Terms like "beginner" and "expert" mean different things to different people, so we use personas help clarify a course's audience. When designing a specific course, you should explain how it will or won't help these people, and what extra skills or prerequisite knowledge you are assuming your students have above and beyond what's included in the persona.

Note: these personas mention some specific technologies or domains, such as Python or epidemiology. When using these personas, replace these by equivalent levels of knowledge about technologies or domains appropriate to your course, e.g., novice understanding of R but advanced understanding of medical imaging (or vice versa).

Unaware Umberto

  • Age: 25
  • Location: London, UK
  • Education: Bachelor in Economic History - King's College London
  • Personal: Partially deaf, so prefers materials with closed captioning.

Previous Job

  • Financial advisor at Lloyds Banking Group.

Current Job

  • Entry-level financial adviser at KPMG.

Data Science Education Experience

Umberto's degree gave him an excellent grounding in economic theory, but the only quantitative work he did was with very small datasets using Excel and a little bit of copy-and-paste in SPSS. He has worked with complex spreadsheets, and some software packages developed in-house, but he hasn't yet had much exposure to R, Python, SQL, or other stars in the data science constellation, and is only vaguely aware of how they could help him. However, Umberto is continually looking for easier and more efficient ways of doing his job tasks-at-hand to move up in his demanding career.

DataCamp Experience

Umberto doesn't know DataCamp.

Career Goal

Aware that his background hasn't given him the same quantitative skills as many of his peers, Umberto is set on learning as much as possible as quickly as he can. He loves on-the-job training and wants to build knowledge so he can grow into a project lead role.

Building analytical reasonings and spreadsheet models from scratch is what I like and spend most of my time at work doing.

Is Uncertain About…

  • Doing things correctly and efficiently.
  • Growing as fast as his colleagues.

Starting Sindhu

  • Age: 28
  • Location: Bangalore, Karnataka, India
  • Education: Bachelor in Biochemistry, Master in Medicinal and Pharmaceutical Chemistry - University of Mumbai
  • Personal: Likes things she can do on the move (e.g., podcasts).

Previous Jobs

  • Chemist at waste recycling company.

Current Job

  • Scientist at a pharmaceuticals company.

Career Goal

Sindhu works in a team of scientists researching the effectiveness of vaccines. She invests a lot of time in the lab, and in summarizing and reporting her results to her superiors. She is constantly looking for better ways of doing so and hopes to one day become the principal scientist on her team.

Data Science Education Experience

Sindhu came out of university with expert-level knowledge of immunology, but only knows basic statistical concepts and techniques. However, needing to visualize a lot of her findings, Sindhu quickly found that nor Excel and JMP - the tools used in her team - nor SPSS - the software used in university - were as customizable as she would have liked. Moreover, she lost a lot of time building her model from scratch each time. A quick survey among research scientist friends led her to R and DataCamp. After two free courses and the relief that it was actually doable, Sindhu subscribed.

DataCamp Experience

Expecting a steep and long learning curve, Sindhu was pleasantly surprised to see how fast DataCamp made her put her hands on the keyboard to code herself. These quick exercises and immediate feedback still are what keeps Sindhu motivated and thinking she can actually do this. The tracks and the fact that DataCamp is well curated and has things in one place are a big plus for her as well. She misses being able to easily export the code so she can keep it with the slides.

I was surprised to see how you had to code from the get-go. As a chemist with no data science background, this made all the difference in giving me confidence.

Is Uncertain About…

  • Whether she'll be able to get it (steep and long learning curve of data science).
  • Where to start (which language and which skills or topics).

Coder Chen

  • Age: 42
  • Location: Calgary, Alberta, Canada
  • Education: Bachelor of Computer Science - University of Saskatchewan
  • Personal: Highly motivated by the social side of learning.

Previous Jobs

  • E-commerce application developer.
  • Software project manager.
  • Co-founder of software development consultancy (acquired by farm machinery firm).

Current Job

  • VP of software development, Lewis Agricultural.

Career Goal

Chen is responsible for a group of 30 programmers developing software for next-generation farm machinery. Her team is now responsible for developing applications that will help customers analyze and understand the large volumes of data being collected by sensors placed in the field and on their equipment.

Data Science Education Experience

Chen has a strong programming background but has never taken a statistics course, and her data visualization experience is limited to creating budget reports for management in Excel. Knowing that she needed to learn some basic statistics to do her job, she asked one of the company's financial team to recommend some online courses. This led her to DataCamp, and after working through a handful of courses on R, she has now purchased six seats for her team members to use as well.

DataCamp Experience

Chen was originally worried that her lack of mathematical background would make it hard for her to work through courses in the time she had. She was quickly reassured by the way that lessons move in small, digestible steps, and by the way that the interactive interface let her tinker with the code as she went along.

I originally started this because my job required it, but I discovered that I was enjoying learning something new. I particularly like the discussion on the Slack channel with people doing so many different things.

Is Uncertain About…

  • Whether she has the necessary mathematics background (and whether she can fill in the gaps in the time she can afford to invest).

Mathematical Marta

  • Age: 28
  • Location: Prague, Czech Republic
  • Education: PhD in Mathematics - Charles University
  • Personal: Retraining after deciding not to pursue an academic career.

Previous Jobs

  • PhD student in pure mathematics.
  • Two summer research jobs (both doing pure math).
  • One-year post-doctoral position (also in pure math).

Current Job

  • Unemployed.

Career Goal

Marta recently completed a post-doc in non-homotopic knot theory, having done her PhD in the same subject. She has decided that she doesn't want to pursue an academic career, and wants to retrain as a data scientist: several of her friends from theoretical physics have already done so, and she thinks what they're doing is both interesting and useful. However, she hasn't done statistics since she was an undergraduate and knows next to nothing about finance, logistics, marketing, and other domains where data science is used.

Data Science Education Experience

Marta is very comfortable with mathematical abstractions and can make sense of complex theory quickly and easily, but even the one stats course she did eight years ago was theoretically oriented. She is aware that real-world data is sometimes messy, but hasn't had to deal with that messiness herself.

DataCamp Experience

Marta was initially worried that her lack of real-world experience would make it hard for her to understand the examples in DataCamp courses. She has found that is occasionally the case but has usually been able to fill in the gaps in her knowledge by browsing Wikipedia. She sometimes wishes that DataCamp courses spent more time on the mathematical underpinnings of the methods used - she is accustomed to proofs, proofs, and more proofs - but has been pleasantly surprised to find how much she enjoys programming.

I started this because I wanted to do the kind of work my friends are doing. I'm continuing with it because I find it just as challenging as the theory I used to do.

Is Uncertain About…

  • Whether she can catch up with people, who have been programming for years.

Advanced Alex

  • Age: 34
  • Location: Reno, Nevada, USA
  • Education: Bachelor in Mathematics and Master in Finance & Risk Management - University of Nevada
  • Personal: Interested in teaching courses himself some day.

Previous Jobs

  • Pricing analyst for a power distributor.
  • Financial analyst at Bank of America.

Current Job

  • Head quantitative analyst in a small hedge fund.

Career Goal

After developing his skill set and finding out what he likes in previous jobs, Alex is set on staying in this role for quite a while. He was recently made part of the management team. His main concern is driving the company forward and outpacing competitors.

Data Science Education Experience

Having touched the basics of data science in his education, Alex is currently using Python in his job. To get better and improve his business' competitive edge, he went looking for more Python skills. Google and forums led him to try edX and DataCamp. DataCamp's renowned instructors and the fact that respected institutions use it pulled Alex in. Today, he uses DataCamp as his first foundational resource and supplements it with specific, more in-depth courses on Coursera.

DataCamp Experience

The main draw for Alex is that DataCamp brings him a high-level understanding of what's out there in the data science field, on pace with the new developments. He doesn't mind shopping around for more specific and in-depth content but could see this becoming a bit annoying in the future. In any case, the business value of DataCamp for him lies in the apply fast way the learning experience is set up.

From a business perspective, I need to be able to apply the data science I learn straight away - I can't afford to spend three months learning without being able to apply it.

Is Uncertain About…

  • Such a fast moving field (focusing on the right data science subjects).
  • So many views and approaches (having the right take on certain topics)
  • Getting data science to work for him (how to apply and build business value).

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