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Innovative technology has revolutionized data-gathering processes that businesses use for educated decision-making. Two of the biggest disciplines relating to data are Data Science and Data Analytics, which, while similar, feature distinct differences that are, in fact, complementary to one another.
Here’s a quick overview: Data Analytics is a subfield of Data Science. All facets of data processing—including collecting, modeling, and insights—are referred to as Data Science. Meanwhile, Data Analytics focuses primarily on statistical analysis, mathematics, and statistics.
If you’re thinking of pursuing any of these two areas of study, we’ve got you! Let’s explore how Data Science compares with Data Analytics.
Comparing Data Science and Data Analysis
| Data Science | Data Analytics | |
| Average Salary (yearly average) | $103,500 | Research analyst: $85,720 Computer systems analyst: $102,240 |
| Job Growth (2022-2032) | 35% or 17,700 openings per year | 8% or 10,200 vacancies annually |
| Why the Degree or Profession Matters | * Highlights operational issues and gaps that might otherwise go undetected * Offers an increased understanding of consumer feedback, corporate procedures, and purchasing decisions | * Uses findings to guide businesses in making critical decisions * Allows businesses to determine trends that can revolutionize their operations |
What is Data Science?
In Data Science, data is analyzed to gain insight into the business world. Besides analyzing massive amounts of data, this technique combines ideas and methods from statistics, computer engineering, math, and AI. Data will answer questions about where things happened, why they did, and what reasoning is behind them.
Education and Career Paths in Data Science
A Data Science bachelor’s degree is your starting point for learning to handle unstructured data, solve complex issues, and provide suggestions based on those data. For skills advancement, you can pursue a Master’s in Data Science degree emphasizing statistical and computational skills that are applicable to any industry.
With a Doctorate in Data Science, graduates gain advanced skills for becoming data scientists who take on top-level or executive positions in private or government organizations, perform scholarly research in the field, or teach the subject in higher education institutions.
Data scientists are also expected to have a basic knowledge of Database Management and Architecture, which is involved in the design and implementation of operational databases.
Data Science graduates can also explore these job prospects:
- Business Intelligence Developer
- Machine Learning Engineer
- Data Engineer
- Statistician
A Data Science Career is for you if:
- You are deeply interested in statistics, data analysis, computer programming, predictive modeling, economics, and calculus.
- You find machine learning and artificial intelligence fascinating.
- You have above-average skills in mathematical or statistical figures.
A Data Science Career is not a great fit if:
- You can’t stand being in front of your computer, gathering or collecting information for analysis.
- You’re not open to lifelong learning or upskilling, which is crucial in the field of Data Science.
What is Data Analytics?
In data analytics, unprocessed data is evaluated to derive informational inferences. There are a number of automated data analytics methods and procedures that operate on raw data in order for humans to use them.
A corporation may increase profitability, make informed decisions, and enhance performance thanks to the use of data analytics. Data Analytics involves methods and procedures to make data easy to interpret and apply.
Education and Career Paths in Data Analytics
A Data Analytics degree focuses on data optimization, data mining, or simulation. It goes beyond the data gathering and organizing phase; you extract, identify, characterize, and analyze data to help explain or predict future trends.
A Bachelor’s in Data Analytics gets you started. At the undergraduate level, Data Analysis entails the basic assessment and application of data to real-world scenarios. Such data will be used to counsel corporate decision-makers.
With a Master’s in Data Analytics, you can become skilled in cutting-edge technologies such as Python, SQL, and R. The goal is to harness the power of data and convert business intelligence into business insights.
A PhD in Data Analytics provides data analysts and data scientists with specialized education and training for analyzing large data sets. Their specialized skills are particularly useful in business, social, health, and industrial scenarios.
Besides snagging a Data Analyst job, you can be any of these experts:
- Data Architect
- Data Scientist
- Database Administrator
- Data Engineer
A Data Analysis Career is for you if:
- You are fundamentally drawn to jobs that require analyzing information or data.
- You like interpreting information and transforming it into useful resources.
- You are organized and patient.
- The idea of helping businesses manage and predict trends for success interests you.
- You don’t mind elevating your analysis and technical skills.
A Data Analysis Career is Not A Great Fit if:
- You’re not up for the pressure and stress that comes with dealing with data.
- You’re not passionate about obtaining highly technical skills.
- You cannot commit to constant upskilling in a volatile and fast-advancing industry.
Typically, data scientists and data analysts work together to achieve shared business goals. Rather than focusing on frequent reporting and routine analysis, a data analyst could focus on frequent reporting and routine analysis. A data scientist, meanwhile, is expected to develop methods for storing, managing, and analyzing data.
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