The age of information has introduced us to a new world of data. Prior to the invention of telecommunication and the technologies of the internet, data was small enough to be stored within local storage and hardware. In the 21st century, we have moved into a space where there is so much information and data on everyone and everything. From the Internet of Things (IoT) like wearables and smart home products to the ways that we engage with the internet, such as through social media, applications, advertisements, and websites, there are multiple data points being collected on each of us at any time. With all of this data available about individuals, the internet, and the world that we live in, data science professionals across industries have learned how to use this data to explore complex problems, such as predicting future trends and influencing user behavior. If you are interested in learning more about what big data is and why it is important, keep reading!

What is Big Data?

But first things first, we need to define big data. In contrast to small data, big data is essentially data that is large enough to require specialized tools and methods of storage and organization in order to analyze it. Understood through the big three of volume, variety, and velocity, big data is set apart from other forms of data not only based on its size but through the different types of data that are included within it, as well as the speed through which we are able to collect and analyze it.

In contrast to small data, which can be easily understood using spreadsheets and statistical analysis, working with big data requires the knowledge of programming languages, machine learning, algorithms, databases, and cloud computing. The faster data comes, the easier it is to generate large data sets. This in turn leads to more insights that data science professionals can discover about how to make better decisions and solve problems that are important to this current moment in time, as well as to generate and implement solutions to problems that we may face in the future.

Data Science Certificate: Live & Hands-on, In NYC or Online, 0% Financing, 1-on-1 Mentoring, Free Retake, Job Prep. Named a Top Bootcamp by Forbes, Fortune, & Time Out. Noble Desktop. Learn More.

How Big Data Changed Data Science

Big data has revolutionized the data science industry by changing the norms of how data is collected, stored, organized, analyzed, and even presented to audiences. Through an understanding of how these different aspects of the data science life cycle are influenced by big data, you can gain a better understanding of how big data is currently being used within data science.

  • Data Collection - In our current day and age, big data is everywhere, and there are many ways that big data is being collected on individuals through their day-to-day interactions. Especially when engaging with the internet or different institutions, each individual person is contributing to the collection of big data. Whether you are shopping at a store with a rewards card or even clicking on advertisements when scrolling through social media, there are many minute choices that we make on a daily basis that contribute to the collection of big data.
  • Data Storage - For many years, information and data were easily stored within physical places and spaces, such as filing cabinets and archives, as well as in materially based technological devices like individual computers or data storage devices. However, storing big data requires even larger storage systems that have the capacity to not only hold massive amounts of information, but that also makes it easy for a data science professional to search and retrieve data from where it is stored. The storage of big data usually requires some type of database system, such as a relational database management system, which can handle both the storage and retrieval of large stores of data. The technological advances in society have also resulted in more data being stored within the cloud, and not just on local servers or hard drives.
  • Data Organization and Management - While smaller stores of data can be easily organized by individuals that may not have a background in data science, organizing big data requires a system or program in order to ensure that all of the data within a database is accounted for and annotated. The organization of big data requires knowledge of higher-level statistical analysis or programming skills, such as performing exploratory analysis of the data-set to identify missing data and uncover potential patterns or data types. In addition, big data management requires a system that makes it easy to illiterate processes i.e. completing the same commands or functions multiple times to the same data-set, such as giving a specific name or designation to a section of the dataset. Through learning programming languages and database management or design, organizing big data can be just as quick and easy as organizing smaller data sets.
  • Data Analysis - As a form of data that has greater volume, velocity, and variety than small data sets, big data requires a form of data analysis that does not only focus on statistical analysis but also has the potential to identify patterns and trends. Big data analysis generally utilizes some form of data mining, predictive analytics, or deep learning in order to learn more about the dataset and to gather insights from what is found. Identifying patterns in a dataset (data mining) sets the stage for data science professionals to make predictions about the future based on these past trends and patterns (predictive analytics) which are not only used to make predictions about information and data but also patterns of human behavior (deep learning).
  • Data Modeling and Visualization - With the increase in data size, there is also an increase in the complexity of insights that come from data analysis. The methods of communication and modeling of big data also require specialized procedures and programs. With specialized techniques, such as dimensional data modeling, or data science tools, like Tableau, data science professionals are able to create visually engaging and aesthetically pleasing models and visualizations of big data.

Why is Big Data Important?

In addition to the many ways that big data has changed the field of data science, big data has also become incredibly important to multiple industries. From advertising to social media, big data offers many businesses a more efficient and reliable way of making decisions and solving problems. Big data also streamlines workflow management and the daily operations of any industry which relies on finding and delivering records and personal information, such as libraries, healthcare, and financial institutions. Due to its reliance on systems and algorithms, working with big data offers many possibilities for the creation of not only new insights and findings but more accurate predictions and forecasts. Big data will continue to play an important role in the future as we learn more about how data can not only be used for industry but also for social good.

Ready to start working with Big Data?

Noble Desktop offers data science classes that include bootcamps and courses which include hands-on and interactive exercises and portfolio projects, as well as a Data Science Certificate that teaches you how to use programming, machine learning, and algorithms to work with big data. The live online data science classes take a variety of approaches to the data science lifecycle. You can also find in-person data science classes near you for a more traditional classroom experience.