Data science is focused on taking data, or large bites or pieces of content, and turning it into information, or digestible portions. In this sense, we can think of data as a storage center full of content, and information as the means of organizing or sorting through that content to make meaning of it. Therefore, the field of data science focuses on taking data and turning it into information that can be used to identify patterns and gain key insights on audiences, businesses, or consumers. As a popular and fast-growing field, data science is a highly sought out skill across industries, and there are multiple types of data, and tools to analyze it, depending on your area of interest or specialization.
Data generally falls into one of two types: private data or public data. Data Scientists engage in work with both types. Public data is data that is not only widely available but data that is freely given and that can be accessed by multiple entities i.e. social media content, data gathered when using public servers or computers, etc. In contrast, private data is data that is inaccessible or private to an individual (not meant to be shared with others or outside entities or institutions). Many times, private data is illegal or unethical to access and can have negative consequences on the data holder if it is discovered i.e. Social Security numbers, locked or password-protected data, etc. Therefore, data science is not only focused on how to collect and analyze data but also on how to protect it.
As an interdisciplinary field, data science combines statistics and mathematics, as well as information science and data analysis. Because of the interdisciplinary nature of the field, there are multiple fields that are similar to or that build upon data science. Some of the fields related to data science are machine learning, artificial intelligence (AI), and data analytics, while the protection of information and data is also covered under cybersecurity.
Both data science and machine learning are considered to be subsets of artificial intelligence. Machine learning includes the use of algorithms to program computers to complete commands, such as analyzing data. Data analytics is viewed as a more general understanding of analyzing data whereas data science focuses more on using specific industry tools to analyze data, which usually require knowledge of programming, statistical analysis software, machine learning, and/or predictive models.
The Most Popular Tools in Data Science
With the rising popularity of Big Data (large stores of information and data), data science requires the use of various tools and technology that have the power to analyze these large amounts of data. Some of the most popular tools in data science are detailed below:
- Statistical Analysis Software (SAS) - Programs like SPSS and STATA were specifically created to analyze information and data, making them some of the most important tools for individuals that want to work in research-based data science.
- Microsoft Excel and Spreadsheets - While Microsoft Excel is not a statistical analysis software, Excel is a popular program for analyzing business and financial data. However, it should be noted that knowledge of Excel will not be as useful for data science projects that require the analysis of big data.
- Database Design and Querying - In addition to the analysis of data, programming languages like SQL are specifically used to organize and find information within stores of data through search protocols like querying. Database design also focuses on the organization of data once it is collected, which is an important part of the data science life cycle.
The types of tools and technologies that you should learn and use are dependent on your industry or field of study. Learning these popular tools and programs in data science is critical to building a career as a Data Scientist.
What is a Data Scientist?
Data Scientists use their knowledge of analytic tools and technology in order to analyze information and data, as well as to draw conclusions from the trends and patterns that emerge through data analysis. Due to the ubiquity of information and data, careers for Data Scientists are available in multiple fields and industries. Currently, some of the most popular industries for Data Scientists are business and finance, social media and technology, healthcare, and medicine, to name a few.
Within each of these areas Data Scientists are needed for specific types of tools and analysis, for example:
- Data Scientists in Business and Finance primarily work on analyzing economic trends and making predictions. This could look like analyzing stocks and investments, or analyzing consumer data around a product or service.
- Data Scientists in Social Media and Technology focus on the analysis of audiences and user engagement. Data Scientists in this field are able to use social media data in order to improve the quality of digital platforms and the experience of using them.
- Data Scientists in Healthcare and Medicine are able to use data on both patients and healthcare institutions for diagnostics, biotechnology, and even the development of vaccines and medicine.
How to Get Started in Data Science
Students and professionals that are interested in pursuing a career in data science can pursue post-secondary education or take classes and certificate programs focused on learning the tools of information and data. For college students that want to pursue a career in data science, pursuing a bachelor's degree in Statistics or Mathematics, Information Studies, Data Science, Engineering, and Computer Science are all excellent options. It might also be helpful to specialize in a particular field or industry, such as Medicine, Business, or Finance.
For students that have other plans, or professionals that want to make a career change, you can also take part in data science classes and certificate programs to learn the tools and skills required to become a Data Scientist. Data science classes are offered in multiple formats and can teach you a variety of skills, such as how to use the most popular programming languages, statistical analysis software, querying, and database design.
Does a career or course in data science sound like the next step for you? Then research some of Noble Desktop’s data science courses. For busy students and professionals, you can take live online data science classes through Noble Desktop or one of its affiliate schools. You can also find in-person data science classes in a city near you.