Jobs that are related to data science are all the rage these days. There are job openings on various online portals for data scientists, data analysts, data engineers and so on. All of these various data job titles sound similar, but there are some key differences between them. If you have wondered what the difference is between a data scientist and a data analyst, this article should help to clarify things.
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What Is a Data Scientist?
A data scientist has an expert grasp of large amounts of data from the point of view of business. A data scientist is charged with helping businesses to make more accurate and profitable decisions. Data scientists have a good foundation in computer applications, statistics and math and modeling. They design and construct new processes for modeling data and production with prototypes, predictive models, algorithms and custom analysis.
What makes them unique is they have a great grasp of data and business and also excellent communication skills so they can deal effectively with both IT and business leaders. Data scientists are efficient in picking the right problems and being able to add value to the company after they resolve that problem. See full salary outlook for data scientists.
Some of the necessary requirements for a data scientist are:
- Being familiar with major database systems, such as MySQL and Hive.
- Also should know Java, Python, and MapReduce job developments
- Should have a good grasp of various analytical functions such as median and rank, and how to use them with data sets.
- Important to be highly skilled in mathematics, statistics, data mining and correlation. Should understand predictive analysis for making better predictions for business decisions.
- Should be able to make deep insights into machine learning, such as Mahout, Bayesian and Clustering
What Is a Data Analyst?
Data analysts are responsible for examining large sets of data to identify key trends, develop charts and to create visual presentations to help business managers to make better strategic decisions. They try to grasp the origin of data and any potential distortions by using advanced technology. Then, they collect and analyze data, identify correlations and patterns to draw helpful conclusions for the business. See full salary outlook for data analyst.
Some of the necessary requirements for data analysts are:
- Be familiar with data warehousing and business intelligence concepts
- Have an in depth exposure to SQL and analytics
- Have a strong understanding of Hadoop based analytics, including HBase, Hive, MapReduce, Impada and Casscading.
- Be skilled with data storage, retrieving and tools
- Be highly skilled with data architecture tools and components
- Be familiar with the major ETL tools, to transform different types of data into analytics data stores. Also should be able to make critical business decisions on data in real time.
How a Data Scientist Differs from a Data Analyst
A data analyst must deal with many of the same activities as the scientist, but the leadership aspect is different.
For example:
- A data scientist is usually expected to form the questions that help the business and then proceed to solving each of them. A data analyst is usually provided with questions by a business team, and then tries for a good solution based upon this guidance.
- Both the data scientist and analyst are supposed to write queries and work with engineering teams to find the right data, as well as do data munging and get key information from massive amounts of data. But in most cases, the data analyst is not relied upon to build statistical models, or to be as hands on in advanced programming and machine learning. Rather, the data analyst normally works on simpler SQL or such databases, or with other BI packages and tools.
- The data scientist also needs to have strong data visualization skills and be able to convert data into a ‘business story.’ A data analyst usually is not tasked with transforming data and analysis into an actual business roadmap.
Choosing Between a Data Scientist and Data Analyst Career
To determine which is the best career path for you, consider these major factors:
- Educational and professional background
- Personal interests
- Your desired career path
Background
Data scientists and analysts are similar in many ways, but differences are rooted in their educational and professional backgrounds. Data analysts review large sets of data to see trends, devise charts and create presentations visually to assist businesses to make better decisions. They usually have a bachelor’s degree in technology, bachelors degree in engineering or bachelors degree in math, and may have experience in math, programming, science, databases and modeling.
Data scientists are responsible for designing and constructing all new processes to model data and production. In addition to the interpretation and performance of data studies and product experiments, they also are required to develop algorithms, predictive models and custom analysis.
Data scientists use many techniques to go through data, such as machine learning and artificial intelligence and data mining, which is a major difference between the two roles. Because of this higher level responsibility, most data scientists have a master’s degree or Ph.D. Data scientists are much more grounded in technical science and mathematics, and usually have a stronger background in computer science.
Interests
It is important to ask yourself if you like numbers and statistics primarily, or do your passions extend to business and computer science?
Data analysts are obsessed with numbers, statistics and programming. They are the gatekeepers for data in their organization, and they work heavily in databases to find data points from disparate and complex sources. Data analysts should really understand the specific industry in which they work.
Data scientists need to have interest and skill in math, statistics and computer science, but they also need to understand the world of business. Data scientists need strong communication and presentation skills, and they must be able to find risks, trends and opportunities in the data. Then, they should be skilled in relaying the findings in layman’s terms to management.
Career Path
Data scientists and data analysts have different levels of responsibility and experience, and their compensation varies. Data analysts have a salary that range between $77,500 and $118,500. They are largely database professionals, and can increase their salary by learning new programming skills, such as Python and R.
But Payscale data shows that most data analysts have maxed out their salary potential after 10 years or so. It is common for a data analyst to obtain a master’s or Ph.D. degree and transition into a data scientist role. Data scientists usually have a more advanced skill set and education, and can earn compensation between $116,000 and $163,500 per year.
Data analysts and data scientists have similar sounding job title and have some of the same responsibilities, but there are major differences in duties, education and career trajectory. Fortunately, both job titles are in high demand in our growing economy, so either one can be a great choice.
References
- Difference Between Data Scientist and Data Analyst. (2017). Retrieved from https://www.edureka.co/blog/difference-between-data-scientist-and-data-analyst/
- Data Analyst Vs. Data Scientist – What’s the Difference? (2016). Retrieved from https://www.simplilearn.com/data-analyst-vs-data-scientist-article
- Data Scientist Vs. Data Analyst: What’s the Difference? (2017). Retrieved from https://www.northeastern.edu/graduate/blog/data-scientist-vs-data-analyst/
