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…
Author
Jungwoo Ryoo
Released
1/26/2018Jungwoo 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
Duration
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Introduction
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Welcome1m 9s
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1. Define Data Science
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Introduction1m 24s
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A brief history2m 37s
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Fundamentals3m 15s
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Big data analytics1m 44s
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Enabling technologies2m 51s
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2. Marketplace
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Introduction to marketplace1m 26s
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Fraud detection2m 5s
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Social media analytics2m 9s
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Disease control1m 24s
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Dating services1m 50s
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Simulations1m 28s
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Climate research1m 24s
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Network security1m 16s
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3. Skills
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Required skills2m 42s
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Data mining and analytics1m 49s
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Machine learning1m 33s
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Statistics1m 10s
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Visualization1m 35s
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4. Roles
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Introduction to roles1m 49s
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Data scientist or engineer1m 48s
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Data visualization developer2m 26s
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Salaries1m 32s
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5. Certifications
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6. Future of Data Science
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Emerging technologies1m 44s
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Emerging careers1m 34s
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Ethics1m 51s
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Professional development1m 45s
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Conclusion
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Video: Introduction to roles