TDI Is Turning Dreams into Reality for Aspiring Data Scientists

Data is changing the way our world works. The Data Incubator gives professionals a chance to join this growing industry and advance their careers in data. As a fellowship, scholarship, and placement company, TDI’s comprehensive program prepares professionals for top data industry jobs, with about 450 companies hiring their graduates. Additionally, students see an 88% employment rate within six months and average $128k+ compensation—a testament to the countless resources at their disposal. 

But what do graduates have to say about their experience?

Below you’ll find an excerpt from a panel interview of TDI graduates about their experiences with the program.  

Background of panelists

Adam GiffordAdam Gifford

Adam Gifford is a highly experienced data scientist with a Ph.D. in neuroscience from the University of Pennsylvania. He honed his skills in academia and later transitioned to the industry through The Data Incubator’s Data Science Fellowship as part of the 2021 Winter Cohort. Adam is now principal data scientist at BehaVR, where he uses virtual reality to educate and motivate healthy behaviors. He brings a wealth of expertise and a drive to make an impact in the data science field.

Robert SchabingerRobert Schabinger 

Robert Schabinger is a high-energy particle physics researcher turned data scientist. Robert honed his skills at The Data Incubator Winter 2021 Cohort, where he created a web application to summarize Amazon product reviews in real time. He now works at DCI Solutions as a navigational analyst, using machine learning to develop advanced cybersecurity solutions for military contractors. Robert’s unique background and expertise make him a valuable asset to the data science community.

Robert ZupanRobert Zupan

Robert Zupan is a skilled data scientist with a Ph.D. in Computational Mechanics from the University of Pittsburgh. He honed his expertise in data analysis and programming through his postdoctoral work and teaching experience. At The Data Incubator’s Winter 2021 cohort, Robert sharpened his skills to excel in the data science field. Today, Robert is a valued member of the team at Afiniti, where he uses his statistical models to optimize customer interactions with agents. 

Trey williamsTrey Williams

Trey Williams, a proud Boston Native and Northeastern University graduate, embodies the qualities of a successful data scientist. At The Data Incubator’s Fall 2021 Cohort, Trey upgraded his skills and honed his expertise. With his unique perspective, critical thinking skills, and entrepreneurial mindset, Trey has a passion for solving complex problems. His dedication paid off, as McKesson recently hired him as a data scientist. Trey is not just focused on his own success but also on creating a more diverse and inclusive industry. He is committed to increasing opportunities for people of color in STEM and is poised to significantly impact the field.

Here are their responses to some critical questions about their experience with TDI. 

What does your current role at your company look like?

Robert Z: As a data scientist, I create statistical models using Bayesian statistics to optimize the pairing of customers and agents. This can be based on various factors, such as the customer’s phone number, account information, and the choices they make while calling a customer service line. I work on sales and non-sales accounts, such as a healthcare company, where I help optimize patient care. However, maintaining the accuracy and integrity of the data is a significant part of my job, as it involves debugging and preventing problems from arising, as well as catching any changes in data early. In fact, maintaining the data is actually a larger portion of my job than creating the statistical models

Adam: As the principal data scientist at BehaVR series B startup, I have a versatile role that covers various aspects of data science. My responsibilities include exploratory data analysis, statistics, and machine learning. I analyze how users interact with our virtual reality products and use statistical methods to determine their efficacy. I am also involved in developing a machine-learning model to predict users’ improvements. Currently, I am implementing a data science machine learning architecture in Azure to streamline the process from analysis to deployment. Although my role requires me to be a jack of all trades, I hope to specialize in pure data science in the future.

Robert S: As a data scientist at a small but rapidly growing company, my role is quite diverse and includes a mix of software development, mathematics, and research. Despite my job title, only about 20% of my work is focused on data science in the traditional sense, with a significant portion dedicated to cybersecurity software development, which makes up around 50% of my responsibilities. The remaining 10% of my work involves handling trade secrets and controlled unclassified information that I cannot discuss.

It’s a unique working environment, being part of a government-side startup, where there’s no venture capital, and the rules are different. This often involves trying to impress government clients to secure interesting contracts. Although I’ve been employed at the company since May of last year, I haven’t even reached the level of top-secret information yet. I am only working with controlled unclassified information, which is information that is not particularly sensitive but cannot be republished or discussed publicly. 

Trey: I just started my job two months ago, and so far, I have been handling a lot of projects that were in the process of being deployed. It is a mix of both data science and software engineering. I am trying to implement test-driven development and move away from functional programming by using more object-oriented programming to develop the specific models we are working on. At times, it’s all about the math and computational aspects of data science. Still, software engineering is involved as we try to figure out how to scale the models to be efficient once they are deployed. Recently, I took over a project where I was working on anomaly detection and trying to develop a high-level approach to dealing with different anomalies in various situations. This is the project that I am currently working on. 

What made you decide to join the TDI program?

Robert S: I’ve always had these skills and believed that they were worth something and that someone would be happy to employ me. But it was never clear where to apply them. I didn’t even know there were opportunities out there, let alone that the government would employ data scientists for unrelated tasks, such as cybersecurity analytics. It’s fascinating to see the vast array of possibilities and to understand which skills are in high demand. That’s what TDI really brings to the table. I can honestly say that it was the best money I’ve ever spent. I wholeheartedly endorse it.

Adam: I faced similar challenges to Robert when I started my journey in data science. I was aware of the demand for my skills but felt overwhelmed with the vastness of information available online. I was unsure of what to learn first and where to find credible resources. This is where TDI (The Data Incubator) came to my rescue.

 At TDI, I was taught by experienced data scientists, who walked me through the entire process of developing and deploying a machine learning model – from exploratory data analysis to natural language processing. This reassured me that I was learning the tools and strategies that real data scientists use.

Moreover, at the end of the boot camp, I had a complete project to showcase, which I had published on GitHub and Heroku. This was a huge plus, as it gave me something tangible to showcase to potential employers.

Another benefit of TDI was its network of hiring partners. Since TDI has worked with these partners in the past, they are aware of the quality of data scientists produced by TDI. This gave me a leg up when applying for jobs, as I had a better chance of getting an interview with these hiring partners.

When applying for jobs on my own, I applied to around 100 jobs and got only a few interviews. However, after joining TDI, I applied to only 15 jobs and got interviews for almost all of them. This made a massive difference for me and was one of the main reasons why I wanted to join TDI.

Robert S: As for me, I can confirm that sentiment. My journey to TDI was a bit different. I came directly from my previous career and never even tried to get a job in this field before. I knew I wouldn’t be successful since programming skills were essential in this industry, and I wasn’t a strong programmer at that time. However, I had a colleague who was an exceptional programmer and applied to 50 jobs but only received one interview. He was worried about what was wrong with him, but I told him that it was just a matter of being recognized for his skills and told him that he had to go to TDI. He took my advice and eventually landed a job at Revalue Lamps. It just shows that if you have the skills, you just need to be recognized for them.

Robert Z: I agree entirely with the sentiments expressed by Adam and Robert. It’s amazing that the partner system at TDI made it so easy for me to get interviews, to the point where companies were reaching out to me for interviews. I don’t think this kind of opportunity ever comes around outside of a program like TDI. I wholeheartedly agree with what Adam and Robert have said.

Trey: As a self-proclaimed data scientist, I thought I didn’t need the validation of the industry. However, I was proven wrong when I joined TDI. The program not only helped refine my skills but also equipped me with a new set of tools to tackle bigger challenges. Before TDI, I felt uncertain in various situations, but now I feel much more confident. The community aspect of TDI is also great, as I have made many friends who are data scientists or engineers. Whenever I encounter a problem, I can turn to them for support, and we can reminisce about our time at TDI and the many projects we worked on.

alumni panelWhat are your favorite parts of participating in the program?

Robert S: For me, it was the projects and the lessons learned from there. 

Robert Z: Yeah. I completely agree with you. The mini-projects truly showcase what it takes to be a data scientist. They give you the hands-on experience of taking a problem, fitting a model to it, and actually applying the solution to real-world scenarios. I’ve been able to take the skills I learned from those projects and apply them to my job, and it’s been beneficial. The many projects were definitely the highlight of the training for me.

Trey: I really enjoyed the lectures as they provided a space for individuals to express their ideas, engage in collaborative brainstorming and speak their minds. This was helpful in preparing for the many projects and helped me figure out how I could apply what I learned to my own work.

Adam: I found the professional resume writing service offered at the beginning of the program extremely helpful. They reviewed my data science resume and tailored it to the specific job I was interested in, ensuring I hit all the key points required for the position. The TDI-provided resume builder app or site also allowed me to publish it in a visually appealing format designed to make it through screening services for data science positions. This helped me create a professional and robust resume, which was a massive help.

What was the most challenging part of the program?

Robert S: As for me, I was worried about the coding challenges at first because I had no prior experience with Python. I had only done exams in Mathematica, as that was the main programming language used in my field of physics. However, I was grateful for the opportunity to participate in TDI, even though I may not have appeared to be the strongest candidate for data science at first. Despite this, I had confidence in my problem-solving abilities, which shined through in the end. I was given a chance to join TDI because I believe I was a good fit for the program. There was a question that popped up about how much programming experience was required, and I can say that while I had experience with other programming languages, I was lacking in Python knowledge. But, I learned quickly, and there are many online resources, including TDI’s prep-materials, to help with learning Python and becoming proficient in it. 

Robert Z: I totally agree. The coding challenges at the beginning of each day were definitely a challenge for me, even though I had some experience with Python. Recursion was especially challenging for me, even though I understood it for the most part. The mini-projects were challenging too, but they gave you more time to ask questions and collaborate with others, which was really helpful. On the other hand, the coding challenges were more like what you might see in a job interview, quick and intense, and that was a challenge at the time.

Trey: As for me, the SQL mini project was a challenge. I thought I was an expert in SQL, but the project showed me that I still had a lot to learn. TDI does a great job of challenging you and making you realize there’s always room for growth.

Another highlight for me was the capstone project. In this project, I had to create a data product that was useful to someone. This was a change from just fitting models to data, which I was used to. This project put me in the shoes of a stakeholder, and it was a valuable experience for me.

Adam: I can relate to what Trey said about the coding challenges being tough. For me, the biggest challenge was figuring out how to approach each task. Some skills were easier to memorize, but others required a different way of thinking. However, what I found the most challenging was the capstone project. This was my first time trying to put together an end-to-end data science project, which included defining a problem statement, finding the right data to answer that question, exploring and cleaning the data, building a model, and testing it. The hard part wasn’t just building a model but also figuring out how to make it accessible and usable by someone who doesn’t know much about coding. I had to turn my model into a web app or another product that someone without technical expertise could use. And finally, I had to be able to sell the story behind the project so that it would be attractive to others. This experience was helpful for me because now I know how to start a project from scratch and finish it with a usable product. This is important in a job interview because you want to show that you can complete a project from start to finish and that the project is worth doing.

Can you elaborate a bit more about your experience, the job placement resources, and things that you took advantage of or support that you utilized in the program? 

Robert Z: As for me, I can’t recall the exact meaning behind the acronym CRM, but it played a significant role in my job search journey. The CRM was essentially a database that kept track of all the active and inactive partners, allowing me to filter by various parameters like location, hiring status and more. This made it easy for me to zero in on the partners I was most interested in. But what I found even more impressive was that the partners could also look for me. I was pleasantly surprised when two companies reached out to me, which never happens in a job hunt. To me, CRM was a valuable tool that made my job search much more manageable and effective.

Robert S: Yeah, that’s how I got my job. It was not one of the interviews I requested. It was an interview that happened because somebody asked me. And so I think that’s a very unique thing to do. 

Getting interviews was also a very simple process of just reaching out. I was always able to get at least a bit of interest, even if the posting was sometimes vague. Some startups I contacted didn’t have secure funding to hire me, but they were still curious to see what was available. There was a lot of communication and exchanging of information, which I would say was beneficial.

Adam: I used the CRM a lot as well. However, the job I got was from one of the jobs TDI posted on the slack channel, which they do daily or every few days. 

Trey: I want to agree with everyone. What I took advantage of was focusing on the job profile I had at the CRM, and that’s how I was able to land my position right now. I had several companies reach out to me. With CRM, I was able to prove my worth and stand out, something that you don’t get on LinkedIn. 

Robert S: I want to add something. It’s very tricky when you apply outside of TDI, as many requirements for the jobs on paper can sound scary. On TDI, even if it had scary conditions, I would still be allowed a fair hearing in the interviews. I didn’t feel like I was excluded. Instead, I felt like I had a chance. 

Is there anything you do at your current job that you developed at TDI?

Robert Z: The biggest one for me is SQL. At my job, there are these large data sets, we deal with calls for AT&T with millions of data points per day, with hundreds of columns and SQL is basically 70% of my job. 

Trey: I would say leveraging flask and understanding version control through GIT.

Robert S: For me, it’s the core Python programming. 

Adam: I use a little of everything, SQL, Python, Jupyter notebook and a lot of the database training we’ve done.

I also downloaded the entire repo from Jupiter hub, and I refer to it at least monthly. 

What do you think is the most important thing that TDI graduates walk away with?

Robert S: A job

Robert Z: TDI gives people an engineering instinct. TDI offers people the ability to look at a dataset and know how to find trends and see changes in the data. 

Trey: Value in the market. I’ve had several people try to poach me from my current position just because of my existing credentials. There’s an inherent value in it.

Join the Ranks of TDI Alumni!

Ready to kick-start your data science career? There’s never been a better time than now. The Data Incubator has you covered with its data science boot camps and programs, helping you master the skills for your dream job.

You can learn more about our programs here:

  • Data Engineering Bootcamp: This program teaches you the skills to build data infrastructures, design better models and effortlessly maintain data. 
  • Data Science Essentials: This program is perfect for you if you want to expand your data experience and improve your current skill sets. 
  • Data Science Bootcamp: This provides you with an immersive, hands-on experience. It helps you master in-demand skills to start your career in data science.

Contact our admissions team if you have any queries regarding the application process.

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