This article on how to prepare for a data science job search while attending a bootcamp is part of the Coaching Collective series, featuring tips and expertise from Flatiron School Career Coaches. Every Flatiron School graduate receives up to 180 days of 1:1 career coaching with one of our professional coaches. This series is a glimpse of the expertise you can access during career coaching at Flatiron School.
Deciding to change careers to Data Science and enrolling in a bootcamp is a major life step. Whether you are attending full-time or part-time, and continuing to work or not during your studies, it’s a big change to adjust to! So congrats on committing and pat yourself on the back for moving toward the life you want.
But while you’re progressing through your studies, don’t forget to look ahead at what’s next – the job search. Don’t wait until graduation to start strategizing and working towards finding an opportunity! Instead, use the time and resources you have while attending your bootcamp to position yourself for success upon graduation.
Here’s how to prepare for a Data Science job search while still attending a bootcamp.
Reflect On Your Strengths and Interests
In a bootcamp, you’re learning at an accelerated pace. New material is introduced each week, and the relentless pace is kept up for weeks to get you industry-ready in a fraction of the time of traditional educational paths.
As you learn, take notes at the end of each week and reflect on the material and processes you use by asking yourself questions.
Which activities did you enjoy?
What was challenging for you and what was easy for you?
Do you like cleaning data?
Do you like dealing with enormous masses of messy data points?
Do you like performing statistical analyses of data?
Do you like conducting, testing, and maintaining architectures such as databases and large-scale data processing systems?
In addition to your experience with the technical “hard skills” associated with your discipline, think about any “soft skills” you’ve experienced as well.
Do you like creating dashboards for different types of audiences?
How do you like managing projects?
Would you like to be a liaison with other departments or work to understand clients’ needs?
Knowing your answer to these questions will inform what type of role you might enjoy once you’re working as a Data Scientist in the industry and your eventual data science job search.
Consider Your Career Direction
Once you have an idea of what you are good at and what you enjoy, begin considering where these skills and passions could best be used in the workforce.
Talk To Instructors
Those that have walked the path before you are a great resource. Talk to your bootcamp instructors about how the skills and activities that you enjoy can translate to specific jobs. Their knowledge about the field is priceless, and when paired with their knowledge about you as a student, they may have some great recommendations.
Mine The Internet
Search online to find the different types of roles that are out there, such as Data Analyst, Data Scientist, Data Engineer, Business Analyst, Machine Learning Engineer, and more. While they may all deal with data, their day-to-day responsibilities can vary greatly.
For example, data analysts collect, process, and perform statistical analyses of data; while data scientists focus more on taking enormous masses of messy data points and using their skills in math, statistics, and programming to clean, massage, organize and model the data. While Data Engineers in many cases will develop, construct, test, and maintain architectures such as databases and large-scale data processing systems, data scientists pull relevant data sets from those databases for analysis.
Read job descriptions, determine which role’s daily activities sound the most interesting, and make a short list of job titles that you’d like to pursue.
Identify Skill Gaps
Once you’ve got an idea of which data-related jobs you might enjoy, it’s time to focus in on aligning your skill set. Look at job descriptions to see what skills are often required for these roles. That way, you can identify what you are strong at already and which you may need to develop further. Once you know what skills you would like to add to your toolbox, consider how you will obtain these skills, make a plan, and take action steps to obtain these skills.
Build A Targeted Portfolio
In your time at Flatiron School, you’ll have the opportunity to develop data-related projects based on a topic you pick yourself. But, before choosing a topic based on what others have done in the past, consider your interests and how you could highlight industries that interest you. Padding your portfolio with relevant projects can go a long way when interviewing potential employers.
For example, let’s say that Samantha has a strong interest in making sure that all people have access to good healthcare and she would like to work in the healthcare industry after graduation. Samantha could complete a data project on the comparison of accessibility and lack of accessibility in the healthcare system and how these data points reflect an overall average age of death. Once she starts interviewing, Samantha’s project will demonstrate her data skills and her passion for the industry.
Showcase the projects you create in your resume, cover letter, and in the “Featured” section of your Linkedin profile, and be prepared to discuss these projects and the steps taken to complete them during an interview.
Conclusion
There is always a ton of information to learn during a bootcamp. But, by being reflective throughout the process and taking action to set yourself up for success, once you graduate you’ll be better prepared for your job search. After all, knowing what type of role you want is half the battle.
About Laura Nicolaisen
Laura Nicolaisen is a Career Coach with Flatiron School. She has 15-plus years of experience as a career coach collaborating with recent graduates, professionals, and executives. In addition, Laura has over 15 years of experience working in the university and bootcamp setting, in such areas as admissions, student advising, coaching, and as an executive team member.
For millions of Americans, the third month of the year means only one thing – March Madness. With more than 70 million official brackets created annually, the likelihood of your bracket being correct is slim.
Here’s Data Science Curriculum Developer Brendan Purdy on how he uses Machine Learning to make predictions and beat the odds.
The Tournament
First things first, we’ll do a short overview of what the March Madness tournament is for those not familiar with all things Basketball, or who just need a refresher.
The NCAA Division I men’s basketball tournament is a single-elimination tournament of 68 teams that compete in seven rounds for the national championship. The playoff tournament is known as “March Madness” due to there being 67 games in the single-elimination tournament within three weeks, many of which overlap and are played simultaneously in the beginning. The winner of the tournament is crowned the NCAA Basketball Champion for the season.
Creating Brackets
Individuals pick who they think will win each game. Millions of fans submit their brackets for online competitions that have payoffs. Over 40 million people fill in brackets, many people more than one, which results in over 70 million officially submitted brackets. The actual number of brackets created each year is likely much higher, considering that many will simply make their own and never share or submit publicly.
To complicate matters, there is a “Round 0” of the tournament called the “First Four” where 8 teams play for four spots in Round 1. Round 1 has 64 teams.
The process of creating a bracket is to select the team you think will win each match-up. As the tournament proceeds, your choices narrow down until the championship game.
This article will cover the men’s tournament, however, two March Madness tournaments run simultaneously – a men’s and a women’s. Everything that’s covered in this piece data-science-wise can be applied to both.
By the numbers:
9.2 quintillion – With 63 games from Round 0 on, there are 2^63 = 9,223,372,036,854,775,808 possible outcomes for a bracket
49 – The longest (verifiable) streak of an NCAA men’s bracket is 49 games, where the person predicted all of the teams who got into the Sweet 16 in 2022
$3.1 billion – About 45 million Americans wagered a total of 3.1 billion dollars on March Madness games in 2022
With billions of dollars wagered each year on statistically slim odds of winning, why not use Data Science to make better predictions?
Data Science
The goal of Data Science is to discover actionable insights hidden in large data sets by creating models using the tools of mathematics and statistics and machine learning, along with germane subject matter expertise.
Let’s break those ingredients down a bit more to see how they all work together to make predictions.
Mathematics and statistics – Math and stats give the foundations of machine learning algorithms and inform us how we can interpret the models.
Machine learning – Machine learning is artificial intelligence algorithms that learn from data.
SME – The data is always about a specific thing, and this is where the subject matter expertise (SME) is germane. Having SME aids you in accurately interpreting data to produce relevant insights.
Now, what skills will you need to create a powerful predictive model to beat the March Madness bracket odds? The main fundamentals the typical data scientist would use to create a model like this one are:
Math and stats – Calculus, Linear Algebra, Statistics, Probability
Programming/software skills – Python, Tableau, SQL, R/R Studio
Other skills – SME, Communicating and storytelling with data, Data ethics, and privacy
If you don’t have all of those skills – worry not! You can still read about how the modeling works using machine learning below, courtesy of Flation School Data Science Curriculum Developer, Brendan Purdy. Or if you want to try your hand at Data Science, jump into our Free Data Science Prep Work!
Machine Learning
Next, we’ll learn a bit about Machine Learning – how it works, its goals, and common examples.
Machine Learning is often defined as “a subfield of Artificial Intelligence where the algorithms learn from data.” That’s the key – that the algorithm learns from data, instead of just delivering preset and easily-definable outputs as a calculator does. For example, programmers initially programmed AI to play games like checkers and chess and inputted all the rules so that if an opponent did an action, the program would perform an expected response action. However, with modern AI, the system has enough computational power to learn from the data and perform new, nonprogrammed actions, making it machine learning.
Types of Machine Learning
There are two main types of machine learning – supervised and unsupervised.
In supervised machine learning, algorithms are trained using labeled data, e.g. linear models, nearest neighbors, decision trees, and gradient-boosting decision trees. The programmer tells the AI what the data means, and then asks the AI to look at similar data and determine what it is. For example, define an image as a picture of a cat, then show another image and ask the AI if the image is of a cat or not.
In unsupervised machine learning, algorithms are used against data that is not labeled, e.g. Gaussian mixture models, and clustering algorithms. For example, give the AI pictures of various animals, and ask how the machine would group them.
Common Goals of Machine Learning
Three Common Goals of Machine Learning
Classification – a type of supervised learning. In this type of program, you give the program attributes to group items based on.
Regression/Prediction – a type of supervised learning. This is the type of model that we will use to create our March Madness Bracket. The most basic example of this is our linear regression model, with a line that represents trends in the data.
Clustering – a type of unsupervised learning. In this case, the machine puts data into clusters. This model type is used to make inferences from demographic data for companies. For example, if you are part of a rewards program, these types of models gather data based on your purchases, then use clustering machine learning to attribute you to a certain demographic and inform targeted advertising campaigns.
Machine Learning Usage In Industry
Whether you realize it or not, machine learning permeates much of your daily life. According to industry surveys, 67% of companies were using machine learning in 2020. 97% planned to use it in 2021.
Some examples of machine learning that you likely interface with, or interfaces with you and your data, almost every day:
Recommendation systems (Netflix, Amazon)
Neural Networks (sports analytics)
CNN (medical image analysis)
NLP (Translations, Chatbots)
Clustering models (demographics of customers)
The applications for machine learning are numerous and growing each year. Knowing how to wield such a powerful tool can set you up for success in far more than just your friend’s March Madness bracket.
Extreme Gradient Boosting
Now that we know a bit more about machine learning, which algorithm are we going to use for our bracket predictions? For making a March Madness prediction, we will be using a decision tree.
Decision Trees are a supervised learning method. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data. They can be used for classification or prediction.
A decision tree takes in data and determines the probability of the most likely outcome.
For the specific decision tree type, we will be using a Gradient Boosting Decision Tree (GBDT). A GBDT uses boosting to create many decision trees iteratively. It uses weak learning to calculate the gradient to minimize the error for each iteration of the algorithm. The model makes the prediction based on a weighted aggregation of each input. The algorithm also calculates a gradient to minimize errors for each iteration.
Gradient Boosting Decision Tree
How Boosting Decision Trees Work
Some facts about Boosting Decision Trees to further explain how the model works:
Ensemble Method – In machine learning, an ensemble method combines more than one technique. Boosting is a type of ensemble learning that uses many decision trees. Ensembles often work better than a singular model as they’re able to exploit multiple algorithms at the same time.
Weak Learner – A weak learner is a model that only does slightly better than random chance. This results in a simple decision tree.
Iterative – One tree is created and then the algorithm determines what the weak learner got wrong. It then builds another weak learner that focuses on what the previous tree got wrong. This process continues until a stopping condition has been met.
Aggregate Predictions – Boosting algorithms make the final prediction based on a weighted aggregation of each input. The inputs that were harder for the weak learners to learn are weighted higher than those that were easy to learn.
Minimizing Errors
As mentioned above, gradient boosting is used to minimize the errors in each iteration created. Here’s how the model does this:
Residual Fitting – A residual is how far the actual value is from the predicted value. GBDT uses the residuals with a loss function to see how well, or poorly, it is doing.
Loss Function – A loss function uses the residuals of the model to calculate the overall loss. (The loss function must be differentiable.)
Gradient Descent – The gradient is a concept from calculus that is used in a myriad of machine learning models. The key idea of the gradient descent is that it uses the second derivative of the loss function to determine how to minimize loss, i.e. error. This is what GBDT is using at each iteration of its algorithm.
All three of these concepts tie together. The residual fitting is the input into the function. The loss function is the function itself, and the gradient descent is the second derivative of the function.
XGBoost & Bracket Prediction
So how do we implement this Gradient Boosting Decision Tree to make March Madness predictions? By using XG Boost.
Data Set
Everything starts with acquiring data. The NCAA and Kaggle release several data sets that contain for each team: historical seeding, performance (both season and postseason), box scores, geography, coaches, etc. Five thirty eight and other websites also have a ranking of the teams.
Preprocessing/ Feature Engineering
This is often the most challenging aspect of machine learning: bringing disparate data sets together, getting the variables in the proper form so that we can use the algorithm, etc.
Preprocessing is prepping the data, and feature engineering is focusing on certain variables. Once this is complete, you move on to XG boost.
XG Boost
XGBoost is the most commonly used library for GBDT and is available in a large number of languages, including Python. Part of where the science of data science comes in is at this point when the data scientist needs to “tune” the model. If two data scientists used the same data sets and did the same preprocessing and feature engineering, modifying the model’s parameters (aka “tuning” it) differently will result in different outputs.
Predictions
In a single-elimination tournament with 68 teams, we predict (68*67)/2 = 2,278 matchups and get the probability of who is more likely to win each possible match-up.
Sample Output
The predictions represent the probability that the team with the lower id will beat the team with the higher id, e.g. 1101 has a 2.7% chance of beating 1104.
Measuring Success
So once you have your bracket, how do you measure success? We’ll consider two ways how well we did.
Bracket Challenges
For sites like CBS Sports, ESPN, etc., typically a weighted value is given for each game where you have a correct pick, e.g. 1st round is 1 pt, 2nd is 2 pts, Sweet 16 is 4 pts, Elite 8 is 8 pts, Final 4 is 16 points, Championship is 32 pts. The highest score at the end is considered the “winner.”
Machine Learning Competitions
For ML competitions like Kaggle where the goal is to use ML modeling, then a metric like log-loss is used. Log-loss is indicative of how close the prediction probability is to the corresponding actual value (win or loss). In particular, this provides extreme penalties for being both confident and wrong.
Brendan Purdy’s Bracket Predictions
Here’s Brendan’s March Madness bracket, based on his Machine Learning model (though he notes, as a long-time UCLA fan, that he does not approve of Houston beating UCLA, no matter what the algorithm data says.)
Come back in mid-April to see how he did, with a follow-up piece explaining model predictions vs. real-life results.
Eric Au, an August 2022 Data Science graduate from Flatiron School, combined his love of sports and machine learning to create his capstone project.
In his project “Stepping Up To The Plate”, Eric used machine learning to predict MLB player salaries and team wins. Watch his full presentation below:
Stepping up to the Plate – Major League Baseball and Machine Learning by Eric Au
If you’re a sports fan or a fan of the movie Moneyball, you know that an issue teams face is how they spend and allocate money when it comes to building a team.
The core subject in Moneyball was how smaller market teams such as the Oakland Athletics can compete with larger market teams like New York and Boston who can spend much more money. For my capstone project, I wanted to explore that and try to predict the MLB player salary of pitchers and batters using historical baseball data. Secondly, I wanted to better understand what statistics contributed the most to winning when it comes down to predicting team wins.
Data Set
The data set for this project consisted of historical baseball statistics and advanced statistics.
In 2014 Major League Baseball introduced Stat Cast which allowed teams to collect more baseball data than ever before. This included detailed statistics such as how hard the ball was hit, how many revolutions per minute the ball spun, and many others.
The key takeaway here is that advanced statistics have far more features or variables to work with.
Taking a look at the data set that I worked with, we see some of the highest-paid players in baseball. This gives you a good perspective of some of the top Echelon of stars in the game as of 2021, as some of the top players are making in the tens of millions of dollars. I considered most of these players as outliers.
However, since 2000 I noticed that the average batter and pitcher make far less than the outlier group. Batters are more recently making about $5 million on average compared to the pitcher making about a million less.
As I mentioned Major League Baseball has incorporated data analytics more. When taking a look at team salaries and wins during the 2021 season we can discern some noticeable observations. We especially see data analytics used heavily for teams like the Tampa Bay Rays who are on the far right with 100 wins and the Milwaukee Brewers directly adjacent to that with 95 wins.
Ultimately these are two example teams that have a smaller relative payroll than some of the bigger M\market teams like the LA Dodgers or the New York Yankees.
Predictive Results
In terms of results for my predictive model, I achieved a margin of error of under $2 million. For Advanced Data the margin of error was about $2.8 million and $2.4 million for batters and pitchers respectively. The reason for the different margins of error is due to the different sizes of the data sets. When predicting team wins per season this was a fairly simple linear regression model where I was able to achieve a margin of error of one win using Advanced team data. This indicates that there’s a strong relationship between features and wins.
Model Web Application
I also want to show this application that I made. I used streamlit to develop a pair of locally run applications. They take in user input and provide a salary prediction for pitchers and batters.
For example, this first input is $750,000, which is the average salary difference across a player’s career. This gives you a little of how that was feature engineered. Ultimately, you can shift around some of these values for batters. You can do whatever you want, you can make whatever player you’d like to make for this previous season. Then, hit submit and it gives you a predicted player salary of $3.8 million.
Hopefully, you can afford that if you’re building your team.
Comparing Model To Season Statistics
Another thing I looked at while working on this project was how it compares to this season’s statistics and how much money players this year might be making as of August 24th, which is when I loaded this data set. If you’re familiar with the game of baseball, one player that’s doing exceptionally well this year is Aaron Judge. He plays for the New York Yankees. He’s recently made $90 million this year; my model is predicting he makes $21 million as of August 24th. One could argue that’s still underpaid. But based on the season statistics alone you’ll know if a player is overvalued or undervalued.
Technology Used
In terms of the technologies that I was using, the main language was Python. It Incorporated the scikit-learn library to apply those machine learning techniques for this project. Visualizations were developed using Tableau Software and the web application was deployed through streamlit. All the data was sourced using the pybaseball library and FanGraphs.
Notable Challenges
There were a few notable challenges I encountered when working on this project. One was narrowing down the many features to the most important features that gave me the best predictions. As I discussed, there were many features to work with. But, simpler model models are generally preferred since they are easier to interpret and understand. This is where domain knowledge about baseball especially helped in identifying those important features. Additionally, reducing the margin of error for predictions was especially difficult. This was because there are those Superstar players who are making well above the average salary. There are other factors that are not necessarily explained in baseball statistics alone that can account for a player’s salary such as basic economic demand for a player in a particular off-season.
Want To Try Your Hand At Machine Learning?
Eric Au was a civil engineer that enrolled in Flatiron School’s Data Science course to change careers. He created this project as his capstone project, using all of the skills he’d learned during the program.
Think that sounds interesting? Try your hand at Data Science with our Free Prep Work and start learning how to make a machine learning project just like Eric’s today.
Zach Zazueta, a 2020 Data Science graduate from Flatiron School, spent half a decade in the finance department of educational institutions before an interest in data-driven problem-solving led him down a different path.
He details his path from finance to data science below.
A Foundation In Finance
When Zach Zazueta graduated from college in 2015, he knew that he wanted to combine his interest in math and economics with his degree in Political Science to work for a mission-driven organization. The mission he chose? Addressing unequal access to quality public education for inner-city minority students coming from low-income homes.
“I was on a finance team in the education field working for a network of charter schools supporting 50 elementary, middle, and high schools in the NYC metro area,” Zach explained when we interviewed him in early 2023.
A few years in, he found himself working with data in Talent Analytics designing evaluation systems. It was during these exercises that Zach’s interest in data took hold and he began to consider a different career path.
“Eventually, the interest and enjoyment I was getting from the problem-solving outweighed the satisfaction I got from the organization’s mission,” he said. “It was time to enter a new environment with new business challenges that would push my learning.”
Pivoting To Data Science
Once Zach made the decision to switch from his current role, the decision to pursue Data Science seemed like an obvious one.
“I always had a draw toward numbers-focused work. In my [early career] I found designing logic behind Excel formulas compelling. Mapping out data to tell a story and bring clarity was rewarding,” he recalled. “And after working with SQL and Tableau [designing evaluation systems], I knew they were areas I wanted to grow in.”
After dabbling in open-source materials and learning on his own for a time, Zach ultimately decided to apply to Flatiron School’s Data Science program to accelerate his learning.
“I was having difficulty making sustained, targeted progress in my learning. I saw Flatiron School as a unique opportunity to boost the nascent skills I had already developed and learn how to code quickly,” he explained. “And a bootcamp was a faster and less expensive avenue than a traditional master’s degree program.”
His Bootcamp Experience
Zach enrolled in Flatiron School’s online part-time Data Science program in 2019. Like many other students choosing to pursue a career change while maintaining their current employment, he initially found the added time requirements difficult to adjust to.
“While I appreciated the flexibility that this option allowed me to have as I was able to continue working and earning income while enrolled, it was a big time commitment to tack onto regular life.”
But, throughout the course, he developed skills that proved invaluable once he entered the job market.
“The module wrap-up projects were quite helpful as a practice to become an authority on a data project I owned,” he explained. “I’ve often had to present and explain findings to non-technical stakeholders [in my career], laying out the ‘so what?’ business impact of my analysis. It was also helpful to take a project from raw data to visualized findings – end-to-end projects can be the most rewarding.”
Working In Tech
Zach graduated from Flatiron School in 2020. Since then, he has been enjoying working in Data Science, mentioning that he uses the skills he learned during his bootcamp almost every day.
“[Working as a Data Scientist] absolutely lives up to the dream,” he said. “I am applying the skills I learned in Flatiron on a daily basis. 90%+ of what I do in my everyday job is coding, writing queries, and making data tell a story.”
The data that first piqued his interest and the data he uses now differ greatly in size and scope, a change that ties back to his early fascination with economics.
“Shifting into tech has afforded me the opportunity to work with truly big data. Working with data tables that are petabytes in size has been a vastly different experience than my time in non-profits,” he said. “I also now work for a global company instead of focusing on just one city; seeing how global markets impact the data has been a really exciting change.”
Reflecting On His Journey
Looking back at the beginning of his career change, Zach’s main takeaway was the necessity of a growth mindset.
“The data community appreciates the learning journey. No one will expect you to know everything at once. They just want to see that you are equally passionate about solving the same types of problems they are,” he explained. “Because of that mindset, it is a terrifically collaborative space that allows learning to flourish.”
His advice for current students is to lean into the discomfort of that growth mindset and embrace the process.
“Try and hold tight to the fact that this is a career change – and careers are measured in years, if not decades. If the first few years start slow, that’s okay; the growth becomes exponential once you have a foothold.”
Ready For A Change, Just Like Zach Zazueta?
Apply Now to join other career changers like Zach in a program that sets you apart from the competition.
Not ready to apply? Try out our FreeData Science Prep Work and test-run the material we teach in the course. Or, review the Data Science Course Syllabus that will set you up for success and help launch your new career.
As of 2022, women make up only 28% of the tech industry workforce. For technical roles, that number is even lower. There are simply not enough women in tech.
That’s why Flatiron School offers the Women Take Tech scholarship to begin closing the opportunity gap for women in tech. With this scholarship, we aim to do our part and start to help make tech equal for all.
In celebration of Women’s History Month, here are the stories of four recent female Flatiron School grads making waves in the tech industry.
Victoria LeBel: Registered Nurse to Software Engineer
Victoria LeBel began her career as a registered nurse. She spent 4 years working on a high-risk labor and delivery unit but felt that she needed to make a change.
“I was missing an element of creativity in my work,” she explained. “[But] I wanted to continue to use my critical thinking and problem-solving skills.”
Combining her acquired skills and her love of continuous learning, she determined that Software Engineering would be a great fit. To make the transition from healthcare to tech though, Victoria knew that she would need to pursue some additional schooling. It was then that she learned about Flatiron School.
Victoria enrolled in Flatiron School’s full-time Software Engineering program and graduated in September 2022. After a short job search, she accepted a Software Engineer position at Econify.
“If you set your mind and efforts toward something you can accomplish anything. So long as you have the focus and determination, you can achieve anything, no matter where you started.”
Jenny Kreiger began her career pursuing a Ph.D. in classical art and archaeology with the hopes of working in higher education or museums. But, as she helped excavate the ruins of Pompeii for the first summer in a row – a dream archaeological opportunity – she knew she was drifting away from studying human behavior.
“The academic job market is notoriously challenging, so from the start of my doctorate, I was always researching and preparing for alternatives. Data Science was a possibility for me because as an archaeologist I liked using data to learn about human behavior.”
After trying out some online tutorials, she decided to quit her job and enroll in Flatiron School’s Data Science course.
She graduated in early 2020 and had the unfortunate circumstance of job searching during the beginning of the COVID-19 pandemic, but ultimately accepted a role as a Data Scientist at Shopify.
“Lots of organizations need your expertise right now, and you might be able to find a great fit in an unexpected place, so don’t give up–adapt!”
Carla Stickler: From Broadway Star To Software Engineer
By the end of 2018, Carla Stickler already had what many would consider to be a dream career. She’d found success in the arts – a difficult feat no matter the medium – and performed on Broadway stages in world-famous musicals such as Wicked, Mamma Mia!, and The Sound of Music.
But, Carla recalls knowing that she needed to make a change for a while, saying that the continuous grind and needed to reach that level of success had begun wearing on her.
Finally, a chance encounter at her 35th birthday party spurred her to act.
“A friend showed up to my party and announced, ‘I’m a software engineer now and I just got a great job making more money than I’ve ever made with health insurance and a 401k!’ I was confused, since last I checked, he was a composer writing musicals,” she mused. “I held him captive for the next 30 minutes asking him how he did it and what exactly software engineering was. He told me he went to the Flatiron School and learned to code.”
Carla graduated from Flatiron School’s Software Engineering program in the Fall of 2019 and accepted a position as a Junior Software Engineer at G2.
“I cannot begin to tell you the number of things I’ve learned in the past year and the amount of confidence I’ve gained as a developer. I love my job and couldn’t be more grateful for the life that attending Flatiron and learning to code has provided for me.”
Wendolyne Barrios: Food Industry to Freelance Designer
Wendolyne Barrios spent the first 10 years of her career in the food service industry. She began helping in her family’s business, then pursued her own career in the field. But a decade in, Wendolyne knew she needed a change.
“Working in the food service industry is tough on the mind and body,” she said. “The field took more from me than I got back, so I knew I had to make a change if I wanted to live a healthy, enjoyable, and sustainable life.”
Fueled on by a lifelong love of the arts and her desire to live the life she’d imagined, Wendolyne applied and was accepted to Flatiron School’s accelerated 15-week UX / UI Product Design program.
Wendolyne graduated from Flatiron School in August of 2022 and began a career as a freelance product designer. In January 2023, she founded wendolyne.design, which specializes in brand design, web design, and mobile app design.
“I pushed myself harder than I thought I could. I pushed myself mentally and emotionally to come out of the other side of it and feel like I was finally going somewhere. It was worth it, for me to feel the way I do now.”
Studies show that companies with a diverse workforce are more innovative, creative, and productive, and earn more revenue.
But, with 39% of women in tech saying that they see gender bias as an obstacle to getting a promotion, it is not enough to simply hire more women. There needs to be an industry-wide shift towards working environments that embrace and promote diversity. That starts with creating more opportunities for women.
Flatiron School’s Women Take Tech scholarship does just that, granting up to $1,000 to eligible female students to get started toward a career in tech.
This article about Flatiron School graduate Doug Lu is part of the Coaching Collective series. The seriesfeatures tips and expertise from Flatiron School Career Coaches. Every Flatiron School graduate is eligible to receive up to 180 days of 1:1 career coaching with one of our professional coaches. This series is a glimpse of the expertise you can access during career coaching at Flatiron School.
Doug Lu, a September 2021 Data Science graduate from Flatiron School, began his job search with the goal of working at one specific company. But, over the course of his search, he found out why it’s important to always keep your options open.
Single-Minded Focus Leads To A Stalled Job Search
Doug Lu began his career working in finance and wealth management.
When he enrolled in Flatiron School’s Data Science program, he knew he could succeed with the technical skills he already had in Python, SQL, and Visualizations. And, upon his graduation 15 weeks later, Doug was confident that he would land a good job at the company of his choice.
When Doug graduated, he had one particular company that he wanted to work at in the Financial Tech (FinTech) industry. He constantly checked the company’s web page for open roles and applied to very few openings at different companies.
In an attempt to appeal as a strong candidate, he meticulously spent a lot of time reading the company’s blog to try to come up with a perfect project to showcase to the company. Fast forward a few months after graduation, and Doug had not had any interviews yet and despite many great project ideas, he had executed on putting together a demo to showcase.
After months of not making any progress and feeling like he would never land a position at the company of his dreams, Doug hit a low point in his search.
Learning To Keep His Options Open
Frustrated after months of single-minded persistence, Doug needed a mindset shift. We evaluated his job search thus far and recommended that he consider other companies in the FinTech industry, not just his dream company.
With a reframed mindset and renewed motivation, Doug shifted to focus on the type of role he wanted to obtain, not just the company. He started networking with professionals already working in the industry, sharing that chatting with other Data Analyst and Data Science professionals informed what it was like to work in the industry, helped him to optimize his search better, and increased his chances of landing an interview and passing the technical round.
“You can leverage past experience, know the role you want and chase down the role you want.”
Doug also shared that when he came out of the bootcamp he felt overwhelmed, thinking he had to try to show off his technical skills. It was only when he started networking with other Data Scientists that he realized “the problems that start-ups and tech companies aim to solve revolve around creating structured insights to solve actual business problems, versus just showing technical expertise.”
Networking with various tech professionals ended up being key to Doug landing a job in the industry as each conversation helped him understand the fundamental challenges that FinTechs faced, with each conversation compounding his knowledge.
While attending a FinTech virtual conference, he speed networked and met FinTech professionals. Finally, after 5 months of searching, and within 10 days after the FinTech conference, Doug obtained two offers and stood out from other candidates by having a deep understanding of how he could provide value to immediately solve business problems, which he learned from all the conversations he’s had with many FinTech professionals.
Reflecting On His Job Search Experience
Doug Lu ultimately accepted a role as Data Analyst at Self Financial. Looking back, he stressed the importance of networking with a purpose.
“Time chatting with people uses up a lot of energy,” he explained. “It’s an investment of your time and at the end of the day, it only works when you have a good [process] as not every conversation will be helpful.”
His advice for other job seekers is the lesson he had to learn for himself.
“Don’t be so tied to one company. A lot of factors are out of your hands, so it’s better to focus on competitors and not put all your eggs in one basket. The dream company may come later after you have some experience in the industry.”
About Laura Nicolaisen
Laura Nicolaisen is a Career Coach with Flatiron School. She has 15-plus years of experience as a career coach collaborating with recent graduates, professionals, and executives. In addition, Laura has over 15 years of experience working in the university and bootcamp setting, in such areas as admissions, student advising, coaching, and as an executive team member.
For people of color, a tech career can often feel out of reach. A lack of representation can make them feel like there is no place for them in the industry.
The five POC graduates from Flation School featured in this blog prove that this is not the case. Their stories are those of determination and resilience, of overcoming naysayers and self-doubts to go after the life they wanted.
Their stories prove that POC belong in tech.
Micah and Colin: Oil Fields To Software Engineers
Twin brothers Colin Mosley and Micah Mosley began their careers as Petroleum Engineers. Citing long working hours and a bad cultural fit, they knew they needed to make a change.
When they were laid off like many others at the onset of the pandemic in 2020, Micah and Colin decided to transition into tech. After weighing their education options – self-taught, university, or bootcamp – they committed to Flatiron School to accelerate their path into the industry.
“Flatiron School does a good job of giving you a cohort and resources that make it easy for you to learn as much as you are willing to learn, and there is plenty to learn if [you are] willing to put in the time.”
After graduating from Flatiron School’s Software Engineering program, Micah and Colin landed twin Software Engineering roles at CitiBank.
Chuck Pryor, Jr. had a long and varied career before joining Flatiron School. He’d been an actor, teacher, writer, mid-level manager, outreach counselor, landlord, and a full-time caregiver for his ill parents. After all of his previous experiences and career paths, he felt a pull toward tech.
While evaluating his options to break into the tech industry, he ultimately selected Flatiron School’s Data Science program. He cites the program’s reputation, cost-effectiveness when compared to a traditional university, and Flatiron School’s career services that work with learners to get their first job post-graduation.
“Had I tried to do this program on my own time without the structure of an on-campus program, I would have failed miserably and not completed the program. Every project applied what I learned to real-world problems that ended up impressing my interviewers.”
Chuck credits his previous careers for building the networking skills that ultimately landed him his first job in tech as a Data Engineer. His advice for others considering a career in tech are simple and concise – Go For It!
Over the summer of 5th grade, a young Deka Ambia fell in love with coding. But after being told not to pursue it, years later she was working as a TSA Agent. It was during a government shutdown that Deka used the limbo state to pick up coding again and pursue her original dream of working in tech.
Deka attended Flatiron School’s Software Engineering course and graduated in 15 weeks while working full-time. She distills her determination in building a new life for herself into a single word: freedom.
“The freedom to be able to work wherever I want, whether that is at an office, at home, or on the beach somewhere. The freedom to look however I want. The freedom that a skillset can be used in almost any industry is filled with endless opportunity.”
After graduating from Flatiron School, Deka landed a Software Engineer position at PopMenu. As for her advice for others beginning the program, her advice is to “take it extremely seriously,” because “it has a real possibility of changing [your] life and mindset forever.”
Read about her journey into tech here: Deka Ambia.
Fredrick Williams: Sales To UX / UI Product Design
Frederick Williams spent more than 20 years in sales and marketing before deciding that he needed to make a change. He’d worked with Product Designers over his career and was interested in the research aspect of the role, but worried that he’d “aged out” of tech at 40 years old.
Despite his doubts, he enrolled in a Flatiron School course for UX/UI design. While he entered the program with an open mind and a strong desire to learn, Williams found that his background and personality made UX design a surprisingly good fit.
“I fell in love with UX and I found that UX is for everyone, no matter their age, and the community is incredibly supportive.”
After graduating from Flatiron School, Frederick noted that his job search was a smooth process, with hiring managers immediately interested in him. He ultimately accepted a position as a Senior Analyst US Designer at Avanade.
As for advice for those considering a career transition, Williams was quick to point to the power of perspective.
“It’s not an age thing, it’s a mindset thing,” he said. “If you can dream it, you can make it happen. You have to put it out there, you can’t operate out of fear. I’m black, I’m queer, and I got a job in tech at 43.”
Learning Python can open the door to many career opportunities in tech. If you’re wondering which jobs you can get knowing Python, the list may surprise you.
Python is one of the most popular languages for those interested in pursuing a career in software development. With its versatility and ease in creating a variety of applications, it is a key skill to have in your developer toolkit.
For those interested in a career in software development, Python is often a great choice for their first language. The language was designed to use plain English for ease of understanding and supported by an active community. In addition to the almost limitless number of available free resources, tutorials, and accelerated learning courses, Python is easy to learn and use.
Now that you know how easy it can be to learn, here are our top 7 jobs you can get knowing Python:
Python Developer
Python developers are responsible for the coding, designing, deploying, and debugging of development projects, typically on the server side (or back end).
They specialize in Python and its frameworks such as Flask or Django for web development, TensorFlow and NLTK, PySpark for machine learning, and Pandas, NumPy, and SciPy for data science.
How do they use Python?
From building websites and applications to running deep learning algorithms to analyzing data, Python Developers leverage the versatility of Python to solve problems and answer questions. They use Python to crunch data, develop web application back ends, and automate scripts.
Full Stack Developers use their knowledge of both front end and back end programming languages to design, develop, and maintain full-fledged and functioning platforms with databases and servers.
How do they use Python?
For Full Stack Developers, Python is primarily used as a back end language to manage servers and databases. Full Stack Developers typically leverage frameworks like Flask or Django with Python to make it easier to build out fully functional applications by taking development in the front end and combining it with the back end.
Data Scientists and Data Analysts are big data wranglers, gathering and analyzing large sets of structured and unstructured data. These roles combine computer science, statistics, and mathematics. They analyze, process, and model data and then interpret the results to create actionable, data-driven plans for companies and other organizations.
How do they use Python?
Data Scientists and Data Analysts mainly use Python and its frameworks to create predictive models, use machine learning techniques to improve data quality and find patterns and trends to uncover insights. They also create algorithms and data models to forecast outcomes.
A Data Engineer is an IT worker whose primary job is to prepare data for analytical or operational uses. These engineers are typically responsible for building data pipelines to bring together information from different source systems.
How do they use Python?
Data Engineers use Python to create Data Pipelines, set up Statistical Models, and perform thorough analyses.
Python packages used in Data Engineering often include:
Pandas – used in data aggregation and data cleaning
NumPy – used in data analysis
(Py) Spark – used to handle big data and leverages Spark ML for machine learning
TensorFlow – used in AI training and inference of deep neural networks
Natural Language Toolkit (NLTK) – used to make natural human language usable by computer programs
Product Managers are responsible for identifying customer needs and maintaining the business objectives that a product or feature should fulfill.
How do they use Python?
Data plays a crucial role in the work that Product Managers do. They use Python to research new features and products and make the case as to why certain features or products should be built and implemented into an existing product.
Being able to automate reports and analysis makes Product Managers less dependent on the Data Science team and refine processes to leverage data-driven insights to solve problems.
Performance Marketers are responsible for managing digital accounts such as Google Ads and Facebook Ads to get the right channel mixes and drive key performance indicators for marketing.
How do they use Python?
Performance Marketers use Python for data reporting automation and analysis. These are leveraged to obtain the latest information about trends and markets when making decisions within accounts.
Interested in one of these career paths, but lacking the Python skill to land a job? Get industry-ready in as little as 15 weeks with an accelerated Flatiron School Software Engineering program.
This article about Flatiron School graduate Brian Tracy is part of the Coaching Collective series. The seriesfeatures tips and expertise from Flatiron School Career Coaches. Every Flatiron School graduate is eligible to receive up to 180 days of 1:1 career coaching with one of our professional coaches. This series is a glimpse of the expertise you can access during career coaching at Flatiron School.
Brian Tracy, a Flatiron School Data Science graduate, came into the job search sharing that every interview in his life so far resulted in him “getting the job.”
While impressive, this resulted in Brian learning the hard way not to over-commit to a single opportunity.
Brian’s Job Search Mistake
When Brian Tracy graduated from Flatiron School and began his job search, he was determined to join a company that dealt with sustainability. He immediately got to work applying to job board listings and reaching out to connections.
One of those connections, a fellow Flation School graduate, worked at a sustainability company and encouraged Brian to apply for an open role.
Brian ended up landing an interview and over the next 4 weeks progressed to the final round. Throughout the interviewing process at this company, however, Brian had not been applying to as many other roles. Instead, he focused only on the role he was interviewing for.
As he’d mentioned at the beginning of his job search, he’d gotten every job he interviewed for, so why wouldn’t he get this one too?
This was a mistake.
In the end, another candidate was selected for the role. After a month-long interview process, Brian had to start at square one with applications. “I was mad at myself that I did not continue to apply to jobs,” Brian said. “I was frustrated that they just had a better candidate and felt like there was nothing else I could do.”
Getting rejected from the opportunity he’d been all in on, Brian shared, was his lowest point during the job search process.
How Brian Bounced Back
After the disappointing rejection, Brian took some time to reflect on his strategy and next steps. Then he decided to put his new data skills to work.
Brian determined that out of all the applications he submitted and all of the job search steps he participated in, making connections had been most successful at landing an interview. So, he doubled down on networking.
After a conversation that Brian and I had about some of the top employers for Data Science Flatiron School graduates, he began connecting with graduates who worked at these companies. For one of these companies, GCOM Software, Brian reached out to 8 alumni and 3 responded. He had informational conversations with each to learn more about the company, the hiring process, and what it was like to work at GCOM. Ultimately, one of the Flatiron School graduates referred him to a consulting role at the company before he even applied.
Brian landed an interview and learned from his previous mistake. This time, he continued to network and apply to new roles throughout the interview process. This time, Brian was determined not to put all his eggs in one basket.
Brian’s Advice For Job Seekers
In the end, Brian was offered the job and he accepted. He has been working at GCOM Software as a Consultant II since October 2022. Looking back, he has plenty of advice on how to stay positive during the job search process.
“Job searching, especially if not currently employed, is one of the most stressful events in our lives. If you need a day off, take it,” he says. “Make time for yourself and take time for self-care.”
Brian also highlights the importance of having a solid support network to lean on.
“Try and find support in the shared experiences of other job-seekers through the virtual community Flatiron fosters” he recommends. Brian attended alumni connection events and regular trivia nights that helped him maintain a more consistently positive mindset.
Part of that positive mindset, he says, is “understanding that there are plenty of things about the job hunting process that you cannot control or affect and this is okay. Focus on what you can: staying positive in what way you are able, representing yourself professionally, continuing to grow, and learning from the experiences behind you.”
About Laura Nicolaisen
Laura Nicolaisen is a Career Coach with Flatiron School. She has 15-plus years of experience as a career coach collaborating with recent graduates, professionals, and executives. In addition, Laura has over 15 years of experience working in the university and bootcamp setting, in such areas as admissions, student advising, coaching, and as an executive team member.
Greg Damico, Technical Faculty Manager at Flatiron School, spent more than twenty years in academia. He accumulated advanced degrees in Physics, Ancient Greek, Philosophy, and Applied Mathematics in that time before ultimately deciding to move into tech.
Greg shares his journey from academia to tech below.
An Academic Brush With Data
To say that Greg’s background is “academic” is an understatement. Beginning with a Bachelor’s in Physics, he followed it up with a Master’s in Ancient Greek and a Ph.D. in Philosophy. But it wasn’t until he was back in the classroom (again) for another Master’s, this time in Applied Mathematics, that Data Science caught his attention.
“I took a class […] in scientific computing and really started to see the power of combining math and programming,” Greg recalled. “From my philosophy days, I also had an interest in things like the nature of the mind and artificial intelligence, so all of these things were pointing to data science.”
The Appeal Of A Change To Tech
After spending decades of his career in academia, Greg cited a desire for professional stability as his reason for ultimately making his exit from the field. Choosing tech, he said, was easy.
“Lots of things about tech are attractive,” he said. “There is a great diversity of jobs (because everyone needs tech, always a need for tech people, great potential for working remotely, lots of really cool tasks tech is contributing to (medical work, police work, plus all of the “purer” work in developing AI and robotics, etc.). And of course, the money is pretty good too.”
As for growing pains when transitioning, he mentioned that there weren’t many. His eclectic background had well prepared him for this new industry.
“I needed of course to develop my own programming skills, but then it was just a matter of applying them.”
His Experience In Tech
Greg attended an accelerated online bootcamp program to expand his programming knowledge. Afterward, he joined Flatiron School as a Data Science Instructor in 2019. He has since moved into a Technical Faculty Manager role, and – after a brief adjustment period to the faster pace of the industry – enjoys the new field.
“I like working in tech a lot. The main thing I had to adapt to was the increased speed of the work week. It’s not that there aren’t deadlines in academia, but they just tend to be softer,” he explained. “Since moving into tech I’ve found that I’ve needed to make decisions faster, and often that means reaching out to people on other teams and being able to rely on them.”
As for what he’s been working on at Flatiron School, his projects have focused on student-facing experiences and systems.
“I am largely responsible for our transition to the CodeGrade platform, which I think should provide a much-improved grading experience on checkpoints and code challenges for our live instructors. I also played a big role in crafting exit tickets that are used after live lectures.”
His interest in data science continues beyond business hours as well. Outside of his work at Flatiron School, he also enjoys “exploring statistical questions that arise in the context of sports.”
As for all of the knowledge he accumulated while in academia, Greg said that it still has a factor in his new career in Data Science.
“I don’t use my Greek every day,” he admitted. “But my philosophical training has absolutely been useful in the tech world. Philosophy trains you to ask good questions and think about new possibilities. This comes up all the time when asking questions like ‘how do we re-organize curriculum to address a new need?’ or ‘if we design this tool from scratch which features would we want to have as users?’ Philosophy also teaches about ethics, which is ever more relevant to the field of data science.”
Advice For Flatiron School Students
Looking back at his career thus far, Greg is most proud of his impact on learners.
“Maybe this is a little trite, but I’m very proud of helping to jump-start new careers,” he said. “Watching students go from zero to hero never gets old.”
His advice for those students, however, is succinct and to the point.
“Do not be shy about asking for help, especially from your peers! Two heads are better than one, and collaboration will be important wherever you go anyway.”
To learn more about Greg’s work, visit his website and LinkedIn.
Ready To Make A Change, Just Like Greg Damico?
Inspired by Greg’s career pivot story? Apply Today to our Data Science Course to take charge of your future in as little as 15 weeks.
Not quite ready to apply? Book a 10-minute chat with admissions to see if you qualify, or test-drive the material with Data Science Prep.
By continuing your journey on our site, you consent to the placement of cookies. To learn more about how we use cookies or how you can disable them, please see our Cookie Policy.
This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
Cookie
Duration
Description
cookielawinfo-checkbox-analytics
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional
11 months
The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy
11 months
The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
