Data Analyst vs Data Scientist.
Setting up a foundation for further analysis and study in this field!!
Hola!!, In this article, I am gonna give you a brief idea about the roles which you might have heard about or come across while applying/looking for the jobs. So, I am gonna help you out by introducing terms such as data analyst, and data scientist and also gonna explain the key differences between data analyst and data scientist. Let’s begin.
A data scientist can be a data analyst too but vice versa is not possible. Data science is a vast field that comprises of research, collection, processing, analysis, and visualization of the data. Data analyst is a subset of
Data Analyst:
Data analysts retrieve, gather data, and organize that data to reach meaningful conclusions and all so identify patterns and trends and present these insights to data scientists.
Data Analyst Role:
Let us look at the below diagram to get a better understanding of the data analyst role.
Data Analysts filters and extract relevant data from the data warehouse (Azure, AWS, Google Cloud Platform). After extracting data, they perform EDA (Exploratory Data Analysis) to gain insights in form and convert data into a structured form(graphs, reports, tables, etc.) for data scientists to build machine learning models.
Data Analyst Responsibilities:
- Extraction of data from primary and secondary sources using automated tools and techniques.
- Developing and maintaining database as well as converting data into readable format.
- Perform EDA (Exploratory Data Analysis) on the database to extract insights as well as prepare an abstract (summary) of the data.
- Defining data quality criteria.
- Performing data mining.
- Using statistical tools for analyzing, and interpreting patterns or trends is helpful in predicting and taking insights from the data.
Prerequisite Skills Required for Data Analysts:
- Strong Mathematical Skills for analyzing data.
- Strong hold on database programming languages such as Python, R, MATLAB, etc.
- Knowledge of data visualization software like Tableau, Qlik, PowerBi, etc.
- Experience with software like Excel, PowerPoint, MS Office, SPSS, etc.
- Problem-solving skills.
- Project management skills.
What is Expected from Data Analysts:
Data Scientist:
Data Scientist is a professional/expert who deals with unstructured and structured data by using more advanced data techniques to predict future outcomes based upon past patterns shown by the data. Data scientists also create their own machine learning algorithms for the predictive modeling process. In simple words, Data Scientist is a person whose role is to estimate the unknown, write algorithms and build statistical models also collaborate with stakeholders to identify business needs.
What is Expected from Data Scientists:
Data Scientist Role:
A data scientist is a professional/expert who requires enormous information to approach questions in view of logical abilities, measurable abilities and foster speculation, make derivations and patterns that are related to business issues, for example, customer patterns, market patterns, buying and selling pattern of a product as well as establishing a connection between datasets.
Data Scientist Responsibilities:
- Cleaning data
- Data mining
- Build models to work on big data
- Infer and Analysis of big data
- Deliver conclusions with actionable insights
Prerequisite Skills Required for Data Scientists:
- Machine Learning
- Mathematical Modelling
- Statistics
- Programming
- Database Management
- Deep Learning
- Big Data Knowledge
- Model Deployment
- Cloud Computing Services
Data analyst vs Data scientist:
Key differences between data analysts and data scientists are shown in the below diagram
Conclusion:
In the end, I would like to reiterate that a data analyst provides solutions to business problems and creates reports, graphs, and tables for the same, on the other hand, a data scientist creates business problems and also finds the most reasonable and feasible solution to it as it depends upon the data analysis and other information at each stage. Extracting insights from data is one thing, however, deciphering those insights to arrive at a business solution is totally different. Being a data scientist is totally not the same as being a data analyst and comes with more responsibilities. It is great to comprehend the entire image of a data analyst with the goal that you can climb the profession stepping stool whenever you need.
Finally…
I really hope this article has been a great read and a source of inspiration for everyone out their thinking to pursue their career as a data analyst or data scientist to develop and innovate.
Please Comment for suggestions and feedback. I am still learning. Please help me improve so that I could help you by upgrading my writing skills as well as knowledge and presenting myself to you in a much better way through my subsequent article releases.
The next article will be on the Difference between data analysis, data science, and machine learning.
Thank you and Happy coding :)
