The positions of data scientists and data analysts were ranked first for rising demand across industries in the World Economic Forum’s Future of Jobs Report 2020. Big data and AI professionals closely followed them. If you’re interested in a job working with data, statistics, and numbers in general, you might want to consider either becoming a data analyst or a data scientist.
Do you know how they differ, though? Two of the most sought-after, well-paying careers in 2021 are data analysts and data scientists. In this post, we’ll examine the variations and trajectories for each field – data scientist vs. data analyst.
The following is a short comparison table in a nutshell.
|Data Analyst||Data Scientist|
|Meaning||A Data analyst is a professional who gathers all the data from various resources, compiles them, and analyzes them.||Data Scientist is a skilled person who forecasts challenges and opportunities for the business.|
|Education||No specific degree is required. But a degree in data analysis is preferred.||A bachelor’s or master’s degree in Statistics and Computer Science is required.|
|Programming Languages||MySQL, Python, R, Java, and SAS||Python, SAS, and R|
|Major Skills||Statistics, Data Visualization, Spreadsheet, and Machine Learning.||Mathematics, Calculus, Machine Learning, Statistics, and Cloud Computing.|
Difference Between Data Analyst And Data Scientist: What Do They Do?
Let us have a look a deep look at the following:
A data analyst basically aids the company in decision-making by gathering information and spotting various trends in it. The main purpose of data analysis is to provide analysis in a statistical form that provides solutions to various issues and queries.
A data analyst makes queries to relational databases using techniques like SQL. Working to identify informational needs, gathering data from sources, data cleaning and re-organizing for analysis, and examining data sets that can be translated into useful insights are some typical tasks for a data analyst.
On the other hand, a Data Scientist will often be more involved in developing algorithms, prediction models, and data modeling workflows. Creating tools, automation systems, and data frameworks might be some ways in which data scientists devote most of their time.
Additionally, frequently cope with uncertainty by employing more sophisticated data approaches to generate future forecasts. This position is typically viewed as an improved version of a data analyst.
You are now prepared to understand the distinction between a data scientist and an analyst after learning about their respective fields of expertise and effective insights. A data scientist may design how data is stored, handled, and analyzed, but a data analyst may spend more time on routine analysis and produce reports regularly.
An analyst often concentrates on providing detailed answers regarding the company’s operations. To create innovative approaches for posing and responding to significant problems, a data scientist may operate at a more macro level.
Data Analysis vs. Data Science: Education
A bachelor’s degree in the fields like mathematics, statistics, computer science, or finance is typically needed for data analyst positions. More often, Data scientists hold a master’s or doctoral degree in data science, information technology, mathematics, or statistics, as do many advanced data analysts.
Even though a degree has historically been the primary entry point for a job in data, new opportunities are increasingly becoming available for those without a degree or prior experience. By earning a Professional Certificate in data analytics from Google or IBM, you can acquire the knowledge necessary for entry-level employment as a data analyst in less than six months of study.
Data science vs. Data analysis: Skills
Both Data Analysis and Data Scientist require a huge amount of dedication and focus in the field.
A Data Analyst is a professional who collects, processes data, and performs statistical analysis to extract valuable insights for the business. They prepare charts, graphs, and diagrams to assess and communicate the data to business owners.
Some skills required by Data analysts are:
- Knowledge of data visualization tools like Power BI, Tableau, and D3.
- Programming languages like MySQL, Java, and Python
- Advanced Spreadsheet knowledge.
- Machine learning
- Skills like communication, presentation, and critical thinking.
The primary role of the Data Scientist is to extract insights from interpreting and analyzing data. They use their statistical and computing techniques to make algorithms and models to interpret data for future business decisions. And communicate and collaborate for the same with different departments and stakeholders.
There are a few skills that a Data Scientist requires are:
- Strong knowledge of Mathematics, Calculus, and Statistics.
- Knowledge of certain programming languages like Python, SAS, and R.
- Cloud computing
- Machine learning
- Communication and Leadership skills
Data Analysis vs. Data Science: Salary comparison
Now, let’s have a closer look at the salary of Data Analysis and Data Science.
The average compensation for Data Analyst is around $70,000 per year. It is forecasted that the employment rate for Data Analysis is expected to grow 23% from 2021 to 2031.
In comparison, the average salary of a Data Scientist is $120,000 per year. Meanwhile, data science is projected to boost by 36% between 2021 to 2031.
Related Reads – How to Become a Financial Analyst
It is preferable to take on an entry-level data analyst position if you wish to begin a career in analytics. This will enable you to gain experience using actual company data to generate insights. To query databases, create reports using BI tools, and evaluate crucial data, you will make use of your current expertise.
Nearly every industry – including healthcare, e-commerce, manufacturing, logistics, and others – uses data science. Data scientists are in short supply internationally, and businesses are looking for experts who can use data to drive important decisions and corporate growth. Companies recognize a lack of qualified data scientists for this position, making it difficult for them to create algorithms and predictive models. Hopefully, this article would have helped to clear any confusion regarding the two highly sought-after professions today.
It solely depends on your choice. If you are a person who loves Mathematics, Calculus and is interested in programming, machine, and learning, then data science is a perfect career option for you. But if you are interested in data interpretation, statistical modeling, and structuring, then data analysis is excellent.
Basic coding knowledge is required and favored to analyze and interpret the data.
Yes, mathematics and calculus are integral parts of Data Science. Moreover, one must have a solid background to practice Data Science.
Data Scientists have a higher pay as compared to Data analysts. The average salary of a Data Scientist is around $120,000, and the average salary of a Data Analyst is $70,000.