Most, if not all, businesses would benefit from having some insight into their data. With structured data that tells a story, companies can make important decisions that can help contribute to its success. Data scientists can deliver valuable information based on data and trends, and they are an important part of forming solutions to an organization's issues. In this article, we examine what a data scientist is and list some skills that a data scientist may have.
Read more: Learn About Being a Data Scientist
What is a data scientist?
A data scientist works closely with data so they can analyze it, find trends and use the data to understand something more or develop processes to improve operations in some way. Data scientists combine mathematics and computer science in their work, but they also have some knowledge about the industry they serve. It's typical for data scientists to have to navigate through unstructured data to produce reports, suggestions and solutions that will help a business succeed.
Here are some skills that a data scientist may possess:
Cloud computing is important for data scientists to know how to use because the cloud provides a place for them to hold on to, retrieve and share data. Because many companies use cloud computing for their servers, storage and databases, data scientists must be able to navigate their shared cyberspace. Businesses may even store data in the cloud without realizing it, but data scientists work on retrieving and analyzing associated data.
Statistics and probability
At its core, data science is working with algorithms, systems and processes to understand more about the data so they can then make informed decisions, gain insight and separate important information from a database. Data scientists frequently have to estimate and predict how different data will perform. If data scientists have probability skills, they can use statistical methods to assist them in performing these predictions and analyzing data even further.
With skills in statistics and probability, data scientists can predict trends and develop forecasts, discover anomalies that exist in the data set, establish a relationship between two points in the data and understand more about the data they are working with.
Data scientists use both multivariate calculus and linear algebra to perform their work. They use calculus to build a machine learning model, but they may also work with derivatives, cost function, plotting, gradients and algebra. When working with advanced mathematics, data scientists can use tools to help them perform calculations, so it becomes more important that they know the principles of calculus and algebra and how they can affect their reports.
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Machine learning involves using statistics to find patterns in data. Machine learning is the artificial intelligence that data scientists use to support their assumptions about a set of data points. Machine learning helps relieve some responsibilities that data scientists have and helps prevent human error. It's even more valuable when data scientists have very large sets of data to work with because machine learning can develop workable algorithms and models so others can process the data in real-time.
Data visualization tools
Data scientists use data visualization tools to translate information and data into visuals like graphs and images. They may do this to help themselves understand their own data report better and from another perspective, or provide details to a stakeholder at a company who requested information from the data. With data visualization tools, it's easier for data scientists to see any trends, patterns and outlying points of data in a set.
A query language is a computer language that data scientists use to ask about databases and the information they hold. The most common query language is structured query language (SQL), and it allows data scientists to quickly retrieve data and use it to form solutions for a certain issue or answer specific questions.
Read more: Top Interview Questions About SQL Server
Since data scientists work with data, they must also be able to manage that data. Most of this involves being able to efficiently retrieve the right data without affecting any other part of the database. There are various database management tools and systems that data scientists may become familiar with so they can work with different companies to store, update and read the data from their system.
Read more: Learn About Being a Database Administrator
Not only must data scientists be familiar with the tools that translate data into visuals, but they must be able to read those visualizations to understand the data better. Visualizations include relationship maps, 3-D plots, bar charts, histograms, line plots and pie charts. The visualization that data scientists render depends on the variables included in the data. Visualizations help data scientists quickly get their questions answered or identify areas for improvement.
Python is an open-source programming language that data scientists use to manipulate data and understand it more. Python syncs well with machine learning and other artificial intelligence tools to provide data in a digestible format that even beginning data scientists can use.
While there are more complex ways to form data listings, Microsoft Excel is a basic program that data scientists can use. With Excel, data scientists can create a database with custom labels, sort and filter data and form tables that you can build calculation functions within.
R is a programming language that centers around statistics, one of the most important types of math that a data scientist uses in their work. R is also open-source software that integrates with other systems to provide a more well-rounded conclusion to some data. When data scientists use R, they are better able to analyze the data, identify trends, create visualizations involving data points or groupings and predict future data. New and seasoned data scientists can find success with R.
Another skill that many data scientists have is data wrangling, which is the process of cleaning raw data, removing outliers, changing null values and turning the data into a format that is more easily used. Through data wrangling, data scientists can come to conclusions faster, especially when they're working with large amounts of data.