Unfortunately, all too many product teams speed through this testing or skip it all together and simply march ahead to e

er September 11, New York felt if not dead, exactly, then numb. In May 2002, New Yorker writer Adam Gopnik wrote, referring to a famous 1976 Saul Steinberg cover of the magazine, that “A Steinbergian drawing of New York at the moment would show eight million people, each person standing on a pole above an abyss of anxiety — not looking down, never looking down, looking only from side to side, warily.” There were a few terrible weeks this April when I rode through the desolate streets of Manhattan that I did wonder if this vision had finally come to pass.

SQL stands for Structured Query Language. Unfortunately, this does not mean that you will come up naturally with a perfectly structured piece of code once you start programming in this computer language.

If you’re considering taking a job as a head of data science, your first question should always be, “Who is responsible for making sure my team has data?” If the answer is YOU, well, at least you’ll know what you’re signing up for.

While it’s true that you’re a key customer for data engineering, you’re probably not the only customer. Modern businesses use data to fuel operations, often in ways that can hum along nicely enough without your interference. When your contribution to the business is a nice-to-have (and not a matter of your company’s survival), it’s unwise to behave as if the world revolves around you and your team. A healthy balance is healthy.

Before signing up for your new gig, consider negotiating for ways to hold your data engineering colleagues accountable for collaborating with you. If there are no repercussions to shutting you out, your organization is unlikely to thrive.

“Jetpack is a suite of libraries to help developers follow best practices, reduce boilerplate code, and write code that works consistently across Android versions and devices so that developers can focus on the code they care about.” — Android Jetpack

Great! You have decided to learn the art of Data Science and now you want to take on the challenge. But which language should you pick? R or Python? In my previous article, I explain why, but for now, all you need to do is just pick one and get started with it. Go to YouTube and search up tutorials for the language of your choice. The first thing you need to learn is how to install the language and it’s IDE (Integrated Development Environment).

Data scientist: The sexiest job of the 22nd century Ask these 3 questions during a job interview to make sure your employer is ready to make data scientists effectivetowardsdatascience.com

With recruiters listing a myriad of “preferred skills” in their job postings, learning Data Science can get quite overwhelming at times. Dividing the journey up into five chapters can provide a clearer picture of what lies ahead.

JetPack libraries cover major features like paging3 for pagination, Room for the local database, WorkManager for long-running background tasks, DataStore for improved data storage, Hilt for DI, navigation component to navigate in the app UI, App Startup to reduce app startup time, and more.

As a Data Analyst, I keep applying myself three simple rules to better structure my SQL queries. They allow me not only to avoid mistakes in the way that I query from databases, but also to save time — for myself as well as for the colleagues I share my queries with. Let me present you these tricks and illustrate them with a real-life example.

Android Studio also provides tooling support for several libraries such as a navigation editor to view an application’s navigation graph and a motion editor to implement effective animations and transitions in a fancy way.

Data scientist: The sexiest job of the 22nd century Ask these 3 questions during a job interview to make sure your employer is ready to make data scientists effectivetowardsdatascience.com

Since data science is at the mercy of data, merely having data engineering colleagues might not be enough. You might face an uphill struggle if those colleagues fail to recognize you as a key customer for their work. It’s a bad sign if their attitude reminds you more of museum curators, preserving data for its own sake.

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Unfortunately, all too many product teams speed through this testing or skip it all together and simply march ahead to e

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