Data Science, Northwestern University MSPA

Python Tops KDNuggets 2017 Data Science Software Poll

The results of KDNuggets’ 18th annual Software Poll should be fascinating reading for anyone involved in data science and analytics.  Some highlights – Python (52.6%) finally overtook R (52.1%), SQL remained at about 35%, and Spark and Tensorflow have increased to above 20%.


(Graph taken from

I am about halfway through Northwestern University’s Master of Science in Predictive Analytics (MSPA) program.  I am very thankful that the program has made learning different languages a priority.  I have already learned Python, Jupyter Notebooks, R, SQL, some NoSQL (MongoDB), and SAS.  In my current class in Generalized Linear Models, I have also started to learn Angoss, SAS Enterprise Miner, and Microsoft Azure machine learning.  However, it looks like you can’t ever stop learning new things – and I am going to have to learn Spark and Tensorflow – to name a few more.

I highly recommend you read this article.


Data Science, Northwestern University MSPA, Uncategorized

DataCamp’s Importing Data in Python Part 1 and Part 2.

I recently finished these DataCamp  courses and really liked them.  I highly recommend them to students in general and especially to the students in Northwestern University’s Master of Science in Predictive Analytics (MSPA) program.

Importing Data in Python Part 1 is described as:

As a Data Scientist, on a daily basis you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before doing any of these, however, you will need to know how to get data into Python. In this course, you’ll learn the many ways to import data into Python: (i) from flat files such as .txts and .csvs; (ii) from files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; (iii) from relational databases such as SQLite & PostgreSQL.

Importing Data in Python Part 2 is described as:

As a Data Scientist, on a daily basis you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before doing any of these, however, you will need to know how to get data into Python. In the prequel to this course, you have already learnt many ways to import data into Python: (i) from flat files such as .txts and .csvs; (ii) from files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; (iii) from relational databases such as SQLite & PostgreSQL. In this course, you’ll extend this knowledge base by learning to import data (i) from the web and (ii) a special and essential case of this: pulling data from Application Programming Interfaces, also known as APIs, such as the Twitter streaming API, which allows us to stream real-time tweets.


Data Science, Northwestern University MSPA

Learning to Use Python’s SQLAlchemy in DataCamp’s “Introduction to Databases in Python” – useful for students taking Northwestern’s MSPA Predict 420.

I just completed DataCamp’s course titled “Introduction to Databases in Python“.  This is a very informative course, and is actually one of the few tutorials out there that I have run across on SQLAlchemy.

I just finished Northwestern University’s MSPA (Master of Science in Predictive Analytics) Predict 420 class – Database Systems and Data Preparation Review, and I wish I would have taken DataCamp’s course first.  It would have helped tremendously.  You have the opportunity to use SQLAlchemy to interact with SQL databases in Predict 420, but I looked and could not find a really good tuturial on this, until I ran across DataCamp’s course, after I finished Predict 420.  I highly recommend this DataCamp course to other MSPA students.

Introduction to Databases in Python is divided up into 5 sections, with the course’s description of each section attached.

  1.  Basics of Relational Database.  In this chapter, you will become acquainted with the fundamentals of Relational Databases and the Relational Model. You will learn how to connect to a database and then interact with it by writing basic SQL queries, both in raw SQL as well as with SQLAlchemy, which provides a Pythonic way of interacting with databases.
  2. Applying Filtering, Ordering, and Grouping to Queries.  In this chapter, you will build on the database knowledge you began acquiring in the previous chapter by writing more nuanced queries that allow you to filter, order, and count your data, all within the Pythonic framework provided by SQLAlchemy!
  3. Advanced SQLAlchemy Queries.  Herein, you will learn to perform advanced – and incredibly useful – queries that will enable you to interact with your data in powerful ways.
  4. Creating and Manipulating your own Databases.  In the previous chapters, you interacted with existing databases and queried them in various different ways. Now, you will learn how to build your own databases and keep them updated!
  5. Putting it all together.  Here, you will bring together all of the skills you acquired in the previous chapters to work on a real life project! From connecting to a database, to populating it, to reading and querying it, you will have a chance to apply all the key concepts you learned in this course.





Data Science, Machine Learning

The world of machine learning algorithms – a summary infographic.

This is a very nice infographic that shows the basic types of machine learning algorithm categories.   It is somewhat informative to follow the path of how the algorithm got posted on twitter, where I saw it.  It was somewhat misleading (although not intentional I believe) about who actually created this infographic.  To me this highlights the importance of making sure we are crediting our information sources correctly.  This topic was also broached in this FiveThirtyEight article “Who Will Debunk The Debunkers” by Daniel Engber.  The article discusses many myths, one of them being a myth of how spinach was credited with having too much iron content.  It mentions that an unscholarly and unsourced article became “the ultimate authority for all the citations that followed”.  I have run across this as well, when I was trying to find the source of quotation about what a “Learning Health System” was defined as.  This definition was cited by at least twenty scholarly articles, but there was not reference for the citation, only circular references to the other articles that used this definition.  This highlights the importance of making sure we correctly cite the source of information, so it can be critically analyzed by other people interested in using the data.

I noticed this infographic after it had been tweeted by Evan Sinar (@EvanSinar).  The tweet cited an article in @DataScienceCentral.  That article “12 Algorithms Every Data Scientist Should Know” by Emmanuelle Rieuf, mentions an article posted by Mark van Rijmenan, with the same title – 12 Algorithms Every Data Scientist Should Know“, and then shows the infographic, giving the impression that this was the source of the algorithm.  That article mentions that the “guys from Think Big Data developed the infographic” and provided a link.  That links to the article “Which are the best known machine learning algorithms? Infographic” by Anubhav Srivastava.  It “mentioned over a dozen algorithms, segregated by their application intent, that should be in the repertoire of every data scientist”.  The bottom line, try to be careful with your source citations so it is not hard for people to follow the source backwards in time.  I was able to do this in this case, it just took a little while.  But there are many times where it is impossible to do this.

Now, for the infographic.




Data Science

Data Science Ecosystem graphic

I ran across this graphic in this article, The Data Science Ecosystem: Preamble, by Lukas Biewald, posted on the Open Data Science (ODSC) site.   This lays out SOME of the ecosystem out there, and I like the way Lukas divides the ecosystem up nicely into components.  I would comment that there is a lot left out about what Python and R can do in the Enrichment, ETL/Blending, Data Integration, Insights and Models sections.  But overall I like the graphic.


Data Science, Jupyter Notebook, JupyterLab

JupyterLab – Exciting Improvement on Jupyter Notebooks

At SciPy 2016, Brian Granger and Jason Grout presented JupyterLab, now in a pre-alpha release.  This was the most exciting and monumental news of the conference for me.  A blog post about JupyterLab from Fernando Perez can be viewed here, the link to the YouTube video of the presentation is available here, while the video is presented below.

The blog post discusses some of today’s “Jupyter Notebook” functionality, most of which I have not used.  This includes the Notebooks, “a file manager, a text editor, a terminal emulator, a monitor for running Jupyter processes, an IPython cluster manager, and a pager to display help”.   The new functionality allows you to “arrange a notebook next to a graphical console, atop a terminal that is monitoring the system, while keeping the file manager on the left”.  Users of RStudio will be happy to see this.  (I am wondering if they are going to create a Package Manager like RStudio?).

Here are a few screenshots of what it looks like.


You can download this now, and help “test and refine the system”.  Instructions to do this are here.

Data Science, Data Visualization, Jupyter Notebook

Jupyter Notebook, matplotlib figure display options, and pandas.set_option() optimization tips.

I prefer to do my coding in a Jupyter Notebook, as my previous posts have mentioned.  However, I have not run across any good documentation on how to optimize the notebook, for either a python or R kernel.  I am going to mention a few helpful hints I have found.  Here is the link to the Project Jupyter site.

First a basic comment on how to create a notebook where you want it.   You need to navigate to the directory where you want the notebook to be created.  I use the Windows PowerShell command-line shell.  When you open it up, you are at your home directory.  Use the “dir” command to see what is in that directory, and then use the “cd” (change directory) command to navigate to the directory you want to end up in.  If it is a longer path, you should enclose in quotes.  If you need to create a new directory, use the “md” or “mkdir” command to create a new directory.  For example, my long path is –  “….\Jupyter Notebooks\Python Notebooks”, and while at SciPy 2016 I created an new folder, and this directory is “….\Jupyter Notebooks\Python Notebooks\SciPy16” – to which I added a folder for each tutorial I attended.

Once you get into the final directory, type “Jupyter Notebook”, and a new notebook will be opened.  The first page that opens up is the “Home” page, and if your notebook exists, you can select it here.  If it doesn’t yet exist, then select “New” if the upper right, select your notebook type (for me R or Python 3), and it will launch the notebook.  (This notebook is from a pandas tutorial I attended at SciPy 2016 – “Analyzing and Manipulating Data with Pandas by Jonathon Rocher (excellent presentation if want to watch the video being created).


Once you click on the “pandas_tutorial”, this Jupyter notebook will open up.


A nice feature is that if you clone GitHub repository into that folder, and start a new Jupyter Notebook, then all the files that go with that repository are immediately available for use.

Importing data in a Jupyter Notebook.

If you are tired of hunting down the path for a data set, there is an easy way to find a data set and get it into the directory of the Jupyter notebook.  Go to the “Home” page, and select “Upload” and you will be taken to the “file upload” application.  Navigate to where you stored the data set on your computer, select, and then it will load that onto the home page.  You can then easily load it into your specific Jupyter notebook that is associated with that directory.


Matplotlib figure display options.

If you don’t specify how to display your figures in the Jupyter notebook, when you create a figure using matplotlib, a separate window will open and display the graph.  This window is nice because it is interactive, and you can zoom in on the graph, save it, put labels in, etc.  There is a way to do this in the Jupyter notebook.

The first option I learned about was:

%matplotlib inline

This would display the graph in the notebook, but it was no longer interactive.

However, if you use:

%matplotlib notebook

The figures will now show up in the notebook , and still be interactive.  I learned this during the pandas tutorial at SciPy 2016.

You can also set your figure size by:

LARGE_FIGSIZE = (12,8) # for example


Some pandas optimization hints



to set a large number of options.  For example:

pandas.set_option(“display.max_rows”, 16)

and only 16 rows of data will be displayed.  There are many options, so just use “pandas.set_option?” command to see what is available.

If you have other useful Jupyter notebook tips, would love to hear about them.