Friday, September 6, 2019
Business Intelligence and Analytics 28
1 10 Machine Learning Methods that Every Data Scientist Should Know
Machine learning is a hot topic in research and industry, with new methodologies developed all the time. The speed and complexity of the field makes keeping up with new techniques difficult even for experts — and potentially overwhelming for beginners. To demystify machine learning and to offer a learning path for those who are new to the core concepts, let’s look at ten different methods, including simple descriptions, visualizations, and examples for each one.
2 Why is DATA important for your business?
https://towardsdatascience.com/how-important-is-data-for-your-business-c15a35c6935e
A human body has five sensory organs, each one transmits and receives information from every interaction every second. Today, scientists can determine how much information does a human brain receive and guess what! Humans receive 10 million bitsof information in one second. Similar for a computer when it downloads a document from the web over a fast internet.
3 Manage Your Leads With Tags: 8 Benefits Worth Checking
https://www.business2community.com/marketing-automation/manage-your-leads-with-tags-8-benefits-worth-checking-02237178
Lead generation processes are evolving. Whether you are using manual forms, API integrations or retargeting, the one thing that makes a lead’s quality better than another, is the amount of progressive data you have gathered around that lead.
4 How to Use Customer Service to Drive a Better Customer Experience
https://www.business2community.com/customer-experience/how-to-use-customer-service-to-drive-a-better-customer-experience-02237045
It’s not very common for customers to contact customer service when everything is going fine. No, it’s at times when something unexpected happens, situations like their order didn’t arrive when anticipated, the product is broken, or instructions aren’t clear.
5 Advice on building a machine learning career and reading research papers by Prof. Andrew Ng
https://www.kdnuggets.com/2019/09/advice-building-machine-learning-career-research-papers-andrew-ng.html
Andrew shared two major recommendations, specifically:
- Reading research papers: efficient techniques ,that he uses, to read research papers when trying to master a new topic in deep learning.
- Advice for navigating a career in machine learning.
6 Why U.S.-China Supply Chains Are Stronger Than the Trade War
While the trade war between the U.S. and China continues to take its toll, global supply chains provide a “force for reason” in ending the standoff because they bind the two countries in prosperity, writes Wharton dean Geoffrey Garrett in this opinion piece.
7 4 aspects to consider for Achieving Artificial intelligence Progress
https://medium.com/@manager_77257/4-aspects-to-consider-for-achieving-artificial-intelligence-progress-3e64fd9a20d6
Artificial Intelligence conquers more and more different areas in business and to present its new solutions to old problems. An attempt to introduce artificial intelligence was driven by business, and not everyone said it was successful enough. And now the logistics industry is also making attempts to implement AI.
8 Data without Borders: How Synthesis Enables Ethical Sharing
https://insidebigdata.com/2019/09/04/data-without-borders-how-synthesis-enables-ethical-sharing/
In this special guest feature, Mike Capps, CEO of Diveplane, discusses why organizations should consider synthesizing their data, what that process entails, what the benefits and use cases of synthesized data are and why he believes synthesized data is the key to not only a successful data/AI project, but also to a successful data-driven future.
9 MIT report examines how to make technology work for society
http://news.mit.edu/2019/work-future-report-technology-jobs-society-0904
Automation is not likely to eliminate millions of jobs any time soon — but the U.S. still needs vastly improved policies if Americans are to build better careers and share prosperity as technological changes occur, according to a new MIT report about the workplace.
10 What’s the difference between analytics and statistics?
https://www.kdnuggets.com/2019/09/difference-analytics-statistics.html
Statistics and analytics are two branches of data science that share many of their early heroes, so the occasional beer is still dedicated to a lively debate about where to draw the boundary between them. Practically, however, modern training programs bearing those names emphasize completely different pursuits. While analysts specialize in exploring what’s in your data, statisticians focus more on inferring what’s beyond it.
