Some functions require a long list of arguments. Although this should be avoided altogether (e.g. by using data classes)

Simply showing a table of numbers without explaining the context and business impact is not useful. Explain to your stakeholder how the numbers relate together using charts to show trends and point out relevant information. Data literacy is a problem in many companies and being able to communicate results a stakeholder can understand and use to make decisions are essential skills of a great analyst.

I was once asked to pull user level data that wouldn’t fit into an Excel worksheet because it was more than one million rows. I knew it was going to be a nightmare for my stakeholder to load into Excel because of memory constraints. I asked why this data was needed and based on the context I was able to provide the view my stakeholder really wanted with a few thousand rows of data.

There’s a lot of information about how to become a data analyst but there’s little that guide you in the right direction when you first become one. To avoid going down the wrong one way road this is my advice on how to become successful as new data analysts.

To become a great data analyst, it’s important to understand the company business, how the company makes money, and to learn the KPIs used to measure success. Once you learn the business and KPIs you’ll be able connect your stakeholder request to the business and the impact they want to drive.

Decorators are wrappers around a function that modify the behavior of the function in a certain way. There are many use cases for decorators, and you may have used them before when working with frameworks like Flask.

OK. Please allow me to stop here. There are just too many impressive features in this library that I can’t show them all in one article, though I have demonstrated the ones that I think are the most interested. There are still more features such as the columns, panel, padding and render groups that are also great. It is highly recommended to check them out by yourself.

The worst thing for any data analyst is giving the wrong number to your stakeholder. This creates doubt in your ability to provide the correct information and hurts your credibility as an analyst. To avoid mistakes always cross check your numbers against other sources. If it’s an important project ask a senior analyst or your manager to look over your numbers before you present to the stakeholder.

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e’s class in the 1980s, but I missed the publication of his book, which came out in the middle of my toddler-nose-wiping, laundry-doing, diaper-changing years. But during those years, as I watched the rise of newspaper digitization in the late 1990s, followed immediately by the rise of blogging in the early 2000s, and then by the seeming decline of print (at least legacy print media), I thought often of Donoghue’s framing of the 18th century: an explosion of texts, most of them awful; a profusion of new voices, all clamoring for a readership, and most destined never to find enough readers to pay the bills; the decline of collective “patronage” for writers regularly producing prose for print media outlets as those outlets lost subscribers and turned to the web for reading.

For example, the company’s website recently had a drop in visitors. The marketing department asked you to help identify a possible cause. You breakdown the website visitors by channel and see a drop from organic search. At this point you can show the results to your stakeholder and you would be done.

Inside print_argument, we define a wrapper function. This function prints the argument and the name of the called function. Next, it executes the actual function and returns its result as if the function was called regularly.

With dreamy prose and dealing with issues of love, loss, friendship, and motherhood, this book is all about what it means to be a woman. Flitting between India and the United States, the stories take you on a wild ride peppered with kaleidoscopic colours. There is a common thread tying the stories — of introspection, self-reflection, and the inevitability of change. The thread is so strong, that sometimes you forget which story you’re on. The transition between them is so seamless, so magical, it makes this a hard book to put down once you start reading.

A great analyst would ask questions to the team responsible for the website to confirm if any changes had been made recently. You’re told there were changes on the same day when visitors from organic search started to drop. Now you can go back to your stakeholder and say “traffic dropped to the website from organic search visitors due to changes made on the website that caused a drop in Google search rankings”. Going this extra mile shows you not only found the source of the problem but took initiative to provide a solution.

It’s common for a stakeholder to ask for one thing but mean something else because they can’t articulate what they want. A great data analyst will dig deeper to understand what’s driving the request and deliver what is actually needed.

“Just like JavaScript.” While trying to stretch my creativity to look for different possibilities, this phrase made me travel back ten years in time. I was learning web development and had just come across Ruby on Rails. At the time, Rails used to ship with a different kind of JavaScript built-in, called CoffeeScript. CoffeeScript had a golden rule that said: “it is just JavaScript.” That being the case, CoffeeScript could fit my criteria, so I decided to see how things were going with the project and give it a try.

For example, let’s say you support the marketing department for an online retail company. The marketing manager would like to make changes to the website and asks what KPIs should be used to measure success. Since you took the time to understand the business you’re able to ask the right questions to recommend appropriate KPIs such as conversion rate and average order value to measure success.