This year can be considered the booming of Artificial Intelligence (AI). Just look at the number of startups with the term AI in their taglines; where acquisitions from big companies focused on; and the topics at the biggest tech conferences. AI is everywhere — even if just as a buzzword. But what actually is AI? That’s a rather difficult and controversial question to answer.
What is clear is that data science is solving problems. Data is everywhere, and the uses we are making out of it (science) are increasing and impacting society more and more. Let’s focus on Data Science, while other philosophize on the best definition of AI.
With the plethora of data being generated every second, and the pace being accelerated by catalysts like IoT, the issue of data security will become more and more important. It can be reasonably expected that more data regulatory schemes will follow in 2019. Data regulatory events like for example GDPR (European General Data Protection Regulation), which was enforced on May, 2018 regulated data science practice by setting certain boundaries and limits on collection and management of personal data. Such regulatory activities will hugely impact future predictive models and different analytic exercises. Moreover, the increasingly sophisticated cyberattacks have mandated the need for a less vulnerable data protection scheme. The high-profile data breaches expose our inadequacy in this aspect. So many more new protocols and procedures to secure data are likely to emerge in 2019.
The buzz created by AI is unlikely to die down in the coming year. We are in the nascent and initial stage of AI, and the following year will see the more advanced application of AI in all the fields. Harnessing AI will still remain a challenge. More intelligent apps will be developed using AI, Machine Learning and other technologies. Automated machine learning (ML) will become common and it will transform data science with better data management. There will also be the development of specific hardware for training and execution of deep learning. Incorporation of AI will enhance decision-making and improve the overall business experience. Applications and other services will increasingly rely on AI to improve the overall experience. All the new applications will incorporate some form of AI in their program to improve their functioning. So, the number of intelligent apps will be on the rise. Intelligent things that are smarter versions of regular gadgets will continue to flood the market.
Digital representations of real-life physical objects powered by AI capabilities will become widespread. These technologies will be used to solve real-life business problems across companies all over the world. The pace of real-time innovations will also accelerate with advanced technologies. ML and neural network design will be extensively used in all the applications. Augmented reality (AR) and virtual reality (VR) applications are already giving way to massive transformations. More breakthroughs in these areas are likely to occur in the coming year and the human-machine interaction is deemed to improve because of this. Human expectations and experiences from digital systems and machines will rise.
With further growth of IoT, edge computing will increasingly become popular. With thousands of devices and sensors collecting data for analysis, businesses are increasingly doing more analysis and data processing close to the source of origin. Edge computing will be on the rise to maintain proximity to the source of information. Issues related to bandwidth, connectivity and latency will be solved through this. Edge computing along with cloud technology will provide a coordinated structure that simulates a paradigm of the service-oriented model. In fact, IDC predicts, “By 2020, new cloud pricing models will service specific analytics workloads, contributing to 5x higher spending growth on cloud vs. on-premises analytics.”
Blockchain is a major technology that underlies cryptocurrencies like Bitcoin. It is a highly secured ledger and has a variety of applications. It can be used to record a large number of detailed transactions. Blockchain technology can have far-reaching implications in terms of data security. New security measures and processes emulating the blockchain technology can appear in the coming year.
I love the question: #What will be the 5 Data Science trends in 2019?
TOP 25 TIPS TO BECOME A PRO DATA SCIENTIST3!
Hi friends, I have worked in a head huntiing company since 2014, main field in data science, AI, deep learning…. Let me share amazing tips to become a pro d,ata scientiist as below. I hope that you love it. (ref from kdnuggets).
1. Leverage external datta sources: tweets about your company or your competitors, or datta from your vendors (for instance, customizable newsletter eBlast statistics available via vendor dashboards, or via submitting a ticket)
2. Nuclear physicists, mechanical engineers, and bioinformatics experts can make great datta scientists.
3. State your problem correctly, and use sound metrics to measure yield (over baseline) provided by datta science initiatives.
4. Use the right KPIs (key metrics) and the right datta from the beginning, in any project. Changes due to bad foundations are very costly. This requires careful analysis of your daata to create useful daatabases.
5. Ref this resourrce: 74 secrets to become a pro data scientiist
6. With big daata, strong signals (extremes) will usually be noise. Here’s a solution.
7. Big dat,a has less value than useful dat,a.
8. Use big dat,a from third-party vendors, for competitive intelligence.
9. You can build cheap, great, scalable, robust tools pretty fast, without using old-fashioned statistical science. Think about model-free techniques.
10. Big dat,a is easier and less costly than you think. Get the right tools! Here’s how to get started.
11. Correlation is not causation. This article might help you with this issue. Read also this blog and this book.
12. You don’t have to store all your dat,a permanently. Use smart compression techniques, and keep statistical summaries only, for old dat,a.
13. Don’t forget to adjust your metrics when your da,ta changes, to keep consistency for trending purposes.
14. A lot can be done without da,tabases, especially for big da,ta.
15. Always include EDA and DOE (exploratory analysis/design of experiment) early on in any da,ta science projects. Always create a da,ta dictionary. And follow the traditional life cycle of any da,ta science project.
16. Da,ta can be used for many purposes:
– quality assurance
– to find actionable patterns (stock trading, fraud detection)
– for resale to your business clients
– to optimize decisions and processes (operations research)
– for investigation and discovery (IRS, litigation, fraud detection, root cause analysis)
– machine-to-machine communication (automated bidding systems, automated driving)
– predictions (sales forecasts, growth, and financial predictions, weather)
17. Don’t dump Excel. Embrace light analytics. Da,ta + models + gut feelings + intuition is the perfect mix. Don’t remove any of these ingredients in your decision process.
18. Leverage the power of compound metrics: KPIs derived from da,tabase fields, that have a far better predictive power than the original d,atabase metrics. For instance, your da,tabase might include a single keyword field but does not discriminate between the user query and search category (sometimes because d,ata comes from various sources and is blended together). Detect the issue, and create a new metric called keyword type – or d,ata source. Another example is IP address category, a fundamental metric that should be created and added to all digital analytics projects.
19. When do you need true real-time processing? When fraud detection is critical, or when processing sensitive transactional d,ata (credit card fraud detection, 911 calls). Other than that, delayed analytics (with a latency of a few seconds to 24 hours) is good enough.
20. Make sure your sensitive d,ata is well protected. Make sure your algorithms cannot be tampered by criminal hackers or business hackers (spying on your business and stealing everything they can, legally or illegally, and jeopardizing your algorithms – which translates in severe revenue loss). An example of business hacking can be found in section 3 in this article.
21. Blend multiple models together to detect many types of patterns. Average these models. Here’s a simple example of model blending.
22. Ask the right questions before purchasing software.
23. Run Monte-Carlo simulations before choosing between two scenarios.
24. Use multiple sources for the same d,ata: your internal source, and d,ata from one or two vendors. Understand the discrepancies between these various sources, to have a better idea about what the real numbers should be. Sometimes big discrepancies occur when a metric definition is changed by one of the vendors or changed internally, or data has changed (some fields no longer tracked). A classic example is web traffic data: use internal log files, Google Analytics and another vendor (say Accenture) to track this data.
25. Fast delivery is better than extreme accuracy. All data sets are dirty anyway. Find the perfect compromise between perfection and fast return.
