Data Scientists Vs Data Analyst

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In this tutorial, we are going to discuss what are the main differences as well as similarities between data scientists and data analysts. We will also look into what a data scientist and data analyst do, their education and work experience,  what are the required skills for both data analysts and data scientists, and a detailed comparison of their skills, work experience, salary, and education qualification.

Data scientists are the experts who will use their technical skills in order to solve problems that are very complex. They will also use algorithms,  technical skills, and scientific methods in order to extract useful information or insights from both structured as well as unstructured data. Data scientists use data very carefully in order to make better and more suitable business decisions. Where a data analyst mainly deals with cleaning data, transforming data, generating inferences, and solving problems that means identifying the problem is not done by a data analyst they will only solve the existing problem. but a data scientist will first identify the problem then they will solve the problem. An important skill required for a data scientist is communication skills and it is not necessary for a data analyst. 

What does a data analyst do? 

A data analyst will collect data from different sources and organize the collected data. After organizing the data they will do the analysis.

1. Define the problem: They mainly determine the needs of a customer and generates plan according to the need of a customer. Finally, they communicate the plan with their team.
2. Data collection: data are collected from multiple sources such as database backups, flat files, and APIs. Data analysts work with the programmers in order to create the ETL process. The final step done in data collection is data aggregation.
3. Data cleaning: Raw data is always messy so in order to use data cleaning is very necessary. Cleaning data make it more useable. Normalization and standardisation are done on the data after data cleaning. Finally, data validation is done.
4. Report delivering, examining the patterns, engaging and collaborating with stakeholders,  finally they will consolidate the data and infrastructure is made out that these are the main roles done by a data analyst. 

How data analysts do collaborate with stakeholders?

One of the important roles, as well as responsibilities of a data analyst, mainly includes collaborating and engaging with salespeople and marketers who are there within the organization. This means data analysts should engage as well as collaborate with the different departments of a company. A data analyst should cooperate with peers who work in other departments.

What do data scientists do?

The data model process of designing, and creating suitable algorithms and predictive models is mainly done by a data scientist. Hence each and every data scientists spend more time designing tools, finding out useful insights from the data, and identifying the business problems and data frameworks.

When we compare a data scientist with a data analyst they will always find out new tools or will develop new tools as well as methods in order to solve complex problems faced by a particular company in their business sector. They will always identify and solve business problems by extracting useful insights from the data with the help of predictive models.  Identifying the problem is the very first thing done by a data scientist and after that gathering and data analysis is done in order to extract useful insights from the data.

Hence data scientists mainly identify the business problems,  gather and analyze data, extract useful insights from data, communicate the findings with higher authority as well as with their colleagues, and design data model processes. They will use their technical knowledge, mathematical skills, and coding skills in order to solve as well as extract useful insights from data.

What are the differences as well as similarities between data analysts and data scientists?

• Programming is done by both data scientist and data analyst but a data analyst will only do basic programming and a data scientist will always do advanced programming.
• When data scientists do predictive modelling which means predictions are made about the future events which are unknown, the data analyst will do static modelling. Static modelling is mainly used to specify the object's structure which is there within the problem domain.
• A data scientist should be familiar with database systems for example MSQL, HIVE, python,  java etc and a data analyst should be very familiar with the concepts like data warehousing and business intelligence and they should also have a strong base of Hadoop based analytics such as HBase, Hive, MapReduce jobs,  cascading etc.
• A data scientist should have strong fundamentals of maths whereas a data analyst should have strong statistical skills.
• Programming is used by both data scientists and data analysts mainly for cleaning, transforming and analysing data.
• If someone wants to start their career in analytics then the data analyst role is more suitable for them and if someone wants to ease the tasks of humans by creating advanced machine learning models and deep learning techniques then data scientist is the better option for them.
• Based on the past patterns data scientists can predict the future of the business whereas data analysts cant do that. For both at least a bachelor’s degree in the quantitative field (mathematics, computer science or statistics) is necessary.
• Data visualization, statistics, math, data mining and data warehousing are the common skills used by both data scientists as well as a data analysts.
• Both are working with the data sets. But the only difference is both are using different tools and using different skills in order to solve the business problems.

     

Educational requirements needed: data scientists and data analysts.

Educational requirements which are needed for a person to become a data analyst is a bachelor's degree mainly in the fields like statistics, mathematics,  computer science or finance.

If one has a master's degree in data science, information technology, mathematics or statistics they can surely work as a data scientist. 

 Apart from this if one can earn a professional certificate from IBM or Google in data analytics and if you are skilled enough to acquire all the basic needs required for a data analyst you will be able to work as a data analyst for certain companies. A data analyst can become a data scientist if he/ she is ready to study more or ready to gain the required skills which are needed to become a data scientist.

Skills needed for data scientists as well as for data analysts

 Both should have great knowledge of mathematics. The data scientists should be experts in advanced statistics and predictive analytics whereas a data analyst should have great knowledge in foundational mathematics and statistics. Software and tools used by a data analyst are SAS, Excel, and business intelligence software.
Hadoop, MySQL, TensorFlow, and Spark are the tools used by data scientists. Analytical thinking and data visualizations are the other skills required for a data analyst. Machine learning, as well as data modelling, are the other skills required for a data scientist. Programming skills required for a data analyst are basic fluency in R, Python, and SQL. Advanced object-oriented programming is the main programming skill required for data scientists.

Responsibilities of a data scientist and data analyst 

Data Scientist	Data Analyst
To discover new opportunities they will use current data. Machine learning models, as well as analytical methods, are developed. Detailed data cleaning is done by a data scientist A/B testing is conducted	In order to solve a problem, they will use pre-existing data. Create reports and dashboards. Very basic data cleaning is done by a data analyst. A data analyst will help to collect incremental data from new sources.

Qualification and skills

Data Scientist	Data Analyst
Masters degree or higher is required  For some positions PhD is must Degrees mainly in computer science, statistics, mathematics, economics, physics, and machine learning is required. Skills required: Great knowledge in  SQL R/Phython, pandas,Numpy,tensorflow NLP Apache spark SAS/SPSS	Bachelor's degree or higher is preferred For some positions master's required Degrees mainly in computer science, statistics, mathematics, economics, and physics is required.  Skills required- Great knowledge in SQL R/Python Data modelling   ExcelAWS/Azure