Data scientists and analysts who specialize in predictive analytics (PA) are in high demand these days, but huge expectations often accompany the role. The media hype around machine learning and artificial intelligence has led some executives to expect PA to continuously spin mountains of data into golden insights.
Whether you are a predictive analytics practitioner or a professional in IT operations, security, or software development and testing, you need ongoing professional development to keep up with the demands of your job—and the PA capabilities in the tools you use.
TechBeacon has curated this list of key organizations, events, and certifications for PA to help you get on target with your career—and stay there.
Organizations and web portals
There are two kinds of organizations and web portals that provide content about PA: those that promote scholarly research, and those that promote real-world, practical applications. Depending on your professional role and career path, you will probably gravitate to the camp that fits you best and stay there.
Still, it might be wise to sample the offerings on your nondominant side from time to time just to keep up with the new developments in this space.
The sites below are listed alphabetically.
Association for the Advancement of Artificial Intelligence
The Association for the Advancement of Artificial Intelligence (AAAI), formerly the American Association for Artificial Intelligence, was founded in 1979 as a scientific nonprofit. It is dedicated to advancing the study of thought, intelligence, and human behavior, and how these can be expressed or embodied in machines. It continues today with that same mission, although clearly the world of artificial intelligence has evolved dramatically.
Part of the AAAI's mission is to increase public understanding of AI in all its various manifestations. To that end, the website provides a digital library of its conference proceedings and technical reports, as well as its journal and magazine articles. You will find this content more academic, scholarly, and scientific than that found on many of the other sites on this list.
The website offers curated content in an area called AITopics, which it claims is "the Internet's largest collection of information about the research, the people, and the applications of artificial intelligence." Fittingly, the engine for AITopics uses machine learning to do a lot of the heavy lifting for the content it presents.
The AAAI also sponsors a major annual event and a symposia series. (Learn more below, under "Events.")
Fee: None
insideBIGDATA
insideBIGDATA is a portal that aggregates news, product information, and articles relevant to the practical side of data science, big data, artificial intelligence, machine learning, and deep learning. It targets data scientists, business executives, and IT professionals with its content, so you will find an interesting mix of predominately vendor-sponsored content here.
The site offers a large section devoted to machine learning; it's a useful aggregator of the newest offerings and ideas from vendors in the predictive analytics space.
One unique offering from this site is its interviews with CEOs and other top executives from some of the vendors. You will also find a job board powered by Monster that surfaces big data jobs.
Fee: None
PAT Research | Predictive Analytics Today
PAT Research is a B2B directory of enterprise software and services that was founded in 2013 and now includes more than 5,000 products and 100,000 members. Although the site lists a great many more categories than predictive analytics, its excellent filtering makes it easy to compare all of the offerings in the predictive space, with nicely granular categorizations.
You can compare products in categories such as predictive analytics API, predictive analytics software, free predictive analytics software, predictive lead scoring software, predictive maintenance software, and predictive pricing software.
Each product gets an editor's review for ease of use, features, etc., and there is a proprietary algorithm called a "PAT rating" that creates a base for standardization. A PAT index score calculates the popularity of the product based on community reviews, likes, ratings, etc., augmented with social media signals.
This is all factored into the PAT grid, which scores the top 20-plus products for each category and provides an easy visualization of the vendor landscape in the areas you care most about.
Free login is required to view all reviews and comparisons.
Fee: None
Predictive Analytics Times
The Predictive Analytics Times is a content portal devoted to the interests of predictive analytics practitioners. It features content including original articles, a few gated webinars, and videos from its Predictive Analytics World (PAW) conferences (learn more below under "Events"), and more.
Its industry news section has an interesting mix of content that includes tidbits from Quora, Data Science Central, Elie.net, and many other sources that are off the beaten path. The site appears to be relatively new, judging by the limited content in the community and the short supply of webinars and white papers, but the articles and curated industry news make this site worth a look.
Free login required to post a comment.
Fee: None
Events
You really need to get away from the office and attend events regularly if you want to keep up on the newest learnings, trends, and tech. It is the single most efficient way to broaden your network and deepen your knowledge.
As with the organizations listed above, the events offered in this space are designed to appeal to either a very scholarly, scientific audience or an audience looking for practical, real-world guidance.
Here are some events—some very new, and others fairly well-established—where you can immerse yourself for a few days in predictive analytics technologies, new research, and emerging practices, and meet the experts you should pay attention to. They are listed in alphabetical order.
AAAI Conference on Artificial Intelligence (AAAI-19) | Association for the Advancement of Artificial Intelligence
Now in its 33rd year, the AAAI conference promotes research in artificial intelligence and scientific exchange among AI researchers, practitioners, scientists, and engineers from all of the related disciplines.
Unlike some of the events on this list, AAAI-19 is not focused entirely on machine learning or PA. But it's included because it always offers a track of sessions, workshops, and tutorials relevant to PA.
AAAI-19 will have a diverse technical track, student abstracts, poster sessions, invited speakers, tutorials, workshops, and exhibit and competition programs, all "selected according to the highest reviewing standards," according to the AAAI.
Date: January 27–February 1, 2019
Location: Honolulu, Hawaii
Fee: Not yet available (the fee for the 2018 event was $925 for a regular pass)
AAAI Fall Symposia | Association for the Advancement of Artificial Intelligence
In addition to its large annual conference listed above, the AAAI holds smaller, more intimate events in the spring and fall that are typically attended by fewer than 100 people. The AAAI Fall Symposia offers a choice of eight symposia; attendees choose a single symposium to attend throughout the event.
This is a good option if you find a topic that is interesting to you, because it's an opportunity to dive deep. These symposia are scholarly, technical events. But not all of them relate to PA.
Fall 2018 symposium titles include "Adversary-Aware Learning Techniques and Trends in Cybersecurity," "Artificial Intelligence in Government and Public Sector," and "Interactive Learning in Artificial Intelligence for Human-Robot Interaction."
Date: October 18–20, 2018
Location: The Westin Arlington Gateway, Arlington, Virginia
Fee: $560 (Discounts available for AAAI members and students)
NOTE: The AAAI Spring Symposia will be held March 25-27, 2019, in Palo Alto, California. The symposium titles for that event had not been finalized at the time of this writing.
AISTATS 2019 | 22nd International Conference on Artificial Intelligence and Statistics
Launched in 1985, AISTATS offers a blend of content from researchers from the disciplines of artificial intelligence, machine learning, statistics, and related areas. All of the papers presented at the conference are selected in "an academically rigorous double-blind, peer-review process," according to the site.
Many experts recommend this event for its high quality of papers, and they consider it one of the most important small conferences for the machine-learning community. Because of its small size, you will not have as many opportunities to network here as you will at the bigger events, but it is regarded as a top source of new research and innovative thought.
Date: April 16-18, 2019
Location: Naha, Okinawa, Japan
Fee: Not yet available
Conference on Neural Information Processing Systems
The Annual Conference on Neural Information Processing Systems (NIPS) is now in its 32nd year. It is a multi-track machine-learning and computational neuroscience conference that features talks, demonstrations, symposia, and oral and poster presentations of refereed papers.
After the conference sessions, there are more informal workshops you can attend, exploring such things as "Challenges and Opportunities for AI in Financial Services," "Machine Learning on the Phone and other Consumer Devices," and "Reinforcement and Language Learning in Text-Based Games."
The conference will also offer the second NIPS competition track, where you will see the eight top-scored competitions run and present their results.
Yisong Yue, a machine-learning professor at Caltech, listed NIPS as one of the top three machine-learning conferences, and his Quora colleagues agree. He wrote: "They are the largest by attendance, attract researchers from across virtually all areas of machine learning, and have high visibility in industry and other computational fields."
Date: December 3–8, 2018
Location: Palais des Congrès de Montréal, Montreal, Québec, Canada
Fee: Not yet available (registration opens September 4, 2018)
Data Summit 2019
The Data Summit conference is a fairly new event, dating back to 2014, when it was held in New York. The event is targeted at chief information officers, chief data officers, IT directors and managers, data architects, data analysts, and data scientists, as well as software engineers and developers.
The 2019 details haven’t been released yet, but if the conference follows the 2018 model, it will be organized by a number of tracks that will appeal to different interests and specialties. The 2018 tracks included things such as "Moving to a Modern Data Architecture," "Competing on Analytics," and "Cloud Day."
Date: May 21–22, 2019
Location: The Hyatt Regency, Boston, Massachusetts
Fee: Not yet available
Deep Learning World Berlin
Deep Learning World Berlin marks the premier of a new one-day event that focuses on the commercial application of deep learning methods and technologies. It runs one day before Predictive Analytics World for Business Berlin (see below for more information), and is held at the same venue.
Deep Learning World will explore the challenges that businesses are beginning to experience as they incorporate deep learning and artificial intelligence in various ways. Sessions will focus on topics such as how to detect machine defects with cognitive sensors, and reinforcement learning, which has potential application in robotics, self-driving cars, and gaming.
If you are already planning to attend Predictive World for Business Berlin, it might be a good idea to add this extra event to your plans.
Date: November 12, 2018
Location: Estrel Hotel Berlin, Germany
Fees:
- Before September 28, 2018: €695
- After September 28, 2018: €895
- Group discounts available
EmTech 2018
EmTech is sponsored by the MIT Technology Review (published by the Massachusetts Institute of Technology). This event presents speakers from both academia and the corporate world addressing emerging technologies that are driving the global economy.
Although EmTech is not exclusively focused on data science, AI, and predictive analytics, it's included here because this year two of the six themes of the conference are relevant to AI:
- The Democratization of AI: Technical advances are making it possible for non-experts to apply AI in their work, accelerating the pace at which new AI solutions are deployed. The pace of automation that this technology is fueling will reach every corner of the global economy.
- Global View: Innovation in the AI Era: AI technologies are driving economic growth in every region. This brings discussions around ethics and governance to the forefront, as we work to ensure that the next wave of innovation will benefit us all.
Date: September 11–14, 2018
Location: MIT Media Lab, Cambridge, Massachusetts
Fees:
- $2,595 for general admission
- $2,995 for a premium pass (includes half-day learning session on September 14 and other benefits)
- Discounts available for MIT alumni, groups of three or more, and other groups
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases dates back more than 16 years.
One keynote speaker will be Misha Bilenko, who heads the machine intelligence and research division at Yandex in Moscow and previously led the machine-learning algorithms team at Microsoft Research. In another keynote address, Corinna Cortes, head of Google Research in New York, will speak about her machine-learning work at Google.
Several tutorials and workshops will provide the opportunity to explore things such as "IoT Large Scale Machine Learning from Data Streams" and "Machine Learning and Data Mining for Sports Analytics."
Date: September 10–14, 2018
Location: Croke Park Conference Centre, Dublin, Ireland
Fee: €725
International Conference on Machine Learning
The 36th International Conference on Machine Learning (ICML 2019) is sponsored by the International Machine Learning Society, a nonprofit that fosters machine-learning research. This is considered a top conference by machine-learning experts. Yisong Yue, a machine-learning professor at Caltech, listed ICML as one of the top three machine-learning conferences, and his Quora colleagues agree. He wrote: "They are the largest by attendance, attract researchers from across virtually all areas of machine learning, and have high visibility in industry and other computational fields."
Date: June 10–15, 2019
Location: Long Beach, California
Fee: Not yet available
KDD 2018 | Knowledge Discovery and Data Mining
KDD 2018 (Knowledge Discovery and Data Mining) focuses on large-scale data analytics, big data, data mining, and the practical application of data science to benefit society. It offers 44 conventional tutorials and 8 hands-on tutorials, workshops, a project showcase, and much more.
Unique to this event is the first full Deep Learning Day, specifically devoted to this field. Conference organizers are supplying speakers from several research institutions and offering tutorials and a panel debate.
The KDD conference is considered one of the top conferences by machine-learning experts. Yisong Yue, a machine-learning professor at Caltech, listed KDD as one of the top three machine-learning conferences, and his Quora colleagues agree. He wrote: "Compared to ICML and NIPS, KDD is a bit more focused on new applications and less focused on basic methodology—but many people consider KDD to be the more well-rounded machine learning conference."
Date: August 19–23, 2018
Location: ExCel, London, UK
Fee: $1,380 (discounts available for SIGKDD members)
Machine Learning for DevOps Summit
Machine Learning for DevOps Summit focuses on how to use machine learning in DevOps practices, with sessions for IT managers, developers, machine learning practitioners, and DevOps managers built around real-world applications. The event offers plenty of networking opportunities and is more of a hands-on, interactive event than some on this list. For example, it includes group brainstorming sessions, interactive workshops, and several networking activities throughout the event.
Sessions explore troubleshooting and triage analytics, present case studies showing how machine learning can play a role in managing production and alert storms, and share architectural best practices. If you are experienced in DevOps but new to predictive analytics, this event is a good one for you, with workshops with titles such as "New to AI and Machine Learning? Time to ask Q’s!" and a panel discussion about how you can minimize the risks of AI in the enterprise.
Date: November 29-30, 2018
Location: JW Marriott Houston Downtown
Fee: $1,495. Before October 5, 2018: $1,095
MLConf San Francisco | Machine Learning Conference
MLconf is a one-day, single-track event devoted exclusively to machine learning. Experts from Google Brain, Uber, Facebook, Tesla, and Baidu will discuss machine-learning algorithms and techniques.
Sessions will include things such as novel applications of machine learning in cancer detection, geospatial data, and more.
Date: November 14, 2018
Location: Hotel Nikko, San Francisco, California
Fee: $250
PAPIs.io | Real-world Machine Learning Stories
PAPIs 2018 is an event singularly focused on the real-world application of machine learning. In contrast with the scholarly events that present the findings of academic researchers, this event is all about how practitioners are actually using machine learning.
For example, a data scientist from Salesforce will discuss some of the challenges the company faced when using automated machine-learning pipelines and how it solved them. Another session will explore the limitations of crowd-sourced human-tagged data and why you should consider the social impact of outsourcing this work. And in another session, a data scientist from Lincoln Financial Group will explain how she gets stakeholder approval and resources for machine-learning projects.
Date: October 15–17, 2018
Location: Microsoft New England Research and Development Center, Cambridge, Massachusetts
Fee: $170
Predictive Analytics Innovation Summit | Innovation Enterprise Summits
The Predictive Analytics Summit series takes place in major cities around the world. These two-day events present a mix of deep-learning and AI best practices, case studies from data analysts at Fortune 500 companies, and exposure to new tools and technologies in the predictive analytics space. Most attendees are director level or above.
Upcoming locations:
Date: October 10–11, 2018
Location: ParkRoyal on Beach Road, Singapore
Fees:
- Gold Pass: $1,295
- Silver Pass: $995
Date: October 30–31, 2018
Location: Sheraton Grand, Chicago, Illinois
Fees:
- Early Bird Diamond Pass: $2,095
- Early Bird Gold Pass: $1,795
- Early Bird Silver Pass: $1,495 (no information available about when early-bird pricing ends, or what full pricing will cost)
Date: February 6–7, 2019
Location: San Diego, California
Fees:
- Early Bird Diamond Pass: $2,095
- Early Bird Gold Pass: $1,795
- Early Bird Silver Pass: $1,495 (no information available about when early-bird pricing ends, or what full pricing will cost)
Date: March 20–21, 2019
Location: London, UK
Fees:
- Early Bird Diamond Pass: £1,295
- Early Bird Gold Pass: £1,095
- Early Bird Silver Pass: £895 (no information available about when early-bird pricing ends, or what full pricing will cost)
Predictive Analytics World | MEGA-PAW
Predictive Analytics World (MEGA-PAW) is a good event to attend if your interests span multiple industries. You can participate in seven parallel tracks and cross-register for up to five conferences that are held during this event: Predictive Analytics World: Business, Predictive Analytics World: Financial, Predictive Analytics World: Healthcare, Predictive Analytics World: Manufacturing, and Deep Learning World.
MEGA-PAW emphasizes real-world applications of machine learning. For example, if you attend PAW Healthcare, you'll learn about the impact of predictive analytics on patient satisfaction, costs, and improved outcomes. In the PAW Business sessions, you'll hear how Fortune 500 companies use machine learning to achieve business results.
Date: June 16–20, 2019
Location: Caesars Palace, Las Vegas, Nevada
Fee: Not yet available (Pricing for 2018 was $4,750 for a five-day mega pass. Several pricing packages were available with different combinations of days, events, and workshops. Group discounts were available.)
Predictive Analytics World: Business Berlin
Predictive Analytics World: Business Berlin is built around the theme of "The Awakening of Artificial Intelligence." Sessions will explore the practical applications of machine learning, using case studies to show how predictive analytics has led to specific business results. Sample session topics include "White vs Black Box: Visualization and Interpretation of Predictive Models," and "Predictive Modeling in Detail: Feature Engineering and Model Evaluation."
Date: November 13–14, 2018
Location: Estrel Hotel Berlin, Germany
Fees:
- Before September 28, 2018: €1,295
- After September 28, 2018: €1,495
- Pre- and post-event workshops are also available for an additional cost. Many pricing packages are available, with different combinations of days and workshops. Group discounts are available.
Predictive Analytics World: Business London
Predictive Analytics World: Business London is a two-day event focused on the practical application of machine learning in real-world businesses. Sessions will cover topics such as "Applied Deep Learning: Self-Driving Cars and Fake News Detection," "Explainable AI in Retail and Marketing," and "Analyzing Ads Effectiveness with Machine Learning."
Date: October 17–18, 2018
Location: London, UK
Fee: £1,745 (combo pass with workshop)
Many pricing packages are available, with different combinations of days and workshops. Group discounts are available.
Predictive Analytics World: Government | PAW Government
Predictive Analytics World: Government is a two-day conference for predictive analytics practitioners who work in the public sector. Pre- and post-conference workshops are offered in addition to the actual conference sessions.
The pre-conference workshop looks like a good option if you can swing the extra day away from the office. It is a practical survey of the standard and advanced methods available today for predictive modeling—comparing their merits, demonstrating their performance, and explaining how to choose the right method and tool for a project. Two post-conference workshops are also offered that will let you get added value from your travel spend.
The actual conference agenda offers a variety of topics, panel discussions, and case studies, and you can choose management-level or technical-level sessions to attend. Sample session topics include "Anomaly Detection and Unsupervised Techniques for Fraud and Risk Detection" (technical session), and" The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement" (management session).
Date: September 17–21, 2018
Location: Washington, DC
Fees:
- $2,695: Five-day pass for government employee
- $4,395: Five-day pass for private industry/contractor
Many pricing packages are available, with different combinations of days and workshops. Group discounts are available.
Strata Data Conference
Strata Data Conference is a three-day conference that presents sessions and learning tracks that explore a wide range of big data technologies. It offers content focused on artificial intelligence and machine learning, and how to create and implement your data strategies.
In contrast to the more scholarly events, this one is tailored to business decision makers, strategists, architects, developers and analysts, and presents the real-business application of the science. Sample session topics include "DIY vs Designer Approaches to Deploying Data Center Infrastructure for Machine Learning and Analytics," and "Your 5 Billion Rides Are Arriving Now: Scaling Apache Spark for Data Pipelines and Intelligent Systems at Uber."
Training courses are available on Day 1.
Date: September 11–13, 2018
Location: Javits Center, New York, New York
Fee: Four conference pass levels ranging from $1,795 to $2,795
NOTE: This event will also be held in San Francisco in March 2019 and London in April 2019.
Certifications
There are not as many professional certifications available for the predictive analytics practitioner as there are for other specialties. Here are some options to consider.
Certified Specialist in Predictive Analytics | CAS Institute
The requirements for earning the Certified Specialist in Predictive Analytics (CSPA) credential include passing three exams, completing a project, and completing an ethics course. Eligibility requirements may allow for experience such as academic degrees, professional technical papers, and other evidence of practical knowledge.
This certification is offered by the Casualty Actuarial Society Institute (iCAS), which provides specialty credentialing and education to quantitative specialists. It primarily serves professionals who work in the insurance and risk management sectors. The iCAS credentials are particularly useful if you want to demonstrate your expertise in things such as catastrophe model analytics.
Fee: Online courses are offered to prepare for the exams. Class fees range from $795 to $1,245, depending on whether you want to purchase the additional study aids.
Predictive Analytics Certificate Program | Caltech
The Predictive Analytics Certificate Program offered by Caltech at its Pasadena, California, campus is designed for working professionals who are responsible for optimizing business performance in high-tech companies. This program is designed for people working in business intelligence, data mining and warehousing, product development, marketing, and service delivery.
The program teaches the predictive analytics skills needed to uncover patterns and trends in financial data, marketing data, etc., and quantitatively make business decisions derived from the data.
You are taught in an accelerated, hands-on, five-day format, and the goal is for you to apply the concepts you learn to the business problems you actually face in your job.
Upcoming dates:
- September 8, 2018
- September 22, 2018
Location: Caltech campus, Pasadena, California
Fee: $2,600
SAS Certified Data Scientist Using SAS 9
The SAS Certified Data Scientist Using SAS 9 credential is offered by software vendor SAS. It certifies that a person has the ability to manipulate and gain insights from big data using SAS and open-source tools. It also certifies that the practitioner can make sound business recommendations with complex learning models, and deploy models at scale in a SAS environment.
The certification requires passing five exams:
- SAS Big Data Preparation, Statistics and Visual Exploration
- SAS Big Data Programming and Loading
- Predictive Modeling Using SAS Enterprise Miner 7, 13, or 14
- SAS Advanced Predictive Modeling
- SAS Text Analytics, Time Series, Experimentation, and Optimization
Fee: $180 for each exam; $250 for Predictive Modeling Using SAS Enterprise Miner
Courses: SAS recommends candidates prepare using its SAS Data Science curriculum from SAS Academy for Data Science. It costs $4,400 for a self-paced e-learning course, or $16,000 for a six-week instructor-led course in Cary, North Carolina.
University advanced degrees and specialty certificates
Many universities offer advanced degrees in data science and predictive analytics, which you may want to consider as you plan your career. Review this list by Predictive Analytics Today of the Top 27 Masters of Data Science Schools in 2018 to explore some of the highest-ranked options.
There are some university options that don’t require as big a commitment as a master's program. Here are two university certificates designed for working professionals.
Cornell
The data analytics certificate from Cornell University requires three courses that are taught online. This is a flexible way to get a credential from an Ivy League school relatively quickly. This certificate is designed to provide functional literacy in critical business analytics.
It is targeted to people who need a deeper understanding of how to perform the statistical analyses that support essential business decisions. The courses are three weeks long.
Fee: $3,600 (covers all three courses)
Time to complete: 2.5 months (minimum)
UC Irvine
UC Irvine offers a predictive analytics certificate through its continuing education program. It takes a year or two to complete, depending on how much time you dedicate to it, and is taught online. The certificate is awarded after candidates complete five required courses and six elective units.
This program is geared to people who may already be using predictive analytics in some aspects and want to round out and formalize their learning.
Fee: $6,625 (average cost of program)
Time to complete: 9 to 24 months
Scholarly journals
Below are some open-access sources of scholarly research that share the advances and discoveries being made at the cutting edge of this discipline. There are other such repositories that are fee-based, but the ones below have made all their content freely accessible to anyone who wants to read them.
Even if you are a PA practitioner with no desire to do academic research, it is interesting to keep an eye on the work that is being done in this space. The things you see may eventually work their way into real-world applications that could end up changing the game in some important ways.
The Journal of Artificial Intelligence Research
The Journal of Artificial Intelligence Research (JAIR) was established in 1993 as one of the first scientific journals on the web. It covers all areas of AI, publishing refereed research articles, survey articles, and technical notes. JAIR invites submissions in all areas of AI. Articles are published in JAIR based on originality and significance of the contribution.
The Journal of Machine Learning Research
The Journal of Machine Learning Research (JMLR) publishes scholarly articles in all areas of machine learning. JMLR invites submissions of unpublished papers about things such as new principled algorithms, experimental and theoretical studies that yield new insights, new analytical frameworks, computational models of data from natural learning systems, and surveys of existing work.
Take yourself to the next level
Recent studies confirm what you probably already know: Predictive analytics is a smart career choice. People with machine-learning skills are commanding an average salary well into six figures and are in high demand in the marketplace. Businesses are competing for top talent, and CIOs are trying to win the recruitment war for machine-learning talent.
The resources listed above can help you position yourself for success in this hot job market. You should attend events every year to watch how others are using PA to solve real-world problems, and read case studies, conference reports, and research papers to keep up with new practices and learnings.
Most importantly, build a professional network that you can turn to throughout your career, and be sure to give back generously by sharing your own discoveries. As you do this you will stay relevant, build your skills, and establish a reputation that will let you take your career whatever direction you want it to go.
Do you have other favorites? Add yours in the comments below.
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