Two commonly cited use cases for advanced analytics across financial services entail risk management and fraud detection; specifically, the use of advanced analytics to detect and reduce incidents of false positives.
Many financial services institutions (FSIs) are still working on optimizing these solutions and contrary to what some may believe, artificial intelligence has not rendered advanced analytics obsolete. In fact, many robust AI solutions rely on insights weaned through advanced analytics, and those organizations that are not yet ready to hand off their reins entirely to AI may find solace in mastering advanced analytics first. Read More
Tag Archives: Data Science
4 Intersecting Domains That You Can Easily Confuse with Artificial Intelligence
Once you start consuming machine learning content such as books, articles, video courses, and blog posts, you will often see the terms like artificial intelligence, machine learning, deep learning, big data, and data science being used interchangeably. These terms represent several closely related areas within the field of artificial intelligence. They are usually used interchangeably without adequate attention paid to their scopes. It’s not entirely the authors’ fault since there is a slight ambiguity about these terms’ differences. With this post, we will put an end to this ambiguity and clarify their scopes, covering: Artificial Intelligence, Machine Learning. Deep Learning, Data Science, and Big Data. Read More
Top 20 Websites for Machine Learning and Data Science in 2020
Data science is booming exponentially in almost all parts of the world. Data scientists are highly sought after because they seem to have the “magical” ability to create value from data for data-driven companies and organizations.
Here is a list of the best websites for ML and data science to follow for valuable resources and news.
1 — Machine Learning Mastery
2 — Elite data science
3 — KDnuggets
4 — Kaggle
5 — Reddit — r/datascience
6 — Towards Data Science
7 — Analytics Vidhya
8 — Data Science Dojo
9 — Data Science 101
10 — Geeks for Geeks — Machine Learning
11 — Google News — Data Science
12 — Datafloq
13 — Domino Data Science Blog
14 — data36
15 — Revolutions
16 — Edwin Chen
17 — Pete Warden’s Blog
18 — InsideBIGDATA
19 — Google AI Blog
20 — Nature
Read More
63 Machine Learning Algorithms — Introduction
Data Science and analytics are transforming businesses. It has penetrated into all departments be it Finance, Marketing, Operations, HR, Designing, etc. It is becoming increasingly important for B-school students to have analytical skills and be well versed with Machine Learning and Statistics. Data is being called the new gold. The fastest growing companies in the coming period will be the ones who can make the most sense of data they collect. As through the power of Data a business can do targeted marketing, transforming the way they convert sales and satisfy demand. Read More
How to Get Into Data Science Without a Degree
Advice from a Data Scientist in the same position
This article is for those who fall into one of the following categories:
- You don’t have a post-secondary degree but you’re interested in data science.
- You don’t have a STEM-related degree, but you’re interested in data science.
- You’re working in a field completely unrelated to data science, but you’re interested in data science.
- You’re simply interested in data science and want to learn more about it.
Read More
Previous article on “How I’d Learn Data Science if I Could Start Over.”
Digital Twin, Virtual Manufacturing, and the Coming Diamond Age
If you have ever had a book self-published through Amazon or similar fulfillment houses, chances are good that the physical book did not exist prior to the order being placed. Instead, that book existed as a PDF file, image files for cover art and author photograph, perhaps with some additional XML-based metadata indicating production instructions, trim, paper specifications, and so forth.
When the order was placed, it was sent to a printer that likely was the length of a bowling alley, where the PDF was converted into a negative and then laser printed onto the continuous paper stock. This was then cut to a precise size that varied minutely from page to page depending upon the binding type, before being collated and glued into the binding.
At the end of the process, a newly printed book dropped onto a rolling platform and from there to a box, where it was potentially wrapped and deposited automatically before the whole box was closed, labeled, and passed to a shipping gurney. Read More
Do You Need a Chief Data Scientist?
Data scientists are modern-day wizards who can turn digital coal into virtual diamonds. But data scientists are unique individuals with special talents, and organizations risk squandering those gifts if data scientists are managed like any other employee. Some organizations are finding that the individual best suited to manage data scientists is another data scientist, also known as the Chief Data Scientist. Read More
Ain’t No Such a Thing as a ‘Citizen Data Scientist’
Dear Aspiring Data Scientist,
Before you start using ‘low code’ or ‘drag & drop’ data science tools, please learn the fundamentals.
Why aspire to be ‘Citizen Data Scientist’ when you can truly become a ‘Data Scientist.’ Read More
Consequences of mistaking models for software
Twelve traps to avoid when building and deploying models
In Part 1 of this series on data scientists are from Mars and software engineers are from Venus we examined the five key dimensions of difference between software and models. The natural follow on question to ask is — So What? Does it really matter if models are conflated with software and data scientists are treated as software engineers? After all for a large cross-section of the population, and more importantly the business world, the similarities between them are far more visible than their differences. In fact, Andrej Karpathy refers to this new way of solving problems using models as Software 2.0. If they are really the next iteration of software are these differences really consequential. Read More
Data Scientists are from Mars and Software Developers are from Venus (Part 1)
Mars and Venus are very different planets. Mars’s atmosphere is very thin and it can get very cold; while Venus’s atmosphere is very thick and it can get very hot — hot enough to melt lead!.
…Software Engineers and Data Scientists come from two different worlds — one from Venus and the other from Mars. They have different backgrounds mindsets, and deal with different sets of issues. They have a number of things in common too. In this and subsequent blogs we will look at the key differences (and similarities) between them and why those differences exist and what kind of bridge we need to create between them. In this blog, we explore the fundamental differences between software and models. Read More
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