Of course, there are some key differences between these two roles. One of the biggest differences is the use of Machine

Say you have a small “niche” blog about a very specialized topic, and your aim is to simply earn passive income with the blog and not grow your blog over the long-term. After you’ve written enough posts to cover the topic well and you’ve built up a steady base of visitors you could leave it to basically run itself (whilst coming back to update it every few months or so — remember you can’t leave a business dead forever and expect it not to stagnate).

As someone who used to work in computer sales, I understand and applaud Apple’s marketing. This is not blind praise, but rather a both business and consumer-centric understanding of how well-crafted their marketing actually is. Most customers who would walk into the shop would throw empty stares my way whenever I’d start talking specs. They rarely if ever cared about what CPU I would put into the case, how much RAM the machine would have or the brand of the mother-board. Some wanted to know which latest game they could play with those specs, how many movies they could fit on the hard drive, and will it all still work in two or three years.

Thank you for reading! I hope you found this article both interesting and useful. Feel free to comment down below some of the similarities and differences you have found or experienced between Data Science and Business Analytics.

On the other hand, if you’ve got bigger plans for your blog and really enjoy the topic, you should focus on building it slowly over the long-term if you want to see success.

This is called Pay Per Click (PPC) advertising and how much you make per click all depends on the topic of your site and how much advertisers typically spend in that industry.

But just a heads up — I wouldn’t recommend this as a stand-alone strategy that you should base a business around — it’s more of a supplemental method to squeeze out more income from an existing website and is commonly used in conjunction with others strategies such as affiliate marketing and/or creating your own product.

These two roles share goals with one another. Each requires a deep dive into data with similar tools as well. The process of communication is similar, too — working with stakeholders from the company to go over the business problem, solution, results, and impact. Here is a summary of the key similarities between a Data Scientist and a Business Analyst.

For example, if you have a site about finance you’re going to make a lot more per click (perhaps up to a few dollars) than say a site about dog training (likely a measly few cents per click).

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awarded for the discovery of radium and polonium. I believe that there is no connection between my scientific work and the facts of private life. I cannot accept … that the appreciation of the value of scientific work should be influenced by libel and slander concerning private life.”

Then you simply place their ads on whichever pages of your site you wish. The ads will automatically be targeted to match the content on your pages, and whenever somebody actually clicks on one of them you earn a small amount of money.

What is even more interesting, I believe, it is to see how accurate it is. When the price was hitting an upper or lower band, the share price was correcting itself. You can see that on the graph above.

Starting the 17th of November 2020, the need for a fan in a powerful machine is not intrinsic, and those that have one do so to keep performance up rather than keep your machine from bursting into flames or your CPU attempting kamikaze. The assumptions that only an Intel or AMD processor can rule the professional and creative world are out the window. The association of endless cryptic specs with performance and the ability to deliver results is now all but unnecessary. The correlation between price and performance is also less of a point now. The M1 Mac Mini for instance can do everything a recent 15' or 16' Pro can and more at a fraction of the latter’s price.

The OEM market will likely struggle to convince both the general consumers and the professionals to buy a machine that’s similar in price, but 5 to 10 times slower. Me thinks, Apple’s move could seismically shift a lot of the professional community to Apple devices and thus Apple-centric development. Microsoft relies on OEMs to sell its software and provide many of its services, but if people will start opting for a relatively cheap yet powerful Mac Mini or Air, instead of a DELL, HP, Lenovo, etc, the results could be monumental. Apple seems to play a different game, one that has at its core what people can achieve with technology rather than meaningless numbers.

Both roles vary from company to company. What stays the same are the goals and impact that each role employs. Perhaps the biggest difference is the method of how you get to the solution or surface a finding. Some Business Analysts may find themselves eventually becoming Data Scientists, and vice versa. It depends on your preference for the skills and tools needed to perform your job.

It is also blatantly obvious now that ARM processors are very mature and completely viable for both laptops and desktops. A stage dominated by Intel and to some extent by AMD, is getting more fragmented now. But me thinks, not for long. While I have a deep respect for both of these companies as they enabled and supported for decades the current industrial revolution, Apple with TSMC has shown the world that ARM can do more and do so in a way that is both cost-efficient and has genuine benefits for everyone who picks up one of their machines.