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Install dbt on Win10 – April 2021

I was getting a little frustrated with the web interface of dbt cloud, and just wanted to feel more “in control” of my environment. Which lead to this twitter thread:

Which led to lots of good recommendations (for Atom, POP SQL, vim+tmux, DeepChannel, and some others) but ultimately I settled on VS Code after a few auspicious DMs.

The process to install dbt on Win10 isn’t exactly friendly for an analyst using dbt without engineering experience, so I wanted to share my journey and hopefully make yours easier too.

Step 0: Install Python

Before you do anything else, install python. Make sure to check the box to “Add Python to PATH”. If you don’t, you can only run it explicitly.

low quality screenshot – but peep the box at the bottom.

If you miss this step, you have two options:
1 – re-install of python and check the “Add Python to PATH” box.
2 – manually add the PATH for python; one example linked here.

To confirm it’s working, open the command line and enter ‘py –version’. It should return the version of python you installed. If you get an error, most likely it is a PATH issue.

Step 1: Install VS Code + MS Build tools

First, download links: VS Code, MS Build tools.

As a quick call out, you need MS Build tools for MSVC v140 or higher, which is an optional component of the C++ build tools.

more low quality images, but check the right box.

The MS Build tool install takes a bit of time, so I would do this when you have a little bit of time (15 minutes or so), and you will need to restart your computer.

Step 2: Config your VS Code Environment

There are a few items that need to be done to get VS Code ready for dbt, so I will list them here.

  • Open the command palette (Ctrl+Shift+P), and type ‘Python: Select Interpreter’. It should then bring up and allow you to select your python interpreter.
  • Add the dbt power user plug-in.
  • Take a look at this article for more suggested plug-ins from the dbt team.
  • Open your command line, and update pip to the latest version with the command ‘pip install –upgrade pip’.
    • If you get an error here, you may need to run it with the ‘–user’ flag as well, but I got that behavior inconsistently.

Step 3: Install dbt on Win10

If you’ve made it this far, congrats. If you are finding this article because your ‘pip install dbt’ isn’t working, go back to the top and work through the above steps first.

In your command line, run the following: ‘pip install dbt –no-use-pep517 cryptography‘. The dependency chain is broken somewhere and this flag fixes it. Do I know why? No. Do I care? Also, no.

This takes a few minutes to run (5-10 mins), but when it’s done, you can check by running ‘dbt –version’ in the CLI. It should return the latest version (as of this writing, 0.19.1).

Lastly, I recommend running ‘dbt init’ to set up your initial ‘.dbt’ folder that holds your profiles.yml file to allow you to connect to your data warehouse. If you don’t run it, you will just need to create that file by hand later. Since that file contains your credentials, it is best practice to put that in another place outside of your source control.

And with that, you are ready to connect to your repo and begin working on your dbt project. And lastly – share your Ws on twitter!

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Medium Data: MS edition

This video is for your data that is too big for an excel spreadsheet and too small for a data warehouse. I like to refer to this as “Medium Data”.

I can think of many times I needed this during my career. Typically, the “medium data” scenarios were related to snapshotting historical data weekly and showing changes in trends over time. One good trick I learned in one of my first jobs was to snapshot my CRM order book every week and save it in a CSV format. Eventually, that got too large for my meager tools, and I started aggregating, losing data, or other hacks (i.e., multiple excel files). Linking excel files together was basically enough to motivate me to learn SQL. With Azure, you can easily scale into the next size of data and keep your analytics rolling. Check the video below for a 15 min walk through.

Going from CSV to SQL in 16 minutes

I’ve just shown the basics – but there are some awesome articles out there that can go more in-depth, including some great automation.

The core tutorial in this video can be found here:

To really amp it up with automatic import, check out this:

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4 Soft Skills to Amplify your Analytics Career

Soft Skills

I think Nate really said it best with “Technical ability is overrated.” When I’m looking to make a hire as a manager, there are four skills that I’m looking for when I’m interviewing and continually assessing for my reports.

  • Curiosity – a childlike ability to keep asking, “Why?”
  • Accuracy – balancing perfect vs. “close enough” for your data
  • Organization – ability to break down tasks into small chunks and reliability execute on them
  • Empathy – actively listening & seeking to understand, and communication centered on your audience

I’ll breakdown each of these with characteristics with an example, an interview question testing for that skill, and a way that you can improve in each of these areas.


The first soft-skill to have in your repertoire is Curiosity. This often means you are always asking questions and aren’t afraid of asking them. I find myself often hedging a bit in this area by saying something like, “Sorry to be dense about this, but can you explain?”

When I’m dealing with new subject areas, this often will mean pausing conversations to understand words and what they mean. “Net Sales” often means something different in the sales organization vs. the finance organization, so getting to certainty on terms is critical. Frequently, digging into these types of questions can get uncomfortable, especially if the person asking you for help doesn’t know the answer or can’t define it well.

In interviews, there are a couple of ways to get at this skill. One way is to probe about problem-solving: identifying and solving tough problems. This is a bit open-ended, so making sure to redirect the question to your underlying objective is advised. Another way would be to model the behavior and assess how the candidate handles it. A curious person should be able to match your energy and get excited at the premise of jumping down the rabbit hole on a specific subject.

Not everyone comes by this skill intuitively, and for those of you in that bucket, there are some great frameworks to unlock a curious mind. My favorite comes from Sakichi Toyoda, of Toyota fame, and is called the “5 Whys”. More on this below, from Wikipedia.

The key is to encourage the trouble-shooter to avoid assumptions and logic traps and instead trace the chain of causality in direct increments from the effect through any layers of abstraction to a root cause that still has some connection to the original problem.


Up next, we have accuracy. As a data analyst, it is critical to be right an overwhelming majority of the time. You don’t need to be perfect, and in fact, perfect is the enemy of good. This can be tricky to do well because, as an analyst, you usually are the least knowledgeable person in the domain of the problem at hand. Getting accurate goes hand-in-hand with Curiosity because you must constantly bring assumptions to light. There is a lot of digging to do.

One particularly thorny problem to deal with is sales data within a CRM. It is highly speculative. It changes frequently. In short, it is unreliable. I have found working with this type of data greatly benefits from a common snapshotting period. Just take a backup every Friday at 5 pm. Do your analysis on a static copy, and figure out how to surface changes to key data fields (like close date or opportunity size). This allows you to bring accuracy to constantly shifting data sets.

Part-and-parcel with my comment above, in interviews, I like to probe around “soft data” and see how the prospective analyst has added certainty when data is vague or unreliable. For analysts with finance experience, asking questions about how they dealt with financial periods and month-end processes since those can bottleneck key metrics (like revenue). In that same vein, asking how, in detail, key metrics were calculated will shed some light on their accuracy approach.

Part of why I favor people with accounting backgrounds coming into analytics roles is that accuracy comes with the package. This learned through long hours of grinding out Excel spreadsheets and cross footing numbers one, two, three times. Thankfully, there some tricks to getting better at Accuracy, and it comes along with Curiosity. My favorite is applying a simple checksum technique and ensuring my source & target are equal on an aggregate basis. If it’s wrong – take time to dig into why and understand why what you thought was correct isn’t. The second trick is a checklist, especially for common tasks (say, deploying code to production). Taking time to document exactly how something is done not only reduces the cognitive load for the next time but improves the quality of your work.


To me, being organized is not about a tidy desk. It is about being to tackle a problem from beginning to end. It means being able to cut through ambiguity and deliver something excellent. An underrated thing about being organized is the skill of breaking down big, hairy problems into small, actionable next steps. To me, an organized person always knows what to do next.

One example where I think Organization comes into play is the meetings you take with your stakeholders. I always try to make sure to recap actions at the end, as a habit to do every meeting that I am in. If I have actions, I’ll write them down but leave others to track their own action.

In an interview, I’ll test for this skill by probing about systems of work. “How do you organize yourself?” or “How do you know what to do next?” are questions that can get to the heart of this. For an analyst, this “system of work” is at the heart of getting more interesting work. Shipping early and often is critical to getting into the more interesting bits of work, so building your own work system is critical.

Getting better at Organization can come in many forms, but looking back on my career, this didn’t come naturally to me. I recall missing a key deadline for some sales analysis for my CSMO. He asked me why I didn’t have what he needed, and I replied, “I was busy.” His response, which put the fear of God into me, was “we are all busy” as he rolled his eyes. Needless to say, I went and picked up David Allen’s Getting Things Done. If you don’t have a work system, GTD is a great starting point, and I still use bits & pieces every day.


You want to seek to understand and always bring empathy to the conversation with your stakeholders. They are taking the time to teach you about their business and its problems, so be a respectful skeptic. Remember, you want these folks to back to you the next time they have a problem! Empathy also means centering your communication on your stakeholders, so they feel heard even when you are giving bad news.

One thing I did as I got more comfortable in my analyst role was “rounding” with key stakeholders. This would mean making space to get coffee, drop-in late afternoon as things are winding down, and occasional lunches. These conversations were often more personal than business, but by the time conversation turned to work, we were both comfortable and ready to listen to each other. At one point, I had a couch in my office, and we joked that people would come by for therapy1.

Testing for empathy in an interview is a bit of a challenge. For me, I try to observe if they are listening versus waiting for their time to speak. I can go on for a bit too long at times, so when I catch myself wandering, I also casually check for body language in the candidate to see if they are really listening. Ultimately, this is one of the hardest skills to judge in an interview for me. But I’m actively trying to find ways to measure this quickly and accurately.

As an analyst, you are pretty sharp and usually have a good idea of how to solve a problem as soon as you hear it. To be more empathetic, slow down. Fall in love with the problem. You need to see the problem clearly enough that you can come up with a solution that exceeds your stakeholder’s expectations. Repeat back what you think you heard. This is especially important if the person across the table from you is from a different background, as cultural context can get in the way of great communication. People should always leave a meeting with you feeling like they were listened to.

Final Notes

With Curiosity, Accuracy, Organization, & Empathy, you can be a great analyst. These characteristics all build on each other and help you build a reputation as a reliable, skillful person who can deliver business value. People will seek you as the analyst to solve their problems. Yes – it’s great if you write some SQL, python, or R, but these soft skills will allow you to be 10x greater than someone much stronger technically. After all, I truly believe that success as an analyst should be measured by is how they enable the people around them. A great analyst doesn’t 10x themselves; they 2x (or more!) everyone around them.


1This is REALLY HARD to do in a remote environment. No idea how to replicate this digitally but I’m sure there is a way…

analytics excel retro

NBA Bubble Sim: A Retrospective

One thing that I really enjoy as an analyst is creating new models – and expanding them. I made a version of the Bubble sim with 1m+ scenarios, for example (that will turn into a blog post here at some point). But I rarely maintain the focus or energy to take a look at it after the fact to determine “how good was it at actual predicting the future?”1 I’m aiming to change that with this real-life example of this NBA model. So with that said, let’s dive in.

Predicting individual games

Using ELO to predict individual games should theoretically massively improve the predictive ability of the model versus, say, coin flips. However, as we will see, that was really not the case.

quality of prediction for individual games

Ultimately, we were just slightly better than coin flips. Sort of disappointing if I’m honest. I do think there is some context that ELO is particularly bad at explaining, which we can distill into the statement “ELO overstates the relative strength of teams that have clinched a playoff birth.”

I’ll dive into this at the end, as I think some faulty modeling by the NBA around this assumption lead to some crappy basketball being played.

Predicting which teams made playoffs

When I look at the 1000 scenarios in aggregate (instead of a game by game basis), a much clearer picture of the model and its effectiveness is painted.

quality of prediction for making playoffs

Looks pretty good! A damn good model. HOWEVER – given that for all intents & purposes, 15 out of 16 playoff spots were guaranteed, this really is a false narrative about the effectiveness of the model.

Reducing scope to measure uncertain outcomes

For the purpose of this analysis, I will take a look at the quality of the model as it relates to 3 teams – the New Orleans Pelicans (NOP), the Memphis Grizzlies (MEM), and the Portland Trailblazers (POR). This is because these are the 3 teams competing for the final playoff spot, so by getting better at predicting these teams, we improve the efficacy of the entire model.

predicting outcomes for POR, MEM & NOP

I can’t say these updated stats are particularly great. We are more accurate here than we were for predicting specific games, but far from some certain enough to do something like gamble on this model reliably. Even knowing what we did going into the NBA bubble, Portland, who ultimately made the playoffs, only had a 29% chance to make the playoffs.

Incorporating some modifications

One obvious observation as the bubble games continued was that “ELO overstated the relative strength of teams that have clinched a playoff birth.” With this knowledge, I started tweaking my model to accommodate this new information. Ultimately what I landed on was to reduce the ELO for teams that have already clinched by 20%. This number is totally arbitrary and based on gut feel. I also assumed the eastern conference was de-facto clinched based on the players who opted out or were injured for the Wizards.

Given the relatively poor performance of the model, I was seeking to explain the following data points:

  • The Bucks & Lakers were playing very poorly.
  • The Suns & Blazers looked unstoppable.

With the modification of the model to reduce ELO for qualified teams by 20%, the new playoff odds looked like this:

playoff odds with ELO reduction for clinched teams

Of course, simply buffing Portland’s playoff odds massively increases the accuracy of the prediction, so this might be a bit too reductionist. Furthermore, with some clever configuration of Excel to leverage the solver, the exact handicap percentage could be tweaked to maximize the odds of Portland making to playoffs.2 That being said, let’s take a look at how model quality changes with this change:

prediction quality post adjustment

This is MUCH better. Obviously, the updated model has the benefit of some hindsight here. But a small, targeted change the model was able to increase accuracy from 54.7% to 69.2%. Precision & recall increased by similar margins. I think there is something here that can be applied to future models of NBA outcomes.


Overall, I am satisfied with the outcomes of this process of exploring the model in the context of the metrics above. The key learning for me is that certainty of outcomes does impact the quality of play, at least in the NBA bubble. After accounting for that, we were able to increase model accuracy by more than 25%. To get more accurate, my analysis would need to be more surgical in approach.

My biggest take-away is that I will be designing future models to enable rapid analysis using the metrics here-in. I didn’t do that in this case as I didn’t account for actually doing this analysis. Having appropriate consideration for accuracy testing in the front end would have meant I could have backtested assumptions and model changes across a much broader data set. As a result, I didn’t have an easy way to test my updated assumption of the 20% ELO discount down at the game level. I’m certain that applying better science techniques could result in an even higher accuracy model.

I do find it super interesting that there was a huge miss on the New Orleans Pelicans performance vis-a-vis their ELO rating. This entire process was arguably designed to maximize the odds of the Pelicans (& Zion) to make the playoffs, and in that regard, the NBA’s experiment failed completely. Conversely, one thing that could have been anticipated based on the 20% ELO handicap is that the Phoenix Suns had around a 35% chance to get 7 or 8 wins. Given that, it probably would have made more sense for the NBA to open a mini-tournament at the bottom of the bracket for 7/8/9/10. It would have increased the quality of play and led to a more exciting finish to the end of the regular season. And I think NBA, who certainly has modelers far more sophisticated than I, should have anticipated the drop in play associated with teams who have already clinched.


1I’m using the assessment framework found here on, for accuracy, precision, true positive rate, sensitivity, and F1 score. You can find the definitions within that link – it’s worth the read.

2After writing this, I did some excel tweaking to allow the solver to optimize the handicap for clinched teams. It was 20.00001%. Bizarre.

Charts Reconsidered

Charts Reconsidered: Mask Wearing

Like any good analyst, I enjoy scrolling through r/dataisbeautiful. And when I say enjoy, I really mean “I’m doom-scrolling through reddit because twitter is depressingly worse.” Of course this leads me here, to our first entrant into “Charts Reconsidered”, where every week I will revisit a chart from reddit and suggest some improvements.

That leads me to this chart – the 5th ranked chart on the subreddit on July 21st, 2020. It tells us who does and doesn’t use masks, by a few different breakdowns. It is an interesting story, but it could be told in a better way.

3D bars. Yikes. This reminds me vaguely of “WordArt”.


There are 4 distinct groups in this chart – Gender, Political Party, Education, & Overall. They are all mashed together with no space.

In excel, I would use “blank” series to add space between each group to improve readability while enabling shared axes. I would also pick a consistent series to sort on from high to low.


Green & Grey isn’t a great color combo and gets amplified by a lime green gridline color. It’s not a good look.

Keep the gridlines in background, a lighter gray perhaps. For a chart like this, I would use a light and dark tone of the same color. Or you can steal the Ben Evans approach – and use shades gray + a single color for emphasis (in his case, red).

Labels & Gridlines

Too much info crammed into this part which muddles the story. There are major and minor Y gridlines, which are then labeled without a percent sign. The bars are also labeled. Lastly, the X & Y axis labels are switched.

Turn off minor gridlines and make the major gridlines either 25 or 50. Add Percentage labels so the units are clear. And fix the axis labels (or remove them).

Chart Type

3D bars with series stacked front to back is not a good look. This is most obvious in the GOP group, where the labels overlap the bars. The lack of spacing between groups makes it challenging to see differences between groups as well.

Just use a regular, stacked bar chart.

New visualization

With the magic of PowerBI – I’ve crafted a new chart, with the same data, to tell a more visually appealing & easier to understand the story.

Regular stacked bar chart, grouped and then sorted alphabetically.

The labels are removed, the legend is cleaned up, and the colors are simplified. Did it take longer to make this chart? Yes! Does it tell a better story – also yes!

I hope you found this feedback helpful. Let me know what else you would change in the comments below.


What is Data Duel?

Data Duel is a site built around the idea of exploring what tools are available to analysts for interacting with data. Nate & I come with Tableau & PowerBI experience, respectively, but are technical enough to explore other toolsets, and of course, we both love SQL.

We expect to be posting a new “Duel” roughly once per week. The Duel is based will be comparing and contrasting ways of visualizing a data set. These data sets will be made available for your own remixing as well, within each post.

The constraints of a “Duel” will be mostly based on time – i.e. we will be spending an agreed-upon maximum amount of time on each viz. For the most part, there are no other rules, although bringing in supplemental data should be agreed upon between both parties.

Outside of the “Duel” posts, we will be posting on other topics as we see fit, with Nate focusing on a “breaking into data analytics” series, and myself focusing on “PowerQuery for SQL users”. We will also be sharing our thoughts on release notes of our favorite pieces of viz software as well as remixing other content as we see fit.

I hope you find this fun & useful. Consider it our love letter to analytics.