US news college rankings over the years
US news college rankings over the years
Where can I find historical trend data for the USNews WorldReport University College Rankings?
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.
(William Beeman, Professor and Chair, University of Minnesota)
This list includes USNews, PrincetonReview & WSJournal -
An interesting article is the Average U.S. News Rankings for 123 Universities: 2012-2019 that gives a historical prospective.
If you want to find other sources, you would google "US New and Report historical national university rankings".
