:
   - coverage run setup.py test
-  - cd docs && make html-raise-on-warning && cd ..
+  - make docs
 
 after_success:
   - coveralls
   - bash tools/deploy_docs.sh
-
-cache:
-  directories:
-  - /home/travis/virtualenv/python3.4.2/
diff --git a/docs/Makefile b/docs/Makefile
index fd5eddeb2..b05545c01 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -52,11 +52,13 @@ clean:
 	rm -rf $(BUILDDIR)/*
 
 html:
+	mkdir -p $(BUILDDIR)/html/_images
 	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
 	@echo
 	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
 
 html-raise-on-warning:
+	mkdir -p $(BUILDDIR)/html/_images
 	$(SPHINXBUILD) -W -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
 
 dirhtml:
diff --git a/docs/conf.py b/docs/conf.py
index 34943d339..1e5fceac7 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -35,6 +35,21 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.autosummary',
     'sphinx.ext.viewcode',
+    # These IPython extensions allow for embedded IPython code that gets rerun
+    # at build time.
+    'IPython.sphinxext.ipython_console_highlighting',
+    'IPython.sphinxext.ipython_directive'
+]
+
+# The following lines silence the matplotlib.use warnings since we import
+# matplotlib in each ipython directive block
+ipython_mplbackend = None
+ipython_execlines = [
+  'import matplotlib',
+  'matplotlib.use("Agg", warn=False)',
+  'import numpy as np',
+  'import matplotlib.pyplot as plt',
+  'plt.style.use("fivethirtyeight")',
 ]
 
 # Config autosummary
@@ -147,6 +162,7 @@
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = []
+ipython_savefig_dir = './_build/html/_images'
 
 # Add any extra paths that contain custom files (such as robots.txt or
 # .htaccess) here, relative to this directory. These files are copied
diff --git a/docs/sample.csv b/docs/sample.csv
new file mode 100644
index 000000000..ecee95a65
--- /dev/null
+++ b/docs/sample.csv
@@ -0,0 +1,4 @@
+x,y,z
+1,10,100
+2,11,101
+3,12,102
diff --git a/docs/tutorial.rst b/docs/tutorial.rst
index 7aaed0f49..20cd18c1c 100644
--- a/docs/tutorial.rst
+++ b/docs/tutorial.rst
@@ -1,16 +1,396 @@
 Start Here: ``datascience`` Tutorial
 ====================================
 
-In progress.
+This is a brief introduction to the functionality in
+:py:mod:`datascience`.  For a complete reference guide, please see
+:ref:`tables-overview`.
 
-Introduction
-------------
+For other useful tutorials and examples, see:
+
+- `The textbook introduction to Tables`_
+- `Example notebooks`_
+
+.. _The textbook introduction to Tables: http://data8.org/text/1_data.html#tables
+.. _Example notebooks: https://github.com/deculler/TableDemos
+
+.. contents:: Table of Contents
+    :depth: 2
+    :local:
+
+Getting Started
+---------------
+
+The most important functionality in the package is is the :py:class:`Table`
+class, which is the structure used to represent columns of data. You may load
+the class with:
+
+.. ipython:: python
+
+    from datascience import Table
+
+In the IPython notebook, type ``Table.`` followed by the TAB-key to see a list
+of members.
+
+Note that for the Data Science 8 class we also import additional packages and
+settings for all assignments and labs. This is so that plots and other available
+packages mirror the ones in the textbook more closely. The exact code we use is:
+
+.. code-block:: python
+
+    # HIDDEN
+
+    import matplotlib
+    matplotlib.use('Agg')
+    from datascience import Table
+    %matplotlib inline
+    import matplotlib.pyplot as plt
+    import numpy as np
+    plt.style.use('fivethirtyeight')
+
+In particular, the lines involving ``matplotlib`` allow for plotting within the
+IPython notebook.
+
+Creating a Table
+----------------
+
+A Table is a sequence of labeled columns of data.
+
+The basic Table constructor works as follows:
+
+.. ipython:: python
+
+    letters = ['a', 'b', 'c', 'z']
+    counts = [9, 3, 3, 1]
+    points = [1, 2, 2, 10]
+
+    t = Table(columns=[letters, counts, points],
+              labels=['letter', 'count', 'points'])
+
+    print(t)
+
+Note how the first keyword, ``columns``, specifies the contents of the table,
+and how the second, ``labels``, gives a name to each column. See
+:meth:`~datascience.tables.Table.__init__` for more details.
+
+------
+
+A table could also be read from a CSV file (that can be exported from an Excel
+spreadsheet, for example).  Here's the content of an example file:
+
+.. ipython:: python
+
+    cat mydata.csv
+
+And this is how we load it in as a :class:`Table` using
+:meth:`~datascience.tables.Table.read_table`:
+
+.. ipython:: python
+
+    Table.read_table('sample.csv')
+
+CSVs from URLs are also valid inputs to
+:meth:`~datascience.tables.Table.read_table`:
+
+.. ipython:: python
+
+    Table.read_table('http://data8.org/text/sat2014.csv')
+
+------
 
-Basic Table Usage
+For convenience, you can also initialize a Table from a dictionary of column
+names using
+:meth:`~datascience.tables.Table.from_columns_dict`.
+
+.. ipython:: python
+
+    Table.from_columns_dict({
+       'letter': letters,
+       'count': counts,
+       'points': points,
+    })
+
+This example illustrates the fact that built-in Python dictionaries don't
+preserve their key order -- the dictionary keys are ordered ``'letter'``,
+``'count'``, then ``'points'``, but the table columns are ordered ``'points'``,
+``'count'``, then ``'letter'``). If you want to ensure the order of your
+columns, use an ``OrderedDict``.
+
+Accessing Values
+----------------
+
+To access values of columns in the table, use
+:meth:`~datascience.tables.Table.values`.
+
+.. ipython:: python
+
+    t
+
+    t.values('letter')
+    t.values('count')
+
+    t['letter'] # This is a shorthand for t.values('letter')
+
+To access values by row, :meth:`~datascience.tables.Table.rows` returns an
+list-like :class:`~datascience.tables.Table.Rows` object that contains
+tuple-like :class:`~datascience.tables.Table.Row` objects.
+
+.. ipython:: python
+
+    t.rows
+    t.rows[0]
+
+    second = t.rows[1]
+    second
+    second[0]
+    second[1]
+
+To get the number of rows, use :attr:`~datascience.tables.Table.num_rows`.
+
+.. ipython:: python
+
+    t.num_rows
+
+
+Manipulating Data
 -----------------
 
-More Advanced Table Usage
--------------------------
+Here are some of the most common operations on data. For the rest, see the
+reference (:ref:`tables-overview`).
+
+Adding a column with :meth:`~datascience.tables.Table.with_column`:
+
+.. ipython:: python
+
+    t
+    t.with_column('vowel?', ['yes', 'no', 'no', 'no'])
+    t # .with_column returns a new table without modifying the original
+
+    t.with_column('2 * count', t['count'] * 2) # A simple way to operate on columns
+
+Selecting columns with :meth:`~datascience.tables.Table.select`:
+
+.. ipython:: python
+
+    t.select('letter')
+    t.select(['letter', 'points'])
+
+Renaming columns with :meth:`~datascience.tables.Table.with_relabeling`:
+
+.. ipython:: python
+
+    t
+    t.with_relabeling('points', 'other name')
+    t
+    t.with_relabeling(['letter', 'count', 'points'], ['x', 'y', 'z'])
+
+Selecting out rows by index with :meth:`~datascience.tables.Table.take` and
+conditionally with :meth:`~datascience.tables.Table.where`:
+
+.. ipython:: python
+
+    t
+    t.take(2) # the third row
+    t.take[0:2] # the first and second rows
+
+.. ipython:: python
+
+    t.where('points', 2) # rows where points == 2
+    t.where(t['count'] < 8) # rows where count < 8
+
+    t['count'] < 8 # .where actually takes in an array of booleans
+    t.where([False, True, True, True]) # same as the last line
+
+Operate on table data with :meth:`~datascience.tables.Table.sort`,
+:meth:`~datascience.tables.Table.group`, and
+:meth:`~datascience.tables.Table.pivot`
+
+.. ipython:: python
+
+    t
+    t.sort('count')
+    t.sort('letter', descending = True)
+
+.. ipython:: python
+
+    t.group('count')
+
+    # You may pass a reducing function into the collect arg
+    # Note the renaming of the points column because of the collect arg
+    t.select(['count', 'points']).group('count', collect = sum)
+
+.. ipython:: python
+
+    other_table = Table([
+        ['married', 'married', 'partner', 'partner', 'married'],
+        ['Working as paid', 'Working as paid', 'Not working', 'Not working', 'Not working'],
+        [1, 1, 1, 1, 1]
+    ],
+    ['mar_status', 'empl_status', 'count'])
+    other_table
+
+    other_table.pivot('mar_status', 'empl_status', 'count', collect = sum)
+
+Visualizing Data
+----------------
+
+We'll start with some data drawn at random from two normal distributions:
+
+.. ipython:: python
+
+    normal_data = Table(
+        [ np.random.normal(loc = 1, scale = 2, size = 100),
+          np.random.normal(loc = 4, scale = 3, size = 100) ],
+        ['data1', 'data2']
+    )
+
+    normal_data
+
+Draw histograms with :meth:`~datascience.tables.Table.hist`:
+
+.. ipython:: python
+
+    @savefig hist.png width=4in
+    normal_data.hist()
+
+.. ipython:: python
+
+    @savefig hist_binned.png width=4in
+    normal_data.hist(bins = range(-5, 10))
+
+.. ipython:: python
+
+    @savefig hist_overlay.png width=4in
+    normal_data.hist(bins = range(-5, 10), overlay = True)
+
+If we treat the ``normal_data`` table as a set of x-y points, we can
+:meth:`~datascience.tables.Table.plot` and
+:meth:`~datascience.tables.Table.scatter`:
+
+.. ipython:: python
+
+    @savefig plot.png width=4in
+    normal_data.sort('data1').plot('data1') # Sort first to make plot nicer
+
+.. ipython:: python
+
+    @savefig scatter.png width=4in
+    normal_data.scatter('data1')
+
+.. ipython:: python
+
+    @savefig scatter_line.png width=4in
+    normal_data.scatter('data1', fit_line = True)
+
+Use :meth:`~datascience.tables.Table.barh` to display categorical data.
+
+.. ipython:: python
+
+    t
+    @savefig barh.png width=4in
+    t.barh('letter')
+
+Exporting
+---------
+
+Exporting to CSV is the most common operation and can be done by first
+converting to a pandas dataframe with :meth:`~datascience.tables.Table.to_df`:
+
+.. ipython:: python
+
+    normal_data
+
+    # index = False prevents row numbers from appearing in the resulting CSV
+    normal_data.to_df().to_csv('normal_data.csv', index = False)
+
+An Example
+----------
+
+We'll recreate the steps in `Chapter 3 of the textbook`_ to see if there is a
+significant difference in birth weights between smokers and non-smokers using a
+bootstrap test.
+
+For more examples, check out `the TableDemos repo`_.
+
+.. _Chapter 3 of the textbook: http://data8.org/text/3_inference.html#Using-the-Bootstrap-Method-to-Test-Hypotheses
+.. _the TableDemos repo: https://github.com/deculler/TableDemos
+
+From the text:
+
+    The table ``baby`` contains data on a random sample of 1,174 mothers and
+    their newborn babies. The column ``birthwt`` contains the birth weight of
+    the baby, in ounces; ``gest_days`` is the number of gestational days, that
+    is, the number of days the baby was in the womb. There is also data on
+    maternal age, maternal height, maternal pregnancy weight, and whether or not
+    the mother was a smoker.
+
+.. ipython:: python
+
+    baby = Table.read_table('http://data8.org/text/baby.csv')
+    baby # Let's take a peek at the table
+
+    # Select out columns we want.
+    smoker_and_wt = baby.select(['m_smoker', 'birthwt'])
+    smoker_and_wt
+
+Let's compare the number of smokers to non-smokers.
+
+.. ipython:: python
+
+    @savefig m_smoker.png width=4in
+    smoker_and_wt.select('m_smoker').hist(bins = [0, 1, 2]);
+
+We can also compare the distribution of birthweights between smokers and
+non-smokers.
+
+.. ipython:: python
+
+    # Non smokers
+    # We do this by grabbing the rows that correspond to mothers that don't
+    # smoke, then plotting a histogram of just the birthweights.
+    @savefig not_m_smoker_weights.png width=4in
+    smoker_and_wt.where('m_smoker', 0).select('birthwt').hist()
+
+    # Smokers
+    @savefig m_smoker_weights.png width=4in
+    smoker_and_wt.where('m_smoker', 1).select('birthwt').hist()
+
+What's the difference in mean birth weight of the two categories?
+
+.. ipython:: python
+
+    nonsmoking_mean = smoker_and_wt.where('m_smoker', 0).values('birthwt').mean()
+    smoking_mean = smoker_and_wt.where('m_smoker', 1).values('birthwt').mean()
+
+    observed_diff = nonsmoking_mean - smoking_mean
+    observed_diff
+
+Let's do the bootstrap test on the two categories.
+
+.. ipython:: python
+
+    num_nonsmokers = smoker_and_wt.where('m_smoker', 0).num_rows
+    def bootstrap_once():
+        """
+        Computes one bootstrapped difference in means.
+        The table.sample method lets us take random samples.
+        We then split according to the number of nonsmokers in the original sample.
+        """
+        resample = smoker_and_wt.sample(with_replacement = True)
+        bootstrap_diff = resample.values('birthwt')[:num_nonsmokers].mean() - \
+            resample.values('birthwt')[num_nonsmokers:].mean()
+        return bootstrap_diff
+
+    repetitions = 1000
+    bootstrapped_diff_means = np.array(
+        [ bootstrap_once() for _ in range(repetitions) ])
+
+    bootstrapped_diff_means[:10]
+
+    num_diffs_greater = (abs(bootstrapped_diff_means) > abs(observed_diff)).sum()
+    p_value = num_diffs_greater / len(bootstrapped_diff_means)
+    p_value
+
 
 Drawing Maps
 ------------
+To come.