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Why Moe Obeidat Chose Pitt’s Online Master of Data Science

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Why Moe Obeidat Chose Pitt’s Online Master of Data Science

Moe Obeidat knows data. Based in the Chicago area, he works as a data analyst at a Fortune 500 company, where reporting, visualization, and SQL are part of his everyday toolkit. But like many working professionals in the field, Moe was eager to take the next step: moving from analysis into advanced roles in data science and machine learning.

That’s what led him to the University of Pittsburgh’s online Master of Data Science degree program (MDS).

“I was looking online for programs that matched the hours that I could do at my own pace as a full-time employee, and I wanted a school that was well known,” Moe shared. “Pitt is an R1 school. The program aligned well with my experience and with what I was hoping to learn and get out of it.”

From theory to real-world application

With an economics background and prior coursework in math, statistics, and econometrics, Moe found the MDS curriculum both familiar and refreshingly applicable.

“In undergrad, you study a lot of theory. What I appreciate about this program is that you go deeper into how you apply it using new methods. The projects are industry-related, which makes it feel immediately relevant.”

His first course was through the performance-based admissions (PBA) pathway. “PBA was very interesting. From my experience so far in the program, it’s one of the hardest classes. But I understand why. It pulls all the theory into one course. It’s challenging, but it sets you up for success.”

Courses that sparked growth at work

When considering the program, certain classes stood out for Moe, not just for their academic challenge but for their direct impact on his job.

  • The Art of Data Visualization: “In my daily work I’ve been creating a lot of reporting, so that course really helped me. I learned new techniques and methods that I now use both on the job and in future projects.”
  • LLM (Large Language Models) and Their Applications: “It’s basically like learning AI. How AI tools are made and how they work. I’d been looking forward to this course since I enrolled, and I just started it recently. It’s exciting because it connects directly to the kind of machine learning work I want to do in my career.”

For Moe, the MDS has become more than a degree. It’s a bridge toward his next career move. “I’m hoping to break into roles more aligned with becoming a full data scientist or machine learning engineer. That’s part of why I chose this program.”

Flexibility that fits a busy life

Moe’s balancing act is no small feat: he works full-time and is completing the MDS full-time as well. He credits the online modality and support structure for making it possible.

“It takes a lot of tools to balance! I usually dedicate weekends to coursework. Coursera’s platform has also been very helpful to help me structure due dates and coursework.”

Live office hours and professor accessibility also stood out: “Professors and TAs run office hours, and you can meet with and email them directly. That flexibility is really helpful and appreciated since I know they are teaching classes in person as well.”

And the application process itself was refreshingly straightforward. “Almost every school out there required GREs, high GPAs, and a more intense application. With Pitt, I completed the PBA course, achieved the grade, verified my degree, and I was in. It was a smooth experience.”

Looking ahead: “The future is data”

Moe has already seen the benefits of his coursework in his current role, and he’s confident it will open doors to the next phase of his career. He also sees the MDS as a program accessible to learners from a variety of backgrounds, not just those already working in data.

As Moe put it:

“You can succeed in the program even without a background in tech or experience in data. I encourage everyone to explore that. The future is data, AI, and machine learning. It’s going to be here for a while; it’s not going anywhere. If you jump now, and you get this experience and learning, and you can apply it in your current role or future role, it’ll be very helpful. Not to mention a degree from Pitt is going to look great on your CV.”

Interested in joining learners like Moe who are leveling up their careers with a data science degree? Explore the MDS program here.

Commonality in Learning Systems – Functionality

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Commonality in Learning Systems – Functionality
  • Instructors have access to course enrollment information
  • Instructors can push surveys/assessments during active class or post-class
  • Instructors can update rosters
  • Allow instructors ability to print rosters, send e-mails to participants, and update course attendance post-class
  • Offers reports – and provides data, including what content X took, what they completed, and other learner data
  • Reports provide overall information by group, department, etc., and individual
  • Ad-hoc
  • Search functionality allows learners to find specific courses, materials, and delivery formats based on keywords, titles, delivery formats, and other tagged identifiers (Some systems limit what can be generated, so think of this more as a search capability)
  • Courses have standard attributes regardless of delivery mechanism, including title and description.
  • If the system offers “opportunities” – it is viewable on the learner side with additional information and specifics – the learner can apply. Opportunity will list requirements – this won’t stop someone from applying anyway.
  • Learners can enroll in or request enrollment for a course, content, group, etc.
  • Learners can view the entire catalog and select their courses
  • Learners can be assigned courses/content, learning paths
  • Learners can view documents and download them (Common is PDF)
  • The learner can see their status, i.e., a progress bar, percentage of completion, or whatever format the vendor has presented it in – some do use colors and expect the learner to know what they mean.
  • Learners can be assigned materials, videos, etc.
  • Create catalogs of content only for job roles tied to skills
  • Curriculum can be set by skills, interests, job roles matched to skill or skills
  • Catalog Search by content related to a specific set of skills/interests (Example: Leadership)
  • Recommends courses/content based on job role, skills
  • Skill gap analysis
  • Skills tied to opportunities (openings or, for example, specific limited opportunities such as a project manager for an upcoming project) in the company/organization – This applies only if the system offers opportunities within their platform
  • Analytics directly tied to career development and acquisition of skills for specific job roles
  • Identify by each learner the skills assigned to them AND the skill(s) they selected
  • Skill Ratings – I can tell you the majority lack one key capability, okay two (specifically what each rating means, and what are the base requirements for that skill – i.e., skill three means you have achieved a level of blah, can do blah blah, blah; and have done blah blah) – I mean what does a two mean without such information? Think this way – I am training you at an ice cream store. What does one mean? At two, you should know how to scoop ice cream and talk to a human across the other side of the counter.

Oh, and

Are You a Fan of Anime?

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Once a niche, countercultural art form, anime is now mainstream. What are your experiences with anime, and what do you think of its explosive popularity?

Word of the Day: montage

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This word has appeared in 190 articles on NYTimes.com in the past year. Can you use it in a sentence?

Teach Taxicab Geometry With Steven Strogatz and The New York Times

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Welcome students to a world of math where pi equals 4 and circles aren’t round.

Are Youth Sports Getting Too Expensive?

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Organized sports for young people have become a multibillion-dollar industry in America. How is this affecting high school athletes’ experiences and access to play?

Braces

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Metal braces are suddenly cool. What do you think of the trend?

Word of the Day: comatose

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This word has appeared in 31 articles on NYTimes.com in the past year. Can you use it in a sentence?

Weekly Student News Quiz: Charlie Kirk, Test Scores, Mars Rock

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Weekly Student News Quiz: Charlie Kirk, Test Scores, Mars Rock

Above is an image related to one of the news stories we followed this past week. Do you know what it shows? At the bottom of this quiz, you’ll find the answer.

Have you been paying attention to current events recently? See how many of these 10 questions you can get right.

The Future of Learning: Identifying Disruptors

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The Future of Learning: Identifying Disruptors
  • ExpertusOne – original incarnation. First vendor to have geolocation in their mobile app – even today, you find systems that lack that, heck even trade show apps – I mean, how can I find the Cornerstone booth in section 309, when I have no idea what section I’m in, because your signage is awful – attend a LTUK show – and you will know first hand.
  • Litmos – Original version – not the version you see today. Strong UI/UX out of the gate and proof that you could be streamlined yet very effective in understanding your audience.
  • Cobent – (RIP) – 100% all compliance platform. Disruptor – they couldn’t adapt enough for compliance
  • Training Orchestra – Training management system – it is all about even management and scheduling here – you want the best? Here you go.
  • Growth Engineering – Truly a gamification platform that understands what is achievable by disruption. Sadly, they couldn’t sustain. Still playing catch-up.
  • Blue Volt – First gamified platform – and first to really target blue-collar workers.
  • Degreed – The behemoth of LXP. I’d add EdCast here too – both disruptors early.
  • Xyleme – an LCMS that was worth its weight. Disruptor – just failed to understand the market (although one vendor, not X here, thinks LCMS is back – no, it’s not)
  • Type of system (LMS – Learning Management System, LXP – Learning Experience Platform, KM – Knowledge Management Platform/System)
  • PR – Product Review – Coming by Nov 25. The specific month is shown.
  • Answer Engine – it drives the entire platform. The AE is using Gen AI – the basics are that the learner asks a question, and a response is presented. The learner can click text or a word, and it goes right to that article, content, or video that exists within the platform. Includes cites.