What’s Hot & What’s Not in Data Science 2015
Interesting infographics from CrowdFlower. In the hot category, I would add data plumbing, sensor data to better predict Earthquakes, weather or solar flares, predictive analytics for flu and other health or environmental issues, automating data science and man-made statistical analyses, pricing optimization for medical procedures, customized drugs, car traffic optimization via sensor data, properly trained data scientists involved in decision and replacing business analysts, the death of the data silo.
DSC Resources
- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Pure Data Science | Competitions and Challenges | Webinars | Our Book
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
Additional Reading
- 4 easy steps to becoming a data scientist
- 13 New Trends in Big Data and Data Science
- 22 tips for better data science
- Data Science Compared to 16 Analytic Disciplines
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- 10 types of data scientists
- 66 job interview questions for data scientists
- High versus low-level data science
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge
Comment
-
Comment by Cari Jaquet on January 6, 2015 at 9:06pm
-
What's hot: Smart Data Prepster. What's not hot: Data Janitors. All too often, data analysts spend more of their time preparing data: cleaning it, pushing it around (wrangling, blending, etc.), leaving no time for the high value analytics work they need to do. 2015 delivers self-service, adaptive data prep solutions (like Paxata) built from the ground up to help even non-technical analysts aggregate, clean, shape, and organize data - in a single platform that offers a single collection point regardless of where data comes from, drives re-usability of AnswerSets, enforces real-world data governance practices, and allows for enterprise-wide collaboration every step of the way.
-
Comment by umamahesh on January 3, 2015 at 4:00am
-
valuable info
Resources
Videos
© 2019 Data Science Central ®
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
You need to be a member of Data Science Central to add comments!
Join Data Science Central