Data scientists and data analysts are the perfect career option for someone who possesses an analytical blend of minds and loves decoding data. Both these job profiles are the most sought-after and well-paying careers in the tech industry today. The data scientists and data analyst jobs are in demand, and it has been projected that these jobs are here to stay and grow in the future.
Most of us are well aware that these two job profiles are among the industry’s most lucrative ones today. However, if you ask about the difference between the two, then the majority of them would falter. Even people who possess some knowledge of data scientists and data analyst job profiles would be confused if asked to explain the differences between the two.
Data scientists and data analysts both work with data. The difference between data analysts vs. data scientists lies in what they do with the data. Read below to understand the key differences between data scientists and data analysts.
Let us start with the definition of the two profiles.
A data analyst will sift through huge amounts of data and try to identify a trending pattern. He will analyze the story that the data wants to tell. He then tries to draw out some decisions based on the insights that he gets from the data. The data analyst will use visual representations like graphs and charts to showcase what the data reveals. Sifting through data, the data analyst will make reports to explain the hidden insights in the data. So if there is someone in your company who tries to explain queries using charts and other graphical representations, they are the data analysts.
Data scientists are professionals who are a pro at interpreting data. They also have mathematical modelling and coding expertise. The data scientists have an advanced degrees. He would have moved the career ladder chain from a data analyst to a data scientist in most probability. The data scientists would have started as data analysts. They also have additional expertise in machine learning and advanced programming skills. They are well-versed in creating new data modelling processes.
Data scientists make use of predictive models, algorithms, and much more. The data scientists will analyze the data and garner insights that they can take action on. They then share their insights with the company management.
In a nutshell, a data scientist is the one who collects, cleans, and mungs data because data is never sorted when in the raw form. So basically, a data scientist is one who does not just collect data but also builds patterns, and algorithms design the experiments and share the results of data with their members in a digestible format.
A data analyst gathers data, organizes it, and reaches an insightful conclusion with it. Every industry benefits from data analyst work, be it retail, healthcare, or any other industry. The data analyst will develop new processes and systems to collect and compile data and draw business conclusions.
A data analyst’s role is to deliver reports, examine patterns, collaborate with the stakeholders across various departments, consolidate data, and set up the infrastructure. Consolidating data is the key work of a data analyst.
A data scientist is someone who helps a company to benefit from data. Data scientists are experts at data science, statistics, R programming, Big Data, SAS, and Python. They are some of them enjoy the best salaries in the industry.
Data scientists are problem solvers who determine the questions for which answers are sought. They come up with various solutions to try and solve a problem. Some of the tasks that a data scientist tries to tackle are pulling our data, merging, and analyzing it. They look for patterns or data trends. The data scientist makes use of various tools like Python, Tableau, Excel, Pyspark, Hadoop, etc., to test the algorithms.
In the data scientist roles, they try to simplify data problems and then develop some predictive models. The data scientist will build the data visualization and then write the results and pull out their concepts.
Now that we are clear about the data scientists’ job roles and responsibilities, the data analysts let us quickly go through the differences between data analysts vs. data scientists.
The difference between a data analyst and a data scientist is that a data scientist is higher than a data analyst. The data scientist needs to have a lot of expertise in data science, which is not wanted in data analysts. The scope of work of a data analyst is at the macro level. The data scientists deal with data at the macro level.
Data analysts are beneficial in industries like gaming, healthcare, and travel. Data science is used in digital advertising and internet searches.
The data analysts need to know the BI tools and only a basic level of understanding of statistics. Broadly if you notice, then the data analyst and the data scientists are a similar kind of profile, with data scientists being a notch over the data analyst profile. Both these job roles are inevitable to an organization, and businesses need to invest in both these fields to sustain themselves in the market.
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