Big data and data science professions have long been a solid pick for those searching for a long-term career path. As AI and Machine Learning grow more incorporated into our daily lives and economy, this tendency is expected to continue. As firms around the world seek to make the most of data, these are the two most popular career titles in this field. Data Science Jobs and Data Analytics are a jumble of concepts that intertwine and overlap but are still distinct from one another.
Data Science vs Data Analytics — The Skills
It is not necessary to have an engineering degree to become a data analyst, although having good abilities in statistics, databases, modelling, and predictive analytics is a plus.
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Data Analytics — Intermediate statistics knowledge and problem-solving abilities are required. |
Dexterity with Excel and SQL databases for slicing and dicing data |
| Working with BI solutions for reporting, like as Power BI, is a plus. | |
| Knowledge of statistical software such as Python, R, or SAS. | |
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Data Science — Advanced Statistics, Machine Learning, Math, Predictive Modelling, and programming are all topics covered in this course. |
Expertise in big data tools such as Hadoop and Spark. |
| SQL and NoSQL databases, such as Cassandra and MongoDB, expertise. | |
| Programming languages such as Python, R, and Scala require dexterity. | |
| QlikView, D3.js, and Tableau are examples of data visualisation tools. |
Similarities Between Data Science and Data Analytics
In many ways, data science and data analytics are similar topics. Both employ data to gain a better understanding of an organization’s activities, which aids decision-making. Both areas are primarily STEM-focused and in great demand across a wide range of sectors. Here are some examples of how the two fields intersect.
Massive quantities of data
Data scientists and data analysts work with massive data sets including millions of data points. These enormous databases may contain low-quality data that must be wrangled (cleaned), maintained, and organised in order for accurate analysis to be performed.
Technical skills
Both disciplines necessitate programming abilities (e.g., R, Python, Tableau, and SQL), as well as knowledge of statistics, Excel, and data visualisation and modelling. Professionals in both sectors must be very analytical and approach problem-solving and project management in a methodical manner.
Communication skills
Data scientists and analysts collaborate with colleagues from many disciplines, many of whom are not tech savvy. It is the responsibility of professionals in these professions to convey their findings in a clear and effective manner.
Differences between data science and data analytics
The most significant distinction between data science and data analytics is their scope. Even though they deal with the same data sets, a data scientist’s responsibility is much broader than that of a data analyst. As a result, many data scientists begin their careers as data analysts.
Here are some of the differences between these two roles.
Responsibilities
Modeling data allows data scientists to make predictions, spot opportunities, and support strategy. They make use of data to forecast the future. The data analyst’s job is to identify trends and address problems. They use the data as a snapshot of the current situation.
Database Manipulation and Management
Algorithms, data science and machine learning are used by data scientists to better how data supports business goals. Data analysts gather, store, and preserve data, as well as analyse the findings.
Questions and Answers
Data scientists identify the queries and develop the most efficient method for obtaining answers. Data analysts are tasked with answering questions by analysing data.
Data Science vs. Data Analytics: Career and Salary Outlook
The demand for data scientists and related vocations is predicted to expand by more than 30% between 2019 and 2029, according to the US Bureau of Labor Statistics (BLS). Data scientists and data analysts work in a wide range of sectors and vocations.
Data Scientists
Here are a few instances of the functions that data scientists can play. They work in a variety of industries and are in charge of a company’s strategy and decision-making.
- Actuary: Actuaries, also known as the “first data scientists,” assess risk using financial, statistical, and mathematical methods. According to the Bureau of Labor Statistics, the median wage for actuaries was $111,030 in 2020.
- Computer Systems Analyst: Computer systems analysts collaborate closely with management and IT to solve problems, detect patterns, and provide recommendations to senior leaders using data analysis. According to the Bureau of Labor Statistics, the median wage for computer systems analysts was $93,730 in 2020.
- Pricing Analyst: Pricing analysts test pricing models and give suggestions using data modelling and algorithms. As of April 2022, the median salary for pricing analysts was little over $58,000, according to PayScale.
Data Analysts
Some examples of data analyst jobs are shown below.
- Management Analyst: Management analysts look at financial and operational data to see where improvements might be made. According to the Bureau of Labor Statistics, the median wage for business analysts was $87,660 in 2020.
- BI Analyst: Data modelling and advanced data science approaches are used by business intelligence analysts, also known as operations research analysts, to turn data into actionable insights for businesses. According to the Bureau of Labor Statistics, the median wage for operations research analysts was $86,200 in 2022.
- Financial Quantitative Analyst: Quantitative analysts, sometimes known as “quants,” are best recognised for developing the algorithms that drive stock trading, but they also supply data to assist strategic business choices in a variety of industries. According to the Bureau of Labor Statistics, the median wage for financial analysts was $83,660 in 2022.
Conclusion
Demand for data scientists and analysts continues to rise as more businesses appreciate the importance of understanding and managing the data they generate. Students who are interested in a career that involves data modelling, statistics, programming, and other analytical skills are likely to have come across degree programmes and job postings focusing on data science or data analytics. Despite the fact that both data science and data analytics require working with and altering data, the two fields are not interchangeable.
