1. 1. Can we meet you?
   
   Chukwuebuka Emmanuel Okoro, I Am a Financial Analyst for one of the biggest banks in Africa.
   
   
   2. Can you walk us through a data science project you've worked on that you're particularly proud of? What challenges did you face, and how did you overcome them?
    

I worked on a shopping mall project with a logistics issue, where we had to extract the data, clean it up, arrange it, and determine the location where each item was headed. What I really enjoyed was solving the problem because, in data analytics, the usual thing is solving the problem. Instead of writing stuff manually and dispatching it one by one, you can dispatch in batches depending on regions and locations.
 

The dataset needed to be extracted first, then cleaned and categorized because the project was on logistics. We needed to categorize it into 6-7 regions, like different states in each region, so each region had a code. Anyone who makes an order will be assigned to one of the regions, depending on the code. We could identify the region that placed the order based on the code. We also attached priorities to it because free shipping takes more time than priority shipping. We couldn't lump everything into the same order, so we broke it down into sections.
 

First, we had the lump data, and we needed to filter it into regions because there were several regions. We sourced data based on the region, then worked on each region. We aimed to save costs as a company, ensuring customers received their orders on time. Priority orders have a price, and free shipping has a price. It was interesting seeing it work on the back end.
 

The only challenge was sourcing and cleaning the data because there were thousands of orders. Sometimes, customer orders weren't properly written, maybe due to the way the name or amount was typed, which caused errors. Once the data was clean, it was easier to present using Power BI and categorize it by region. We enabled a set of priority orders to send to the logistics company for dispatch, considering the time frame. Customers could track their orders, which was the only challenge; after that, everything was good.
 

Now that I think about it, I miss working on that project and those types of datasets. In my current role as a finance analyst, I still work with data. There's a rule for financial analysts: we see the statement and customers who want to collect loans, so we look into that and submit it to management to make decisions.

3. What inspired you to pursue a career in Data Science?
 

I think a couple of years back, we all know in Nigeria that it's not what you studied that you will work with. The bank job is the easiest to get, and it is still the easiest to get. I just found myself in the bank, and it was supposed to be a temporary thing, but I just started enjoying it. That was what led me to get my first master's degree in Business Administration, so I had an MBA in Finance and Investment after I had worked in the bank for 3 years, that was when I got my MBA. So, the MBA opened me up to finance, Business Analytics, kind of Data Science, but more of Data Analytics, and finance analytics. So, when I started in the bank, I started as a customer service officer, but when I finished my MBA, I switched to the risk management department as a financial analyst. So, I think that just opened my mind to working with numbers, and I started researching on what kind of tech skill... because Tech is the new gold, yeah... since I already have a background in engineering, I decided to go for tech... that was what made me go into Data Science. Tech has a good career, and it's well. So, the tech settled for the synergy between engineering and tech, and I'm currently doing my master's in Data Science and enjoyed what I learned from Utiva.

4. How has the program changed your career or life direction?
 

It has made me push forward and I’m currently doing my masters in the UK Data Science.

5. How do you approach the task of cleaning and preprocessing data? Can you share a specific technique you find particularly effective?
 

Just finding common ground, something that is unique for each candidate. Having a unique identifier for each person, with this I'm able to check for duplicates. Once you have that, everything is done.

6. What advice would you give to someone just starting out in data science, especially when it comes to developing essential skills and gaining practical experience?
 

It can be overwhelming if you are scared of numbers or calculations. If you're someone who gets tired easily, so you don't smash your system one day due to error messages - it is just like coding. When you are trying to run a code it is giving you errors just because of a little error. But if you like numbers, you will enjoy it. Keep practicing, and you will get it done.

7. Data science often involves working with large datasets. How do you ensure data accuracy and maintain quality throughout your projects? 
 

I group them because it is easier to work on a small data set than working on a large data set. That is what works for me to avoid duplicates, so I break them down to assure accuracy.

8.  How can our readers connect with you?

Readers can connect with me via LinkedIn  Here