Introduction

On May 30th, I had the pleasure of presenting at the MinneAnalytics Data Tech Conference with @KatieKodes. Our talk was on Evangelizing Python for Business. Here is the summary of thetalk:

Pythons simple structure has been vital to the democratization of data science. But as the field rushes forward, making splashy headlines about specialized new jobs, everyday Excel users remain unaware of the value that elementary building blocks of Python for data science can bring them at theoffice.

Join us for a conversation about bringing Python out of IT and into the business. Well share challenges and successes from writing tutorials, teaching classes, and advocating adoption among newusers.

I really enjoyed the presentation and received a lot of positive feedback. As a result, I wanted to capture some of the ideas in a post so that the broader community could see it and generate some dialog on tips and techniques that have worked for you. The actual content in this blog is closely tied to our presentation but contain some additional idea and thoughts that I may want to expand on in futureposts.

Finally, thanks to Katie for suggesting the topic and allowing me to partner with her on the presentation. I think having two different but complimentary backgrounds really helped the presentation cover a lot of differentperspectives.

What are you trying toaccomplish?

Before jumping right in to training everyone how to use python, it is important to understand what the goal is. This diagram shows where I think python fits into the IT ecosystem of a relatively largeorganization.

[IT Continuum]

My experience is that Corporate IT functions are good at implementing large ERP-type applications or major cloud integrations like SalesForce. I suspect most medium to large organizations have some flavor of these applications in place (and likely manyothers).

In some cases, the IT organization will have a team to build custom developed applications using .NET or Java. Obviously, there will be a lot of Access and Excel sprinkled through theorganization.

This distribution leaves a huge gap. There are problems that are not big enough for an off-the-shelf-solution or worth developing a full application. However they are critical enough that Excel + Access is not a good solution. Vicki Boykis does an excellent job of talking through this problem on a broader scale. I encourage you to read IT runs on Java 8 for a well written perspective on the problem of the hype of IT vs. the reality in manyorganizations.

I contend that python is an excellent candidate to fill in that gap and that it does not need to be solely the realm of corporate IT. Super users and other domain experts can (and should) be trained in using python to fix the problems that they face on a dailybasis.

Python is almost 30 years old. Over its lifetime, it has always been known as a great glue language. It has gone through a phase where it demonstrated success as a strong language for web development with tools such as Django and Flask (and many others). Now more recently, it is widely used in Data Science. All of this power and flexibility leads me to wonder, why cant we leverage it for other tasks within the organization that have not had much support from IT? We can do so much better for our people if we give them more tools besides Excel and VBA!

Pick Your Battles:People

If you have a similar experience and are interested in trying to use python to fill that gap, how do youproceed?

The first step is figuring out which people are good candidates for learningpython.

My default profile is the person that is viewed as the Excel guru and has a strong understanding of the business process. In any group of 5-10 Excel users there always seems to be at least one person that knows the ins and outs of the Excel tools as well as the business problem. This combination can be a good place tostart.

However, there are some people that are more interested in collecting a paycheck than trying to automate the boring stuff. Job insecurity is a real issue that needs to be factored into the people part of the process. It is important to emphasize what the benefits to the employee will be if they automate some of the mundane parts of their job. In my experience there is more than enough work to goaround!

Given the rise of python in Data Science, there is likely going to be more general awareness of python than there was 10 years ago. One implication of this is that people may be more willing to agree to try python out. However, having interest in python is not sufficient. There is actually a bit more of a gap to get someone from I can cut and paste VBA to I can cut and paste python to solve myproblem.

[I Can Learn Python]

Unfortunately I dont think there is a simple checklist to determine who is a good candidate to try to teach python. I do think that extra work outside of the 9-5 daily job is needed. If you embark on this process, you will relatively quickly get a sense for who is really willing to work at it and who isnot.

There are lots of additional dynamics when trying to teach co-workers how to usepython:

• Are people doing this only because the boss expects themto?
• How much outside of work time should be dedicated to thelearning?
• Can people of different levels learn together? What if the senior person really struggles during theprocess?

Despite these potential gotchas the payoff for the organization can be very large. Think about how much could be done in your job if you had a team of 2-3 python-savvy experts that could help youout!

Adjust to youraudience

I can almost promise that once you embark down this path of trying to bring python into your organization, you are going to have to adjust to the audience. Some people are going to be much more eager than others. The style that you used to learn python is likely going to be much different from your co-workersneeds.

You will need to be prepared to adjust and take cues from the audience. In addition, there may be broader organization changes that cause you to shift focus. For instance, what happens if more departments are interested in your activities? What will you do if IT or management push back for variousreasons?

In addition, keep in mind some of thesethoughts:

• How would you scale if more peoplejoin?
• How can you keep people engaged as they move at differentspeeeds?
• How much take-home content do you need toprovide?
• How much time can you carve out toteach?

Imitate BetterTeachers

One of the biggest challenges with spreading python in an organization is that knowing python does not make someone a good teacher. In fact, the way you likely learned python is not the same way others will want tolearn.

Once you start the teaching process, here are a few tips andtricks:

• Dont be ashamed to bring in better teachers or others with pythonknowledge
• Bring in lab assistants to help with the minorgotchas
• Build a team approach so that it does not just fall on yourshoulders
• Identify a blog, YouTube Channel or other resource that can be used inside and outside thetrainings.
• Leverage any outside meetups/groups in the area so that the training can happen outside of workhours.

Learn with theyre trying toaccomplish

It is really important to understand what the students are trying to accomplish. Do they want to move into a Data Science role? Do they just want to be more efficient in the currentrole?

They are likely excited to try to solve some sort of real-world problem in their day to day jobs. However, it is somewhat tricky to figure out the right problem to tackle first. You will need to steer them to solvable problems that they can maintain in thefuture.

Here are some reasons to automate aprocess:

• Save time - This is the first instinct but may not be the bestreason.
• Get a quick win - Prove the value ofpython.
• Learn about the problem - How solvable is theproblem?
• Development an improvement mindset - Get people to think about their problemsdifferently.
• Process is boring or has lots ofmistakes.

In addition, certain problems are better for python automation than others at this early stage in the process. Here are some characteristics of goodproblems:

• Large data sets - 100,000+ rows of data inExcel.
• Well understood problem - Focus on learning python not theproblem.
• One step in a long process - Start with a manageable piece of theprocess.
• Text manipulation - Excel is used for lots of string manipulation tasks that python can do verywell.
• Formatting of output does not matter - Focus on core data wrangling, not making it lookpretty.

As a python expert, you may be tempted to start with the biggest challenges first. I recommend starting with something a little smaller - even if the time savings is small. There are going to be lots of hurdles and challenges outside of the specific problem. Do not bite off more than you can chew when first gettingstarted.

People are going to be naturally skeptical so proving the value early iscritical!

Pick your battles:process

There are lots of way to teach people how to use python. It is really important to think about all the availableapproaches.

[Google Search for python]

How do you decide where to start? It is not trivial to find and curate the content for your specific needs. A lot of it feels like reinventing the wheel- which can be discouraging. However, that might be ok if it helps you learn more and build better content for yourattendees.

As you search for sources of content, try to keep a blend of various contenttypes:

• Formal onlinecourses
• Custom developed lessons focused on your datasets
• Balance of formal lesson approach vs. real-worldexamples
• CS 101concepts

Once you do startteaching:

• Reassure people that this can beconfusing
• It is ok if you forget things. It is ok to google or refer to pastexamples.
• Part of the process is figuring out how to getun-stuck
• Gamification through chocolate, treats, swag is great for keyconcepts

The entire process is not just about teaching python language syntax. It is about teaching people to solve problems in a unique way. Shifting from an Excel-first mindset to a python-first mindset takestime.

One FinalNote

During one of the talks at Data Tech, I had the pleasure of listening to Peter Wang, the CTO of Anaconda talk about the role of the Data Scientist. One of the the really interesting comments he made is that we are moving into a world where basic data literacy is going to be a requirement for success in the workforce. Today we dont expect you to be a Math major to do math or an English major to write. In much the same way, data literacy is going to be required of many more people and is not going to be just for DataScientists.

I firmly believe that we can and should bring in more tools like python into our organizations so that we can be more efficient but also start to increase the data literacy of the entire organization. These are two mutually benefical and reinforcing goals to keep inmind.

Takeaways

Five to 10 years ago, it might have been quite an uphill battle to try to bring python into your organization to solve your business problems. With the rise of pythons popularity in the Data Science world, you will have a much smaller hill to climb to convince others that python can help them solve their problems - even if it is not formal DataScience.

Once you get your organization on-board with the idea of using python, there is a lot of work to implement those ideas. This article includes a high level framework for thinking through theprocess:

• Know what youre trying toaccomplish
• Pick your battles:people
• Know what they are trying toaccomplish
• Adjust to youraudience
• Imitate goodteachers
• Pick your battles:process

I hope you found it useful. I am contemplating building out some more content for a Lunch and Learn series. If you have any ideas, tips or contents that you have found effective, feel free to share any of your successes in the commentsbelow.