When you think of a data scientist, what comes to mind? Probably some combination of a hacker, a mathematician, and/or an analyst—someone who can hack together a prototype and use math to solve big problems. This is certainly true for me! I love building new tools and analyzing data to gain insights about how people are using them.
I also enjoy sharing my results with others in the company so that everybody gets excited about the changes we’re making and has a clear understanding of how this impacts their day-to-day lives as social media users.
As the world becomes more digitalized and the internet of things expands, companies are starting to collect more data than ever before. This data is being used to better understand their customers, predict what they will want next, and create new products.
A Facebook Data Scientist is a person who uses statistical models and algorithms to analyze user behavior on Facebook in order to make predictions about future trends. They work with engineers and other data scientists in order to model user behavior.
What does a Facebook Data Scientist Do?
Collect and analyze data
Collecting and analyzing data is a critical part of a Facebook Data Scientist’s job. This process can be broken down into two main parts: collecting the data, and then analyzing it.
First, you start by importing your raw data into an environment where it’s ready for analysis. You’ll need to know what tools are available for this purpose (there are many!), as well as how to use them effectively. Then you’ll need to determine what kind of insights you want from your analysis so that you can design the right queries with which to analyze the data in order to get those insights!
In addition to having knowledge about the tools themselves and their capabilities, it’s equally important that a Facebook Data Scientist knows how much effort needs to go into collecting high-quality data in order for any analyses they perform on those datasets later on down the road during their project cycles.”
Build and prototype analysis systems
A prototype is a preliminary version of something, which is subject to change. A prototype system is a piece of software that has been built to test out and evaluate theories without needing to be fully developed or released.
A prototype system can be used for many different things, such as:
- Testing out new features before they go into production.
- Providing a sample set for A/B tests on users’ behavior with certain features or content.
- Evaluating how well internal processes will work before implementing them in production systems (for example, testing whether an algorithm will return accurate results).
Solve problems using mathematics and statistics
As a data scientist, you’ll spend most of your time solving problems. You’ll use math and statistics to identify what’s causing the problem, and then you’ll use data science tools to solve it.
You may be thinking: “Wait, I thought data scientists were all about big data.” And that’s true! But they also need to be able to make sense of the small bits of information that make up those bigger datasets. These small pieces can be used in many different ways depending on how many pieces there are, how far apart they are from each other geographically or demographically, etc.
So being able to understand them better gives you more control over making sense out of larger sets as well as smaller ones—which means more opportunities for finding patterns (and thus more insights).
Communicate results
It’s important for a data scientist to understand the implications of their results, and be able to communicate these in a way that doesn’t just speak to other data scientists. To do this, you need to be able to find ways of communicating your results that are both accurate and understandable.
This means that you need to be able to explain any new or complicated concepts in simple terms. You also need to consider who is going to be reading what you write: if they have no experience with the subject matter, then it might not make sense if you use technical language or jargon.
On the other hand, if someone has more knowledge than you do in some area, there could well be an opportunity for collaboration—for instance by answering questions they have on a particular topic.
A data scientist builds systems that use math to solve big problems.
A data scientist is a problem solver. Data scientists use math and statistics to solve problems in a variety of industries. For example, if you need to know how many people are viewing your website or which kinds of ads are most effective, a data scientist can help you find the answers.
A lot of people have heard about big data, but few know what it means or how it works. Big data refers to large datasets that are very complex and difficult to analyze using traditional methods (like Excel spreadsheets). To understand big data, it helps to think about two things: volume (the size of an entire dataset) and velocity (how quickly information changes over time).
A simple example would be an online retailer trying to predict how many units will sell out during Black Friday sales based on past buying patterns; this requires analyzing all previous purchases as well as current trends in customer behavior.
Conclusion
This is a great career if you’re interested in helping people solve problems using math. You can work at a big company or a small startup, but either way, you’ll be working with people who are passionate about solving the world’s biggest problems.