Chukwuemeka Okoli

Chukwuemeka Okoli was exposed to various data science techniques during his time at the university. Upon completing his Master’s Degree in Petroleum Engineering, he looked to explore a career in data science. He started by using open resources to learn some programming skills but soon struggled to translate that knowledge into solving real-world problems.

That’s when he found Practicum and joined its Data Science Bootcamp. There, he developed the relevant experience, which led him to his current role as a machine learning petroleum engineer at Leidos Inc. Here’s his story.

Tell us about your background. What were you doing before attending the program?

I completed my Master’s Degree in Petroleum Engineering where I applied data science techniques in forecasting oil and gas reserves primarily using the R programming language. I decided to learn more about it, using various MOOCs [Massive Open Online Courses]. First, I started by learning Python programming language, then I learned Microsoft SQL Server and PostgreSQL.

What motivated you to explore a new career, and why did you decide to pick Practicum?

I wanted to become a data scientist. I started looking at job descriptions for data scientists and realized that I had a skill gap even though I did a lot of data science work in the university. I decided to focus on the essential skills to become a data scientist or machine learning data scientist.

Despite completing so many of those programs, I was lost when it came to starting a project. I did not know how to think like a data scientist and complete an end-to-end project. I started looking for a project-based bootcamp where I could get hands-on data science experience. After reviewing various bootcamp in the US, I chose Practicum Data Scientist bootcamp.

What did you like about the program? Are there any highlights that stood out to you?

First, the program structure stood out to me. I loved the projects and project reviews at the end of each module. The bootcamp applies a hands-on approach. You solve several challenges from real industry datasets and complete over 15 data science projects.

Practicum also offers more support than you get from MOOCs with detailed feedback on projects and guidance from a professional. [I also enjoyed] the optional career support…which included mock interviews…video workshops, weekly webinars, peer-to-peer sessions, and Slack chats on diverse topics.

Practicum seems to be one of the most accessible data science bootcamps in the US. The current price is $1,400 per month for nine months or $11,000 for the full price. There is also a money-back guarantee, so, if you don’t get a new job or promotion within six months of graduating, Practicum will refund your money.

How did you fit the program into your schedule?

My program was part-time. I had a lot of flexibility in fitting it into my schedule. There were months when I put in a lot of effort, there were also times when I relaxed a bit. The most important thing was meeting the deadline for each project and going along with your cohort.

Can you give us any examples of projects that you worked on during the program?

I worked on so many projects. I built a random forest classifier to analyze subscribers’ behavior and recommend the right phone plan. I built a predictive model to predict customer churn from a bank based on customers’ past behavior.

Adding to that, I developed a machine learning algorithm for optimizing well placement, predicting the volume of reserves in new wells and selecting well locations that will maximize profit while minimizing risk.

Do you have any advice for someone considering this program?

I would say go for it. Once you’ve completed a couple of projects, your confidence level improves, and you can start applying for data science roles. In addition, don’t let imposter syndrome hinder you from applying for any data science role as long as you meet about 60 percent of the job requirement.

Did you find a new position after the program?

Yes. I found a new job [as a machine learning petroleum engineer].

How did the program support you in finding a job?

I got the job before completing the optional career preparation course, but that support [is available] if you need it. I am still completing the career preparation course even though I have a job. I still want to take advantage of that.

Was the job search process different from what you expected?

The job search was different. I started even before the program ended so I made a lot of mistakes and messed up in a lot of final onsite, too. That is why I will still complete the career preparation course so I don’t make the same mistakes I made when I did it on my own.

How many companies did you interview at? How did you choose which one to work with?

I was interviewed at a couple of companies. I got to the final onsite [interviews] at DoorDash, Verizon, CVS Health, and TransUnion for data scientist roles, and Leidos Inc for the machine learning engineer role. I chose Leidos because of the people I will be working with and the job I will be doing. My goal is to become a machine learning engineer and Leidos offered me a role to do that.

How are the skills you gained from the course useful in your current career?

The skills I gained [from Practicum] are useful because I will be applying AI/ML methods to generate models and tools relating to subsurface geologic natural and engineered systems. I will also be designing a variety of supervised and unsupervised AI/ML algorithms after assessing the most effective methods.

What do you think is different about your life now versus before the program?

I am fluent with numerous tools like Pandas, NumPy, Sci-kit learn, and various machine learning algorithms. I know how to approach any data science problem, and apply analytics to business cases. I am also proficient in programming with Python and building various machine learning algorithms.

What do you find fulfilling about your current line of work?

I find cross-training and knowledge-sharing among our team to be fulfilling, plus guidance from my managers. Likewise, I enjoy building algorithms and deciding on the best algorithm for each unique use case. Working with the deployment and monitoring team is also fulfilling because I also want to get a hang of that.

What do you enjoy about working at your current company? Are there any specific perks you enjoy?

I love the fact that I am working from home. I don’t have to commute to work. I miss the office environment and physically meeting my co-workers but for now, I am enjoying the remote work. There are also paid time-off, robust medical, vision, and dental benefits.

Do you have any job search advice for someone considering a career in your field?

Make connections on LinkedIn and go to hiring events. Make yourself known in the data science industry. Open a Medium account and blog about your learning experiences. When recruiters look you up, they see someone who is engaging.