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R Open Lab Summary Report – Fall 2018

This semester, I returned to holding R open labs with another student intern, Hanying Ji. We made some adjustment about the structure of the open labs. Time flies, and now our R open labs have reached its end for this semester. I would like to summarize our journey and provide insights for both myself and future interns, hopefully.

A quick overview

We covered 9 topics this semester in total including the starter kit. This is almost twice as many as last semester. For the last open lab, we even ran out of major topics. This is unimaginable to me, and it is proof of success. We conducted open labs on the fundamentals, apply family, exploratory data analysis, character strings, data visualization, data manipulation, shiny, and randomness. These are pretty much everything a beginner needs to learn to use R without going too theoretical.

The flow of teaching is the same. I first introduce and do an example of the function quickly. Then, I show attendee the help page of the function so that they get a sense of arguments and results. Finally, I will talk about additional important arguments and do some examples with those arguments changed. For example, when I was introducing the function matrix, I demonstrated examples with the argument byrow = T and byrow = F. By visualizing the output of both, students can understand that argument better.


We made some changes compared to last semester, and they are all positive based on our program’s performance.

  1. We started using Github repository as the way of sharing codes and data sets. It was messy to share files through google drive, and people had to come back to me asking for access. Using Github solves this problem. Plus, I think it is great to introduce Github to them since it is useful while handling and managing codes and projects.
  2. We promoted and advertised this program at the beginning of this semester. A lot of students told us that they wish they knew this program earlier. So I asked my friends to help me spread the words in SIPA and QMSS community. Also, my supervisors got the words out by sending emails to different departments. As a result, we have seen a decent population who attends our open labs consistently.
  3. We stuck with using examples and let the functions demonstrate themselves. Introducing and describing coding can be boring and dry. Having examples let students visualize the process in their heads, and thus, they can get familiar with the new functions quickly.

Potential improvement

  1. The fundamental concepts, especially the part about loops, should have more examples and practice problems. I noticed that for people who had coding experience before, it is easy to grasp the basic concepts, but for those people with zero experience in coding, the loop and conditional part is the first obstacle. Having enough practice in this part will help students get used to R coding.
  2. The pace could be a little slower. As I mentioned above, we ran out of topics to cover by the end of this semester. I also felt that students didn’t fully understand some of the topics. I think in the future, the pace of open labs should always be adjusted according to the situation. Also, diverse examples can be added to help attendee learn important topics such as data manipulation.

This is my last semester at Columbia University, and I am honored to participate in this program again. It has been a fulfilling experience. Starting from my undergraduate years, I have been a TA for many years. I enjoy helping fellow students overcoming their difficulties and serving the university community. It is definitely pleasant to see that this program has a better performance this semester. For the ones who take this position in the future, I am more than happy to discuss with you about how to be a helpful TA and how to run this fantastic program.

R Open Lab Reflections

Sharing is always good!

I am really happy to get a great opportunity to work for Digital Center in my last semester at Columbia as a R Open Lab Intern in Digital Center Internship Program.R is really a powerful tool for statistic and data science nowadays which I love most. Holding weekly R Open Lab and daily consulting really give me good experience to teach and share my understand of data and R programming. During this process, I also refresh my own idea by interacting with people from different academic background.

Teaching and sharing are quite different from just telling the knowledge. When I was deciding where to start and how to explain every idea clearly, I was thrown back to the moment when I was new to R, such as “what foundation is important but tricky?”, “what mistake will we make if we are confused with some concept?”. Most importantly, this is always the idea that guide me how to teach in the whole semester. Nowadays there are tons of material people can find online to get them familiar with a programming language, but I think our Open Lab is great place for (1)getting idea of where to start; (2)improving understand of R.

In R Open Lab, I followed the roadmap of basic knowledge of variable type, data structures, functions and environment, data manipulation, exploratory data analysis/ visualization, apply family, text analysis, R Shiny, which starting with the fundamentals and going to more applied knowledge.

I always went forward and backward when we met fundamentals during teaching something new and gave enough exercises after each new concept, which can not only remind the attendee the fundamental but also satisfy them when they know that they really learnt the knowledge. Discussions and questions were always encouraged during lab, which means that every attendee engaged in the lab and thought by themselves.

In the consulting part,  I have to say it is really worthwhile to take time listening to people’s question, clarifying it and solving together. During this semester, I worked on different problems from coding in R, statistical modeling and data analysis. I realized that it is often the case that people come with error but have no idea about what their question really is, I always talk with them, dig into their problems and try to find out the real question. It is a skill for problem solving. I also improved my own knowledge like building boosting models and writing SQL in R to simplify data manipulation.

I recommend this internship program. Sharing what we have and learning from people are what we need to do all the time.


Hanying Ji
M.A. Statistics (2018)

Python Open Lab Reflections

This fall semester I got an excellent opportunity to work as a Digital Center Intern, as part of the Digital Centers Internship Program. My work involved data consulting, as well as assisting in leading Python OpenLabs. I am a Statistician, so  R is more intuitive to me. However, due to onset of Machine Learning and Neural Networks, Python is “the” language.

I believe that one can not build anything great on a weak foundation. This is what drove all the Python OpenLab sessions. We spent considerable time on teaching about variables, variable assignment, different operators, data types, manipulating them, writing small functions, looping over different data types, and then finally introduced Numpy and Pandas package. I personally tried to draw a parallel with R language as and when I could, to help R users learn better as well as students who are unfamiliar with R language, to gain an insight into a very helpful tool.

Practice makes one perfect, and therefore after midterm, we started off our sessions with first half-an hour devoted to working on assessment worksheets. This seemed to be very helpful, as not only did it help the students to understand where they stand and things they need to work on, but also gave us an idea as to what topics need to be repeated over and over again. This also lead to more engaging discussions, where every student came up with their own way to solve a problem. Discussion definitely helps to understand things better, and we did see it work during our OpenLab sessions. We found the students to be more involved in learning together.

The consulting part of my job was very rewarding indeed. During this one semester, I got to work on very diverse problems that people were trying to solve- be it coding in R, Python, Machine Learning, or basic Statistics. I even got to learnt a bit of generalized linear models, while trying to help somebody understand their model better. I would strongly recommend anybody to be part of this wonderful internship. I believe I have become a better student and a learner by being a part of this internship, and I do hope all the students who came for help, found us to be useful.

Anshuma Chandak
M.A. Statistics (2018)

Python Open Lab mid-term reflection (Fall 2018)

This semester I am glad to participate in the Python Open Lab of the digital center. Staying with students just reminds of myself. When I learned my first programming language, I had a lot of struggle and made tons of mistakes. As a result, I get much experience and I am really happy to share them with others.

Python is one of the programming languages I ever learned and it is the most concise one. With Python, many complicated things can be simplified. For most students who join our open lab, Python is the first advanced programming language they learn. This is great because Python can help students learn the basic programming skills and data structures without spending much time on irrelevant details. It helps students focus on their task and gives them the confidence to explore the unknown world.

I started with the basic ideas of types and operations in Python and introduced data structures like list, dictionary later. Other topics such as function, conditional statement and FileIO are also taught. I found that the sequence of materials can be organized better. And more examples could be shown for students to have a better understanding of the materials.

During the teaching, I learned a lot too. I began to see things from a different perspective. Although I have knowledge about programming languages, it still takes great efforts to spread the ideas to students and support them. The most important thing I learned is I need to focus on the big picture and skip the specific tricks to help students have a complete view of the materials. This is where I need to improve myself.


Mid-Semester Reflection – R Open Labs

Now the semester is halfway done, I think it is a good time to review our progress of this code teaching program. The R open lab’s performance has improved compared to the last one. We are having a consistent population of attendee, and we have already covered more topics than last semester. I am confident to say that our program is heading towards the right direction.

The people attending the open labs have diverse backgrounds: SIPA, SPS, GSAS, etc. I’m glad to see many people actively showing up to learn how to apply coding techniques to their customized scenarios. Another surprise is that we are having a decent number of the attendee in each open lab so far. Also, people are showing up consistently. One of the biggest problems in the past is that we are not having enough attendee, and people don’t attend continuously. That causes the issue that attendee’ coding levels have huge gaps; there could be complete beginners, and there could be people who’re already familiar with R. Now, the attendee will have the same level of R proficiency after a few workshops. That allows us to move further to new topics.

Also, using Github seems like a good idea. In the past, sharing my codes and data sets in open labs was troublesome and inconvenient. Now, students can always go to my Github repository to look at past materials without asking for access from me.

I am happy to see that our program is developing, and we are able to help more fellow students step in a new field. I invite all to our open labs regardless of your level; we welcome you to take a journey to explore the facts behind data.

Python Open Labs S.2018: How it all went down

Lab Structure and Technicalities:

This semester I returned to running the Python Open Labs with another student intern and upon started, we had some discussions about our ideas for structuring the labs over the next few months. We decided to stay consistent with the formatting of the lab: started the semester with the starter kit (Python fundamentals), and continued to build on those fundamentals every week (see weekly blogs here).

The first change we implemented was switching to Jupyter notebooks as opposed to running the labs on the console itself. For me, this was quite a challenge! I had never used Jupyter notebooks, it seemed like a strange and abstract way to code, one that was definitely built with user experience in mind and something that was unfamiliar to me on all fronts. I spent a few weeks playing around with its functionality – the headings and commenting features as well as common errors that can happen (e.g. running every cell to ensure the code works). Once I got the hang of it though, everything changed! I have tasted and enjoyed the Jupyter notebook kool-aid and there is no going back.

One of the best features of the Jupyter notebooks is the UX of its layout. The simplicity of its layout makes the code that much easier to parse out and build upon. Organizing the code, or in our case, entire lesson in the Jupyter notebooks meant that we could share the lessons with the class at the end. Prior to the lesson, we would come up with the content and create the notebook in full. Then, we would go back and recreate another lesson without the completed cell blocks so that we could use the prompts and live code. At the end of the lesson, we shared the lessons in full with the class to ensure that students could spend time going over and reviewing the examples and problems with the full code (i.e. all answers) readily available.

That being said, we also changed how we shared the lessons with the students. This semester, we maintained a google folder with all the Jupyter notebook lessons and .pdfs and shared it with the students that came. A welcome change considering the amount of paper it took last semester to print and share each lesson! We also received great feedback on the organization and sharing of the Jupyter lessons so that’s definitely something we will keep in mind and hope to continue next semester.


This semester, the range of programs represented by the students who attended the labs were incredibly diverse. Students from the School of Professional Studies and the School of International and Public affairs were the most consistent, however we did encounter students from Journalism, Economics, and Latin American Studies as well. Although it’s a challenge to encourage students to attend on a regular basis, we were able to see some faces week after week, and sharing the lessons on an accessible drive folder ensured that those who were not able to make it in person but interested in continuing to expand their coding horizons could keep up.

Most enjoyable Lab:

The lab we held on python classes in early April was my favorite lab – partly because I taught the entire session on my own, but mostly because I structured the lesson in a way that focused on fewer and more intense practice problems. Instead of going through quicker and shorter sample problems I thought I would try create problem sets that incorporated functions as well to keep things interesting and to offer students a challenge. The class was well received and you can find the lesson on the DSSC blog if you want to check it out!

Ideas for future labs:

To conclude this post, I will underline two suggestions for future lab lessons:

  1. Plan out lessons before the labs

It would be great, in my opinion, to post a description of each lab before it happens to outline the structure of the lab and the concepts covered. This was the route we took for the R open labs towards the end of the semester and it worked really great – I am excited to try it out for Python as well!

  1. Continue to market to a diverse group of students

Before commencing in the fall, I would like to spend some time strategizing on how to market to different departments. The open labs are such a great way to learn a coding language – they are free (!!!), but more importantly the communal vibe is optimistic and welcoming and a great space to learn.

I have learned so much this year in preparing and leading labs and now that they have wound down for the summer, I feel motivated to continue to market the space and engage students across all departments.

Computationally Detecting Similar Books in Project Gutenberg

As one of the first digital libraries, Project Gutenberg has lived through a few generations of computers, digitization techniques, and textual infrastructures. It’s not surprising, then, that the corpus is fairly messy. Early transcriptions of some electronic texts, hand-keyed using only uppercase letters, were succeeded by better transcriptions, but without replacing the early versions. As such, working with the corpus as a whole means working with a soup of duplicates. To make matters worse, some early versions of text were broken into many parts, presumably as a means to mitigate historical bandwidth limitations. Complete versions were then later created, but without removing the original parts. I needed a way to deduplicate Project Gutenberg books.

To do this, I used a suggestion from Ben Schmidt and vectorized each text, using the new Python-based natural language processing suite SpaCy. SpaCy creates document vectors by averaging word vectors from its model containing about 1.1M 300-dimensional vectors. These document vectors can then be compared using cosine similarity to determine the semantic similarities of the documents. It turns out that this is a fairly good way to identify duplicates, but has some interesting side-effects.

Here, for instance, are high-ranking similarities (99.99% vector similarity or above) for the first 100 works in Project Gutenberg. The numbers are the Project Gutenberg book IDs (see, for instance, this index of the first 768 works).

1.  The King James Version of (10) -similar to- The Bible, King James Ver (30)
2.  Alice's Adventures in Won (11) -similar to- Through the Looking-Glass (12)
3.  Through the Looking-Glass (12) -similar to- Alice's Adventures in Won (11)
4.  The 1990 CIA World Factbo (14) -similar to- The 1992 CIA World Factbo (48)
5.  Paradise Lost             (20) -similar to- Paradise Lost             (26)
6.  O Pioneers!               (24) -similar to- The Song of the Lark      (44)
7.  O Pioneers!               (24) -similar to- Alexander's Bridge        (91)
8.  Paradise Lost             (26) -similar to- Paradise Lost             (20)
9.  The 1990 United States Ce (29) -similar to- The 1990 United States Ce (37)
10. The Bible, King James Ver (30) -similar to- The King James Version of (10)
11. The 1990 United States Ce (37) -similar to- The 1990 United States Ce (29)
12. The Strange Case of Dr. J (42) -similar to- The Strange Case of Dr. J (43)
13. The Strange Case of Dr. J (43) -similar to- The Strange Case of Dr. J (42)
14. The Song of the Lark      (44) -similar to- O Pioneers!               (24)
15. The Song of the Lark      (44) -similar to- Alexander's Bridge        (91)
16. Anne of Green Gables      (45) -similar to- Anne of Avonlea           (47)
17. Anne of Green Gables      (45) -similar to- Anne of the Island        (50)
18. Anne of Avonlea           (47) -similar to- Anne of Green Gables      (45)
19. Anne of Avonlea           (47) -similar to- Anne of the Island        (50)
20. The 1992 CIA World Factbo (48) -similar to- The 1990 CIA World Factbo (14)
21. The 1992 CIA World Factbo (48) -similar to- The 1993 CIA World Factbo (84)
22. Anne of the Island        (50) -similar to- Anne of Green Gables      (45)
23. Anne of the Island        (50) -similar to- Anne of Avonlea           (47)
24. A Princess of Mars        (60) -similar to- The Gods of Mars          (62)
25. A Princess of Mars        (60) -similar to- Warlord of Mars           (65)
26. The Gods of Mars          (62) -similar to- A Princess of Mars        (60)
27. The Gods of Mars          (62) -similar to- Warlord of Mars           (65)
28. Warlord of Mars           (65) -similar to- A Princess of Mars        (60)
29. Warlord of Mars           (65) -similar to- The Gods of Mars          (62)
30. Adventures of Huckleberry (73) -similar to- Tom Sawyer Abroad         (88)
31. Tarzan of the Apes        (75) -similar to- The Return of Tarzan      (78)
32. The Return of Tarzan      (78) -similar to- Tarzan of the Apes        (75)
33. The Beasts of Tarzan      (82) -similar to- Tarzan and the Jewels of  (89)
34. The 1993 CIA World Factbo (84) -similar to- The 1992 CIA World Factbo (48)
35. Tom Sawyer Abroad         (88) -similar to- Adventures of Huckleberry (73)
36. Tarzan and the Jewels of  (89) -similar to- The Beasts of Tarzan      (82)
37. Alexander's Bridge        (91) -similar to- O Pioneers!               (24)
38. Alexander's Bridge        (91) -similar to- The Song of the Lark      (44)

The first pair here is of duplicates: both are King James Versions of the Bible. The same is true of lines 5 and 8, and lines 12-13: they’re just duplicates. All the other works are members of a novelistic series. Lines 2 and 3 are Alice in Wonderland and its sequel. Lines 6 and 7 are Willa Cather novels of the Great Plains trilogy. Lines 16-19, and 22-23 identify the Anne of Green Gables novels. Lines 24-29 are a cluster of Edgar Rice Burroughs of the Mars series, and there is also another cluster of Burroughs novels, the Tarzan series, at 31-33. Line 35 shows Mark Twain novels of the Tom Sawyer and Huck Finn world. The algorithm even identifies the two 90s CIA World Factbooks as part of a series.

When I lower the cutoff similarity score, I can get even more interesting pairs. Less-recognizable series, like Paradise Lost and Paradise Regained, have similarity scores of around 97%. At that level, completely unrelated novels with the same settings, or written in around the same time period (Victorian novels, for instance), begin to cluster together.

The chart below shows a PCA-reduced 2D vector space approximating the similarity between books 1-20. There are interesting clusters here: the American Constitution and Bill of Rights cluster together, along with the Declaration of Independence, the Federalist Papers, and Abraham Lincoln’s first inaugural address. Lincoln’s second address, however, clusters rather with his Gettysburg Address, and John F. Kennedy’s inaugural address.

PCA of PG Books 1-20

PCA of PG Books 1-20

Non-fiction seems to be clustered at the bottom of the chart, whereas fiction is at the top. Quasi-fictional works, like the Book of Mormon and the Bible, are in between. Similarly, Moby Dick, a work of fiction that nonetheless contains long encyclopedic passages of non-fiction, lies in the same area. The most fantastical works, which are also the three children’s books, Peter Pan and the two Carroll novels, cluster together in the upper left.

As always, the code used to create all of this is on GitHub. I welcome your comments and suggestions below!

>>> myPythonOpenLab = (“A Holistic Approach to Python”)

Overview of the Fall 2017 Python Open Labs

Python Open Labs reconvened in September with three new DSSC interns. Unlike my colleagues studying Computer Science and Computer Engineering, I am a Human Rights Masters Student at SIPA/ISHR. While we come from different backgrounds and levels of expertise in Python, this semester has been both productive and challenging as we worked to leverage our abilities and share our experiences in Python in an accessible and comprehensible format.

Teaching Style:

Our approach to the workshops was simple: teaching in a lecture-style format week after week– a slow progression to build on concepts introduced the previous session. We collectively agreed that Python is a coding language that is relatively easy to grasp given that the correct tools are available. Moreover, given its relevance to a variety of academic disciplines and careers, we strove elicit a positive reception from students attending these sessions. The latter point was quickly reciprocated by learners, who responded really well to the linear format of the labs. We welcomed students from a wide variety of schools, including Teacher’s College, SIPA, Urban Planning, and Journalism.

There was always too much material (or too much ambition — call it what you may), and we tried our best to manage time, but often found that sessions ran over their allotted time and continued on to the following week. Our pseudo lectures always included practicing concepts (eg. classes, dictionaries, loops etc.) a few times over the course of the 2 hours to see how students interpreted and applied them. We felt inspired by the variety of solutions that students shared! It has been incredibly rewarding to watch students become more confident in their abilities to write code and utilize their fluency of Python applications to solve a given exercise or problem as we delved deeper into the language.


As a student disengaged from Python in my day to day studies, I found myself trying to push my fellow interns to simplify material and slow down! This has definitely been one of our most obvious challenges as moving through material too quickly has caused confusion and an influx of questions via email post-lab. It has been difficult at times to gauge exactly how students are responding to a specific concept such as list methods (a particularly complex lesson), as we receive little to no participatory feedback from students in the lab. I hope to challenge this next semester by pausing more often for feedback and for creating a space where active dialogue between interns and students allows us to work in sync.

Looking Beyond:

Between the three interns, we have had great fun amalgamating our skills, strengths, and weaknesses in and around the lab to optimize students’ experiences. In doing so, we have learned more than we imagined about our own approaches to the language as well as teaching habits. Next semester, though my teammates will be graduating and moving on from Columbia, I am very much looking forward to continuing the labs in the aforementioned format.

One of the ways I hope to further enhance the workshop format is to focus on team, or group learning by way of small projects or discussions to accompany the lessons. Collaborative learning not only promotes learning by bringing people of different skill levels together, it also replicates the type of environment (think career) in which one would participate as a professional with coding expertise.

I also hope replace the weekly handouts I would create for lessons with an electronic format. I hope to share lesson structures and practice problems in a blog post or a Jupyter notebook to alleviate the environmental impact of printing paper.

Already looking forward to the Spring semester! All questions, comments, and feedback are strongly encouraged.

Please visit the DSSC blog for Fall 2017 Python Open Lab weekly summaries and materials.

R Open Lab: Looking Back and Moving Forward

In the past academic year, I worked as a teaching intern with the Digital Social Science Center and Digital Science Center, hosting R open labs and workshops. Most of the people at R Open Lab are using R for their research projects; therefore in the past semester, I tried out several different teaching practices in search of the best way to enable the participants to harness the power of R as a research tool.

New Teaching Practices

  1. Peer learning: It was the first method I tried out and it seemed to be the most helpful one. By talking with people with similar research interests and learning from each other’s experiences, many participants expanded their professional networks and found a better way to apply R to their research. Exchanging learning experiences with people from different academic backgrounds helped the participants to gain a broader understanding of the functionality of R. However, creating a stimulating environment for peer learning but not making the participants feel pressured can be challenging.
  2. Group discussion: I tried to encourage group discussion by throwing open-ended questions at the participants during the instructions. The problem with this method is that more often than not, the discussions were very shallow, probably because the questions were not interesting enough. Looking forward, it might worth trying to prepare a thought-provoking question for each R Open Lab session and make group discussion as a standalone part.

Challenges and Solutions

Based upon my experience with both workshops and open labs, and the feedback from the attendees, the challenges facing the R Open Labs and possible ways to improve them are as follows:

  1. Due to lack of a clear syllabus for the whole semester and its self-paced nature, most participants are not motivated to attend R Open Labs regularly. To help participants have a better idea of the progress of open labs, we can try to post topics for each session a month in advance on the workshop list. We can also maintain a GitHub repository and upload materials used for instruction after each session so that other interested individuals could easily access them.
  2. Although the Swirl package is an immensely helpful starter kit, most people new to coding and statistics still struggle with very basic R operations. Furthermore, the huge gap between basic operations and being able to fully implement a research project can be frustrating and overwhelming. Therefore, instead of Swirl, we could provide participants with some sample code and links to GitHub repositories, so that they could use them as a starting point for their own projects and learn best practices of building a project with R
  3. Some questions are frequently asked by the participants, so we can probably provide participants with a list of FAQs and the general answers to maximize efficiency.

To conclude, this internship motivated me to think deeper about teaching and better understand people’s needs. It was an incredibly challenging and rewarding experience. As data analysis becomes increasingly important in a multitude of research areas ranging from biology to history, R is becoming an essential research tool. I hope by continuing to make improvements to the contents and structure of R Open Lab, it could serve as a platform to introduce R as a useful digital tool and promoting collaboration between scholars interested in R.


Python Open Labs – Spring 2017


This post details my experience as a Digital Science Center Teaching Intern for the Spring ‘17 semester, wherein I hosted the weekly Python Open Labs to teach programming with python. This internship is my first full fledged teaching experience where I got complete freedom to choose the topics that I introduced during Python Open Labs and the way I conducted  weekly sessions and a couple of workshops on python through the semester. My internship this semester was in continuation of the year long Digital Science Centers Teaching Internship that I secured in Fall 2016. A detailed post about my last semester’s experience can be found on this link: Muggles Speak English. Pythonistas Speak Python

This semester I continued introducing new topics in Python, building on top of the basics of programming that were covered in the open labs held in Fall 2016 semester. The broad range of topics included object oriented programming, web scraping, file and data handling etc which included applying the basic concepts of programming from past sessions in a cohesive manner to solve relatively complex problems. In particular, I had two motives for the sessions this semester: 1) To be as inclusive as possible and hence I tried to keep the open labs generic, catering to the needs of attendees from various Columbia Schools such as Law, Journalism, Medical, SIPA etc rather than restricting ourselves to a particular domain like data science or scientific programming. 2) To introduce Python as a helping tool that would make the day to research, academic and professional tasks easier for the attendees, a majority of whom had little or no prior programming experience. I ensured that everyone who attended these labs had something to take away that would facilitate their encounters with programming.

In the first half of this semester I included some advanced topics in python such as object oriented programming, file I/O and some data structures. This was in continuation of the basics of programming with python that had already been covered during the open labs sessions from previous semester. At this point of open labs the main challenge I faced was to ensure that people attending the labs were able to grasp the concepts very well and tie the multiple concepts together as we moved towards more complex applications and problem solving. This required extended practice problems and discussions during the two hour long weekly open labs. This approach towards teaching meant less number of slides and more examples to give a better perspective to everyone, which also helped me get better at explaining concepts to others and be involved in detailed discussions.

The second half of semester was concentrated towards covering on-demand topics from the people who attended Python Open Labs. As a result we got to cover a good number of python libraries and topics like BeautifulSoup for web scraping, csv module for csv file handling, lxml parser for XML parsing and the requests module for handling web requests. These are some of the topics that I had initially thought to be too complex to be introduced in the open labs, but it turned out to be a pleasant experience that many of the attendees specifically asked for these topics which related with their academic and professional works and they were able to better relate with these topics combined together with the basics of programming. It ensured that they were able to practically apply the programming concepts that were covered throughout the open labs to their advantage and also gave me an opportunity to learn many new things about Python as a programming language.

To conclude this post I must mention that the past one year as a teaching intern with the Digital Science Center, Columbia University Libraries was a wonderful enriching experience that gave me a good insight into teaching a self-designed open course, an opportunity to interact with many people from various backgrounds and to brush up my skills on python. I am thankful to Columbia Libraries for providing this great opportunity for students.