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
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Tag Archives: Data Science
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
5 Concrete Real-World Projects to Build Up Your Data Science Portfolio
Do you want to enter the data science world? Congratulations! That’s (still) the right choice.
The market currently gets tougher. So, you must be mentally prepared for a long hiring journey and many rejections. I assume that you have already read that a data science portfolio is crucial and how to build it up. Most of the time, you will do data crunching and wrangling and not applying fancy models.
One question that I am asked on and on is about concrete data sources for cool data and project opportunities to build such a portfolio. Read More
How Do Data Science Machine Learning And Artificial Intelligence Overlap
In conjunction with data science and digital transformation, you have probably heard the terms of artificial intelligence, machine learning, and deep learning is used. You might wonder what the relationship between those topics is. How do companies in industries range from biopharma to chemicals to food & beverage that incorporate AI, machine learning, and data science to enhance their processes? AI and machine learning allow applications such as virtual digital assistants, facial recognition, and self-driving cars, as well as improvements in healthcare diagnostics and process manufacturing. Are you interested in making a career in these? There are many AI certification courses, data science certification courses, and ML certifications available online. Check out! Read More
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