Learn how to differentiate the key roles that are available within the field.
- There are a number of opportunities…you can take advantage of to play an active role…and contribute to data science and analytics fields.…To name just a few, there are job titles…such as data scientist, data engineer,…business intelligence architect,…machine learning specialist, data analytics specialist,…and data visualization developer.…Each of these roles are critical in effectively leveraging…data and its potential despite numerous challenges.…
For example, big data requires special processing…by data engineers before an analytics specialist…can even try to do their job.…Take network security.…Let's assume that you need to analyze…a terabyte of data every day.…The goal here is detecting suspicious behavior.…There are numerous roles involved in this…including domain experts,…such as cyber security professionals,…data base administrators, cloud…and distributed computing specialists,…network engineers, software engineers,…and last but not least, data scientists.…
The list goes on and on.…In fact, you can see this in action in my recent course…
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and its subfields, explores the marketplaces for these fields, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the six leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why data analytics is important
- How data science is used in fraud detection, disease control, network security, and other fields
- Data science skills
- Data science roles
- Data science certifications
- The future of data science
Skill Level Beginner
Insights on Data Science: Lillian Piersonwith Lillian Pierson, P.E.23m 51s Intermediate
Learning Data Science: Understanding the Basicswith Doug Rose1h 16m Appropriate for all
1. Define Data Science
6. Future of Data Science
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.