My data journey started in 2012.

I came across a NYTimes article about how Target was analyzing shopper behavior to determine which of their customers were pregnant. I remember thinking, “how is that even possible?” That’s when I went online and learned about the term “predictive analytics” for the first time.

Now you have to remember it was a different era of technology back then. Only a quarter of Americans owned a smartphone, Instagram was ad-free and Netflix hadn’t started creating original content yet. There were only a handful of programs in the United States that taught analytics.

Luckily, one of them just happened to be located 10 miles from where I lived.

The Right Place at the Right Time

I was a member of the first Master of Science in Analytics cohort at Northwestern University that kicked off in fall 2012. There were 30 of us from a variety of backgrounds, and we all took a gamble with the program since it had never run before.

I knew right away that I was in the right place. My undergraduate degree was in engineering and my work experience was in consulting. Analytics combined my favorite parts of both – working with numbers and coding, along with problem solving and working with people.

A few weeks into our program, the Harvard Business Review came out with an article called “Data Scientist: The Sexiest Job of the 21st Century” and it spread like wildfire. It was the first time the term “data science” was introduced to the general public and suddenly we were in high demand – I had three job offers when I graduated.

I decided to work at Cars.com as their first data scientist. I got the opportunity to immediately apply the concepts I learned to real world data. I worked with the marketing and technology teams on projects ranging from customer churn to user segmentation to creating a recommendation engine.

Since it was the early days of data science, little had been done on a lot of these data sets besides some basic analysis in Excel. With the combination of SQL, R and machine learning algorithms, I was able to discover impactful insights for the first time and people – myself included – were wowed. It was a really exciting time to be in the field.

I often chat with my old classmates about that fateful year of 2012. We had no idea what we were getting into, but we all feel very fortunate to have been a part of the first wave of data scientists.

Going Viral

As a new data scientist, I loved reading data analysis blogs. One of my favorites was Christian Rudder’s OkTrends blog, where he analyzed OkCupid dating data and gave people recommendations like, “if you want people to find you attractive, look away from the camera in your profile photo”.

Feeling inspired, I decided to start my own blog. For my first post, I analyzed the text messages that my husband and I sent to each other over the course of our six-year relationship and called the piece “How Text Messages Change From Dating to Marriage”.

It went viral.

Someone posted my data visualization on Reddit where it got upvoted to the front page. My blog got over half a million views within its first week. My analysis was talked about on morning shows, referenced in a number of articles and videos, and translated into multiple languages.

People seemed to really relate to the analysis, especially women. At the time, a lot of data analysis was focused on sports (like the Moneyball movie) and politics (like the FiveThirtyEight blog that forecasts presidential elections). Many women reached out to me and said “thank you for making data analysis interesting to me!”

So I ended up falling into this niche of creating female-focused content. Or rather – just content that I’m interested in.

I’ve analyzed Bachelor data, used data science to show that cupcakes and muffins are different, made data visualizations about motherhood, and created a natural language processing tutorial centered around Ali Wong’s stand up.

It has all started amazing conversations with women in data and those who are pursuing data roles. My original goal was to entertain, but now I see that it’s also showing that data analysis can be accessible and fun, especially for women.

Today, only about one in five data scientists are women. I’m hoping to do my small part in helping to increase that number. Because just like how algorithms thrive when the data is diverse, the field of data science can only thrive when there is diversity in thought.

From Student to Teacher

Now going back to my day job – while I was working at Cars.com, a lot of people were interested in learning more about data science, so I started this Friday lunch-and-learn series where I taught my coworkers about data science in R. People really enjoyed them and suggested that others outside the company might find the workshops useful as well.

So I decided to start a data science education company on the side. I called up two of my friends from my masters program who had prior teaching experience and we launched our company, Best Fit Analytics. We created our own curriculum from scratch, tested our lectures on our significant others and posted flyers around town.

We ended up teaching an Intro to Data Science in R course a few weekends each year at 1871, the local Chicago startup incubator and leading corporate trainings around the city. It was a lot of fun engaging with members of the up-and-coming data community in Chicago – people were so excited to learn and talk about the latest data tools and techniques.

After a few years of weekend workshops, a company called Metis reached out to me and asked if I would be interested in a full-time role teaching their data science bootcamps. Ever since I was in high school teaching math and science summer camps, I have wanted to be a teacher.

So when I got the offer to be a data science instructor full-time, my answer was a resounding yes!

Metis was a 12-week-long full-time data science bootcamp that helped students transition into a career in data science. We taught adult students from all types of backgrounds. I’d spend half my time lecturing and the other half whiteboarding, debugging code and working 1-on-1 with students to guide them through their data science projects.

It was truly my dream job. I was able to teach a subject that I loved and I worked with students that were smart, curious and eager to learn. I came home feeling fulfilled every day. I would tear up at every graduation – it was amazing to see how much people could accomplish and transform in so little time.

And then the pandemic hit.

Taking a Step Back Professionally

To be honest, I wondered if I should include this section in my data journey, but after talking with some friends, we all agreed it’s important to be open about things like this. I hope some of you find this a helpful read.

The pandemic was a hard time for us all in different ways. For our family, my husband and I were both working from home full-time with a 1 and 3 year old in the house, so it was quite hectic for us. There were not enough hours in the day to work, sleep and manage a household.

At the end of 2020, I decided to quit my full-time job without another job lined up. My first feeling was guilt. I felt like a hypocrite for spending the past few years of my life mentoring women trying to break into tech, telling them that it was possible to have both a career and a family, when I couldn’t do it myself.

A few months into 2021, an old coworker reached out to me out of the blue. He had started a new job and wanted to get some of my thoughts on a data science project he was working on. He asked what I was doing and I blabbered various things like – COVID was hard… I’m focusing on my family… I’m taking a slight pause...

He laughed and said he was just getting back from a two-year career break himself. I was shocked for two reasons – (1) he was an incredibly motivated person and I couldn’t believe he would take time off professionally and (2) he said the term “career break” with no shame at all.

He said his wife worked full-time while he spent time with his two young kids. He made sure to continue to network and take on small projects here and there to keep his skills fresh. He was physically and mentally healthier than he’d ever been in his life. I shouldn’t be feeling bummed, he told me, I was fortunate to have this time and I should be feeling excited.

So I got excited.

During my two-year break, I worked part-time and focused on myself and my family. I wrote a technical book (SQL Pocket Guide, 4th Edition), took on fun data consulting projects (like the one where I worked with a former Bachelor producer!), became friends with other moms in my neighborhood, and took a two-month-long road trip with my husband and kids.

It was so refreshing and at the end of the two years, I felt the same way that my old coworker had felt – both physically and mentally balanced – and ready to start something new.

My Path to Maven

In late 2022, I was freelancing when my old coworker Chris Bruehl reached out to me and said they were looking for a data science instructor at his company, Maven Analytics. I was actually just starting to consider something a bit more steady in the new year, so I decided to check them out.

The more I learned about Maven, the more I wanted to work for the company. They had an expectation of high quality, which is something I have always been committed to. All the team members that I talked to were kind, accomplished and truly believed in their product and mission. They also prioritized work / life balance.

With my background in data science education and content creation, I was the perfect fit for the role. I could not believe my luck. Yet again, I was in the right place at the right time.

When I look back on my data journey, I think about all the small actions I took that led me to the next big thing:

• How I read an article, which led me to going back to school to learn data analytics.

• How I made a lunch-and-learn presentation, which kicked off my career in data science education.

• How I quit my full-time job with nothing lined up, which led me to Maven.

And that’s my data journey in a nutshell.

Even though it’s my job to predict things, I could not have predicted my career path. Every step of the way, I just had to – to quote Frozen – "do the next right thing", which always led me to a more fulfilling opportunity, even if it was years down the line.

I know that for many people reading this, taking a Maven course is probably the next right thing you're doing, which is going to lead to something bigger down the line. I am so excited for you and honored that I'll get the chance to be a part of some of your data journeys.

I cannot wait to share the curriculum and content that I'm creating with the Maven team. While my data journey has been more than a decade in the making, I feel like I am only just getting started.