Trying to cross both disciples and business, the world of using and knowing big data has a tremendous opportunity for those exploring a career in the field. However, as broad as this potential is, it is important to consider the various approaches to data studies to decide which one is right for you.
In the data world, terms such as “data analytics” and “data science” are also used interchangeably. To know the field is right for you, it is important to consider that these fields are different and what these differences mean in terms of context and potential for future career development.
|Data Science||Data Analytics|
|Data science is the study of where information comes from, what it represents, and how it can be turned into a valuable resource in the creation of business and IT strategies.||Data Analytics is the process of examining data sets in order to draw the conclusion about the information they contain, increasingly with the aid of a specialized system and software.|
|Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization’s competitive advantage.||Data analytics technologies and techniques are widely used in commercial industries to enable the organization to make a more informed business decision and by scientists and researchers to verify or disprove scientific models, theories, and hypotheses.|
|Data science is the house that holds the tools and methods.||Data analytics is a specific room in that house.|
|Data science often moves an organization from inquiry to insights by providing the new perspective into the data.||Data analytics sorts data into things that organizations they know or they don’t know and can be used to measure events in the past, present, and future.|
|In artificial intelligence and machine learning, a data scientist has a great role to play. For data scientists, knowledge of machine learning is a must. A machine is the most impressive development in the tech world.||Data analysts verify and improve existing theories or models. It also used in many industries to enable an organization to make better decisions.|
The two key distinctions between Data Science and Data Anlytics:
- Scope — Data analytics takes a narrow approach to data, looking into individual information, whereas data science aims at data at a global level, trying to understand the broader picture.
- End Goal — Data scientists admire their ability to think critically to develop and create replicable processes and data models; data analysts concentrate on extracting relevant, actionable knowledge for particular data sets to identify patterns and help their organization make smarter business decisions.
What is the best fit for your talents and strengths?
If you are new in terms of big data, you may have the idea that these professions are reserved for individuals with a deep background in mathematics and computer science. It appears to be happening for data science students who need specialized coding skills to design and build new models. They also have experience in machine learning, software development, and multi-language coding, and need an advanced degree in advancement in the field.
Data analytics, on the other hand, can become an exciting career path for any person interested in the intersection of science, business, and engineering, and must be able to learn the resources required to make substantive decisions on numbers.
Which is best suited to your narrow goals?
If people enjoy numbers and want the opportunity to turn big data into smart data, a career in data analytics could be the best professional choice to suit their interests. Data analysts work throughout the lifecycle of data searching for additional actions to solve specific business problems. This implies anything from capturing, handling, storing, and transforming the data to analyzing and presenting data in a visually pleasing and intuitive manner. The information they obtain could be used to make decisions within their organization, also increase the effectiveness of the activity.
Although data scientists often work inside massive data sets, the outcome focuses more on technological skills, building tools, systems, and structures to help solve theoretical situations and problems on a larger scale. Data scientists have a broad background in mathematics, statistics, and statistical methods, allowing them to assess which methodology is most suitable for a given collection of data and analytical goals, and are centered on asking these questions to lead them to other avenues of research.
A graduate degree in data science or data analytics provides excellent job opportunities. The latest industry studies show the following:
- The availability of such talent is increasingly rising in the market for these technical skills.
- The number of positions available in these areas began to expand significantly.
- These career options provide competitive opportunities for earnings.
Jobs Salaries and Career after Masters in Data Science - Updated 2020
Best Data Science graduate certificate programs - Updated 2020
Best Graduate Programs in Data Science in Virginia - Updated 2020
Best Graduate Programs in Data Science in Massachusetts - Updated 2020
Find Data Science masters degree program based on GRE score
Search for Graduate Programs in Data Science
What are the admissions requirements for graduate program in Data Science?
Are the admissions requirements for masters degree programs in Computer and Information Sciences different than any other program?
What is a good GRE score required for the master's degree program at Temple University for Computer and Information Sciences?
What is the GRE score required for admission to Master's Degree Program in Data Science at Georgia Institute of Technology-Main Campus?
Find Data Science Masters Degree program from GRE score