Milena Afeworki: Civil Engineering to Data Science

Milena Afeworki, a September 2021 Data Science graduate from Flatiron School, began her career as a civil engineer in Northeast Africa. Now, she is living in California and thriving as a Data Scientist.


Milena Afeworki began her career in Eritrea, a country in the Northeast of Africa on the coast of the Red Sea. She earned a Civil Engineering degree from the Eritrean Institute of Technology, joining a consulting and engineering firm after graduation where she explored her interest in mathematics and data visualization. 

“I began as a Structural Civil Engineer, working on Infrastructure design projects in various sectors, including public, and NGO initiatives,” she explained. “I was passionate about solving problems using mathematical analysis, design, and visualization which was my main reason for choosing the field in the first place.” 

But, 5 years and a move to the USA later, she saw just how big an impact data could have on the world – and she wanted to be a part of it. 

“After moving to the US my network grew, I learned more and was captured by the beauty of how much data was behind the projects being launched,” she said. “I wanted to be part of the bigger determining factor that drives the decisions of projects that are set in motion.” 

Though many would consider Civil Engineering and Data Science completely unrelated, Milena says there is a thread of logic that weaves the disciplines together.

“There is a lot of similarity between the two fields. Both aim to solve engineering problems by optimizing processes and resources,” she explained. “Moreover, resolving intricate engineering problems and presenting the outcomes through impactful visualizations to offer valuable insights to guide business strategies interested me more.” 

Bootcamp Experience

Despite the similarity of logic used in both the fields of engineering and data science, the tools used differed significantly. Making the transition between fields would require Milena to expand her skill set to include Python, Statistical Analysis, Machine Learning, and Deep Learning. To expedite her learning process, she turned to a Data Science bootcamp – specifically, to Flatiron School.

“Flatiron School was first my choice because of its reputable Data Science program and structured career coaching,” she recalled. “I wanted to gain hands-on skills and practical experience in designing and building data science projects applying Machine learning tools. The program’s curriculum, which covered relevant tools, languages, and frameworks, aligned with my career goals.”

With her goals set, Milena enrolled in Flatiron School’s Data Science Live program. A full-time, immersive course designed to teach students the data science fundamentals they need to enter the industry, the program moves quickly over 15 weeks of instruction. Milena remembers initially struggling with the accelerated pace of learning. 

“Understanding the intricacies of data science in a fast-paced learning environment [was challenging]. Handling large volumes of data efficiently and effectively required a deep understanding of various tools and techniques,” she explained. “Given the program’s demanding nature, I occasionally encountered frustration when confronted with obstacles, but such moments ultimately pushed me to persistently seek solutions.”

Unsurprisingly, Milena’s favorite part of the program was what drew her to the field initially – seeing, and being part of, projects with impact. 

“My favorite part of the program was working on real-world assignments. These projects simulated the challenges that Data Scientists face in the industry, allowing me to apply the concepts I learned, incorporating my previous experience and enhancing my problem-solving skills.” 

Job Search

Milena graduated in September 2021 and began the job search, supported by her dedicated Flatiron School Career Coach. 

“My career coach played a crucial role in providing guidance on crafting my resume, preparing for interviews, and expanding my network,” she recalled. “I owe my interviewing skills and networking skills to my coach, without whom it would have been difficult.”

Ultimately, Milena accepted a Data Integration Engineer position, which she thanks her Career Coach for helping her land. 

“Her professional insights and emotional support helped me navigate the competitive job market and ultimately secure my first Data Engineering role.” 

Working In The Field

When we spoke with Milena, she’d been working at her company for 1.5 years and had received a promotion to Data Analyst. Her experiences in the field, she said, have been exactly what she hoped for. 

“Working in the field of data engineering has been an incredibly rewarding experience for me. The opportunity to work with cutting-edge tools and technologies to solve complex data challenges is fulfilling and aligns well with my passion for creating efficient data solutions,” she explained. “Overall, I’m thoroughly enjoying my journey as a data engineer, and I’m excited to see where my career leads.”

She is also particularly proud of a project she worked on that tied back to her original career in civil engineering, examined through the lens of data science. 

“I designed and implemented a Classification Model on the Structural condition of Bridges in the US, using climate data from NASA and bridge records from the Department of Transportation. The purpose of the project was intended for the bridge management system to be capable of accurately predicting future bridge conditions and help make an informed decision on when and where to allocate maintenance budgets.”

Visit Milena’s LinkedIn page to see what else she’s up to. 

Reflecting On Her Journey

Looking back on where she began, a career and a continent ago, Milena credits her adaptability for her success in pivoting professions by way of a bootcamp. 

“The most significant lesson I gained from my time at Flatiron School was developing the skill to thrive in uncertain and ambiguous scenarios,” she said. “I’ve come to view these situations as valuable opportunities for me to inquire, observe, learn, and embrace mistakes, all of which contributed to my growth beyond my initial capabilities.”

But, remembering the challenges she experienced at the beginning of her program, she recommends that future students just keep moving forward and working at the material, even if it feels impossible at first.

“Expect a learning curve where immediate progress might not be apparent. But keep persisting, and once those skills take root, you’ll be amazed by how far you’ve come.”

Ready To Dig Deeper Into Data, Just Like Milena Afeworki?

Apply Now to join other students like Milena Afeworki in a program that sets you apart from the competition. 

Not ready to apply? Try out our Free Data 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.

Read more stories about successful career changes on the Flatiron School blog.

How Much Math Do You Need to Become a Data Scientist?

Have you ever considered a career in data science but been intimidated by the math requirements? While data science is built on top of a lot of math, the amount of math required to become a practicing data scientist may be less than you think.

The “Big Three” In Data Science

When you Google the math requirements for data science, the three disciplines that consistently come up are calculus, linear algebra, and statistics. The good news is that — for most data science positions at least — the only kind of math you need to become intimately familiar with is statistics.


For many people with unpleasant memories of mathematics from high school or college, the thought that they’ll have to re-learn calculus is a real obstacle to becoming a data scientist.

In practice, while many elements of data science depend on calculus, you may not need to (re)learn as much as you might expect. For most data scientists, it’s really only vital to understand the principles of calculus and how those principles might affect your models. 

If you understand that the derivative of a function returns its rate of change, for example, then it’ll make sense that the rate of change trends toward zero as the graph of the function flattens out. 

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That, in turn, will allow you to understand how gradient descent works by finding the local minima for a function. It’ll also make it clear that a traditional gradient descent only works well for functions with a single minima. If you have multiple minima (or saddle points), a gradient descent might find a local minima without finding the global minima unless you start from multiple points.

local vs global minima

Now, if it’s been a while since you did high school math, the last few sentences might sound a little dense. But the good news is that you can learn all of these principles fairly quickly.  And it’s way less complicated than being able to algebraically solve a differential equation, which (as a practicing data scientist) you’ll probably never have to do — that’s what we have computers and numerical approximations for!

Want to try your hand at Data Science? Try out our Free Data Science Prep Work and test-run the material we teach in the course.

Linear algebra

In data science, your computer is going to be using linear algebra to efficiently perform many required calculations. If you perform a Principal Component Analysis to reduce the dimensionality of your data, you’ll be using linear algebra. If you’re working with neural networks, the representation and processing of the network are also going to be performed using linear algebra. In fact, it’s hard to think of many models that aren’t implemented using linear algebra under the hood for the calculations.

At the same time, it’s very unlikely that you’re going to be handwriting code to apply transformations to matrices when applying existing models to your particular data set. So, again, understanding the principles will be important, but you won’t need to be a linear algebra guru to model most problems effectively.

Probability and statistics

The bad news is that this is a domain you’re really going to have to learn. And, if you don’t have a strong background in probability and statistics, learning enough to become a practicing data scientist will take a significant chunk of time. The good news is that there is no single concept in this field that’s super difficult — you just need to take the time to really internalize the basics and then build from there.

Even more math

There are lots of other types of math that may also help you when thinking about how to solve a data science problem. They include:

Discrete math

This isn’t math that won’t blab. Rather, it’s mathematics dealing with numbers with finite precision. In continuous math, you are often working with functions that could (at least theoretically) be calculated for any possible set of values and with any necessary degree of precision.

As soon as you start to use computers for math, you’re in the world of discrete mathematics because each number only has so many “bits” available to represent it. There are several principles from discrete math that will both serve as constraints and inspiration for approaches to solving problems.

Graph theory

Certain classes of problems can be solved using graph theory. Whether you’re looking to optimize routes for a shipping system or building a fraud detection system, a graph-based approach will sometimes outperform other solutions.

Information theory

You’re going to bump up along the edges of information theory pretty often while learning data science. Whether you’re optimizing the information gained when building a decision tree or maximizing the information retained using Principal Component Analysis, information theory is at the heart of many optimizations used for data science models.

The good news

If you’re terrified of math or unwilling to ever look at an equation, you’re not going to have much fun as a data scientist or data analyst. If, however, you have taken high school-level math and are willing to invest some time to improve your familiarity with probability and statistics and to learn the principles underlying calculus and linear algebra, then math should not get in the way of you becoming a professional data scientist.

Ready To Get Started In Data Science?

As a next step, we’d encourage you to Try out our Free Data Science Prep Work to see if data science is right for you.

If you realize you like it, apply today to get started learning the skills you need to become a professional Data Scientist.

Not sure if you can do it? Read stories about students just like you who successfully changed careers on the Flatiron School blog.

Why Financial Services Should Invest In Their Employees

Most financial services know that training is important. Whether training is optional or compulsory, most of their employees have gone through at least one course of training. 

The downfall, however, is that many companies consider it as secondary to primary business operations. They think of it as something they just have to do, instead of an opportunity to invest in their employees and the company’s future growth. In reality, training is critical to the profitability and growth of the company.

Here’s why when you invest in your employees, you reap the greatest reward.

Skills Quickly Become Obsolete

The primary reason financial services should invest in regular employee training is that skills are becoming obsolete at a faster rate than ever before. 

Research has found that skills have a “shelf life” of about 5 years. This is cut in half for technical skills, with obsolescence at just 2.5 years. This ever-shortening shelf life of technical skills requires consistent re-skilling to keep employees’ abilities relevant and useful. 

Unfortunately, many companies are already behind. According to McKinsey & Company, 87% of companies worldwide admit to having a skills gap in their workforce or anticipate one in the next few years. These industry-leading organizations are already playing catchup. 

More Jobs But Fewer Workers

Today’s market is highly competitive, with each business endeavoring to develop the next world-changing innovation. These innovations, however, cannot be made by the company itself. They must be made by the bright minds working there. 

As a result, the stakes to attract and retain top talent are high. 

But, due in part to the impact of the COVID-19 pandemic, workers have become more selective about the jobs they take. Even while companies are expanding their technical departments, they have to work harder to attract top talent to fill open roles. 

Rising Hiring Costs

With a skill gap on teams, and the need to hire more, many companies put more effort into hiring. But, rising hiring costs are putting a limit on the lengths companies can or will go to bring talent in the door. 

The Society for Human Resource Management states that the average cost to hire an employee is $4,129, with around 42 days to fill a position. According to Glassdoor, the average company in the United States spends about $4,000 to hire a new employee, taking up to 52 days to fill a position.

In addition, the cost of replacing an individual employee can range from one-half to two times the employee’s annual salary.

With such high costs associated with bringing in new employees, companies are turning to training to close skills gaps on their team, no hiring required. 

Invest In The Right Training

The benefits of investing in training employees are clear – closed skill gaps, retained workforce, and eliminated hiring costs. 

But, just as important as executing training, is choosing the right provider with programs that will satisfy your business needs. Choose carefully to reap the greatest reward. 

Here’s what to consider when getting started.

Diversify Training

Leading upskilling practices recommend incorporating both digital and soft skills into training to help finance employees influence decision-making. Don’t focus only on growing technical expertise, use training opportunities to grow employees into well-rounded team members. 

Select The Right Provider

When picking a training provider, it’s important to make a decision based on the factors important to your business. This could be specific software or languages offered, schedule flexibility, program length, in-person or remote delivery options, or customization options. 
Picking a provider with a track record of success working with companies like yours can also make the difference between an experience that’s just okay, and one that builds your employees into star performers.

Celebrate Achievement

Most importantly, recognize and celebrate your employees’ educational achievements. Learning something new can be difficult, and recognizing even incremental improvements and celebrating upskilling achievements can add up to big cultural change.

Training Programs Made For Financial Services

When you invest in your employees, you invest in your company too. Why not invest in the best? 

Let Flatiron School modernize your business with industry-tailored training programs in Cybersecurity, Data Science, Software Engineering, and Product Design.

Contact us to get started.

Learning How to Learn

This article on “Learning How To Learn” is part of a series developed by Curriculum Design to guide students through the Flatiron School program experience.

We believe that when learners feel autonomous and in control of their learning, they achieve greater success both academically and motivationally. Learning to Learn is designed to offer a variety of resources and tools to help you take control of your online learning journey and life beyond Flatiron School.

Take Ownership Of Your Learning

Taking ownership of your learning journey, through personalized learning, means finding your motivation, being engaged, and personalizing your learning experience with complete autonomy, choice, and responsibility in how you approach your online learning journey. Every learner has a fundamental need to feel in control of what they do versus only being told what to do. When this autonomy is exercised, the motivation to learn and the desire to perform well academically are much stronger.

As you go through the Learning to Learn series, our goal is to encourage you to take ownership of your learning journey- make decisions that matter, pursue directions that feel meaningful, and hold a sense of responsibility and control for both your learning successes and setbacks.

Connect The Dots

Taking the leap to build technical skills takes courage and determination. It can be intimidating to dive into new skill sets and knowledge, but the rewards and sacrifice will be worth it. As you learn, your horizon will expand and the information you collect along the way will start to connect in unexpected ways.

The saying goes, knowledge is power, and when it comes to personal and professional growth, this couldn’t be more true. When we actively seek knowledge through experiences or formal education, we add another “dot” to our mental map. These dots, connected, generate new ideas and help to solve problems in unique ways. Some of the greatest innovators credit their success to continue expanding their knowledge base through both life experiences and deliberate learning sessions.

Continue adding dots to your map.


  • Personalized learning is a great way to improve your skills and knowledge base.
  • Learning on your own can be intimidating to start, but the rewards are worth it.
  • Seek out new experiences and resources to challenge yourself and broaden your perspectives.