First, we list the predictions from our data scientist in residence. Then we provide predictions from leading data scientists. We invite you to add your own predictions in the comments section below.
Predictions from our data scientist in residence
Here’s what he says:
- Data science and statistical modeling will be further automated, with better black-box products
- Frontiers between data science, operations research, machine learning, artificial intelligence and statistics, will disappear
- AI will become more prominent, and referred to as deep learning in our community
- We will see more open data and open projects
- The death of the fake data scientist: if you only know basic R or SQL, earned your title spending a few hours in a data science boot camp not working on any real big data project, and if your knowledge comes from free books read by millions of people, you won’t easily find a job.
- The birth of the data scientist convert: conversely and ironically (with respect to my previous point), if you are not a data scientist, maybe a biologist or physicist, but have worked on real data, are able to code, and produced value out of data, you might get a data science job easier than ever. Read learning data science on the job for details.
- More telecommuting for data scientists and related jobs, as it is difficult to attract great candidates to fill job vacancies for this type of skill set. Unless if driverless cars become the norm: then employees will be able to work when commuting.
- More self-funded data science entrepreneurs and consultants, fewer VC-funded data science entrepreneurs as consolidation takes place, and unprofitable / unsustainable data science businesses vanish.
- More women and minorities.
- Automated digital publishing backed by data science algorithms, putting more pressure on traditional publishing business models, and replacing Editors by software.
- Better use of data and data science by the government to further track people and detect fraud and terrorists.
- Explosion of sensor data (IoT).
- More API’s and Apps sharing data between devices and systems. Monetization of data arising from these systems.
- More data science applied to environmental issues. Both on Earth and beyond (predicting solar flares, discovering new asteroids and so on.)
Predictions from leading data scientists
- Bernard Marr (Bestselling author): I see particular growth in real-time data analytics and increasing use of machine-learning algorithms.
- Kirk Borne (Principal data scientist at Booze Allen Hamilton): the world of big data will focus more on smart data, regardless of size.
- Gregory Piatetsky-Shapiro (Founder of KDDnugets): 2016 will be the year of deep learning. Data will move from experimental to deployed technology in image recognition, language understanding, and exceed human performance in many areas
- Paul Zikopoulos (VP of Analytics at IBM): I would say data science for the masses is one and the other is more disruption with open source technologies to the point that no one knows what Hadoop means anymore
- Scott Gnau (CTO, Hortonworks): Next year businesses will look at deriving value from ALL data.
This is an extract from a much bigger list. Read the full list here.
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Additional Reading
- The 10 Best Books to Read Now on IoT
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 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
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