fix README links to fonnesbeck notebooks

README.md

@@ -122,7 +122,7 @@ Monday | Wednesday
 * Don't forget about the Command line exercises (listed at the bottom of [Introduction to the Command Line](slides/02_Introduction_to_the_Command_Line.md))
 
 **Optional:**
-* To learn more Pandas, review this [three-part tutorial](http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/), or review these two excellent (but extremely long) notebooks on Pandas: [introduction](http://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/Section2_5-Introduction-to-Pandas.ipynb) and [data wrangling](http://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/Section2_6-Data-Wrangling-with-Pandas.ipynb).
+* To learn more Pandas, review this [three-part tutorial](http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/), or review these two excellent (but extremely long) notebooks on Pandas: [introduction](http://nbviewer.ipython.org/github/fonnesbeck/Bios8366/blob/master/notebooks/Section2_5-Introduction-to-Pandas.ipynb) and [data wrangling](http://nbviewer.ipython.org/github/fonnesbeck/Bios8366/blob/master/notebooks/Section2_6-Data-Wrangling-with-Pandas.ipynb).
 * Read [How Software in Half of NYC Cabs Generates $5.2 Million a Year in Extra Tips](http://iquantny.tumblr.com/post/107245431809/how-software-in-half-of-nyc-cabs-generates-5-2) for an excellent example of exploratory data analysis.
 
 
@@ -140,8 +140,8 @@ Monday | Wednesday
 * Watch [Look at Your Data](https://www.youtube.com/watch?v=coNDCIMH8bk) (18 minutes) for an excellent example of why visualization is useful for understanding your data.
 
 **Resources:**
-* For more on Pandas plotting, read this [notebook](http://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/Section2_7-Plotting-with-Pandas.ipynb) or the [visualization page](http://pandas.pydata.org/pandas-docs/stable/visualization.html) from the official Pandas documentation.
-* To learn how to customize your plots further, browse through this [notebook on matplotlib](http://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/Section2_4-Matplotlib.ipynb) or this [similar notebook](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb).
+* For more on Pandas plotting, read this [notebook](http://nbviewer.ipython.org/github/fonnesbeck/Bios8366/blob/master/notebooks/Section2_7-Plotting-with-Pandas.ipynb) or the [visualization page](http://pandas.pydata.org/pandas-docs/stable/visualization.html) from the official Pandas documentation.
+* To learn how to customize your plots further, browse through this [notebook on matplotlib](http://nbviewer.ipython.org/github/fonnesbeck/Bios8366/blob/master/notebooks/Section2_4-Matplotlib.ipynb) or this [similar notebook](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb).
 * To explore different types of visualizations and when to use them, [Choosing a Good Chart](http://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf) and [The Graphic Continuum](http://www.coolinfographics.com/storage/post-images/The-Graphic-Continuum-POSTER.jpg) are handy one-page references, or check out the [R Graph Catalog](http://shinyapps.stat.ubc.ca/r-graph-catalog/).
 * For a more in-depth introduction to visualization, browse through these [PowerPoint slides](http://www2.research.att.com/~volinsky/DataMining/Columbia2011/Slides/Topic2-EDAViz.ppt) from Columbia's Data Mining class.
 * [Mashape](https://www.mashape.com/explore) and [Apigee](https://apigee.com/providers) allow you to explore tons of different APIs. Alternatively, a [Python API wrapper](http://www.pythonforbeginners.com/api/list-of-python-apis) is available for many popular APIs.