Beginner’s Guide to Data Science

Data science is a complex and rapidly growing field. It involves organizing large amounts of unstructured data into manageable sets of information. Data scientists use a combination of mathematics and computer science to analyze data from almost anywhere (such as emails, social media feeds, and online surveys) and look for trends.

Identifying patterns leads to actionable insights for businesses in nearly every industry. Read on to learn more about what a data scientist does daily, as well as what tools they need to do their job successfully.

Daily Life of a Data Scientist

A data scientist’s daily responsibilities will vary widely depending on where they work and the projects they’re working on. Here are just a few examples of tasks that data scientists can expect themselves to perform on a routine basis:

• Clean huge volumes of data so that only relevant information remains, then prepare the data for processing and visualization.
• Combine analytical methods with artificial intelligence and machine learning to present prescriptive and predictive analyses to teams. These types of analyses try to explain what could happen if a certain action is taken and makes educated guesses about what a company should do with the information that is currently available. 
• Build and use computer programs that automate common and repetitive processes so that more time is allocated for other tasks.
• Communicate and recommend affordable solutions to relevant departments by using understandable illustrations of the data. These are usually charts and infographics that someone without a data science background can grasp. 

Common Tools for Data Science

The work data scientists do is complex, so they need a lot of different tools to effectively complete it all. These tools need to be powerful enough to handle the large amount of information that data scientists often work with. Below are some of the most popular tools that today’s data scientists use daily:

• Data Storage Tools: Data scientists often work with truly enormous sets of data, sometimes growing to Terabytes in size. Most of this data is unstructured as well, meaning the data isn’t stored or organized in a defined way. Structuring data is often the first step toward being able to see patterns emerge, so data scientists need tools that can help them. One popular storage tool is called Apache Hadoop, which breaks down large files across different nodes with specific instructions on what to do with the files. This allows for both quicker and smoother processing. Statistical Analysis Systems (SAS) is another program that merges multiple data tables to perform predictive analysis. It also contains a powerful visual analytics component that will create eye-catching reports
  
  

• Data Analysis Tools: Once data has been structured and organized, it can be prepared for analysis. The tool you choose to analyze your data may depend on what kind of analysis you want to perform. For example, RapidMiner is a popular choice for those who require predictive analysis; this type of analysis seeks to find out “what might happen if we do X.”
  
  

• Data Visualization Tools: The information that a data scientist collects always needs to be shared with others. Most often, it needs to be illustrated understandably so others can easily see the same patterns and agree on a path forward. Data visualization software does just that. It takes the raw data and creates graphs and charts that display the results found in the data. These images can help entire teams quickly get on the same page regardless of their comfort level with statistical analysis. 

Preparing for a Data Science Career

Typically, data scientists require a minimum of a bachelor’s degree before they can start their careers, though most will have a master’s or a doctoral degree. Many will start work in a junior data analyst position at a smaller company to gain valuable experience before they begin their education toward a more advanced degree. These degrees are especially important if your ultimate goal is to be in a management position. 

Not only do data scientists possess a mastery of topics like mathematics and statistics, but they also need to be quick and innovative thinkers. They often need to rework their understanding of data at a moment’s notice, and they need to remain unbiased while targeting their research toward what will have the greatest benefit for their organization. The best data scientists are also those who remain curious, even when they have years of experience under their belts. 

Data science rarely generates one concrete answer; for those who have the drive to examine things more closely, there’s always more to learn. If you’d like to boost your curiosity in the workplace, there are lots of simple things you can do. For example, you could spend more time with a colleague who has a different background than you, practice active listening to learn more about what coworkers have to offer, and work on asking more questions. 

Learn Data Science with Career Centers

Noble Desktop, a partner program of Career Centers, offers a range of data science classes to help beginners gain a solid foundation as they begin their data science careers. There are shorter, more focused classes that teach just one component of data science. For example, Noble offers a Python for Data Science Bootcamp that focuses specifically on how the coding language Python aids in successful data science practices. There is also a three-day SQL Bootcamp if that specific language interests you. For some more intensive options, you can check out the Data Science Certificate or the Data Analytics Certificate. Both require a greater investment of time, but they are more comprehensive and include a portfolio of your work to display for future job interviews!