Should I become a data scientist or a data analyst?
What should I do? Data analysis or data science? What’s the difference? These were a few questions I kept wondering when deciding my future path. These two are among the most demanded jobs in Information Technology. But it’s essential to realize the difference between these two. Let’s catch up with these two and decide which can be better for us.
Data Science:
Data science is a study where new data concepts and processes are developed using mathematics, programming, algorithms, and machine learning skills.
Data Analysis:
Data analysis refers to evaluating the available data and representing it in different forms using charts and graphs. They also work with cleaning and organizing data.
Why they are often compared?
These two areas of study are quite closely related as they both deal with data and its usage. They are rapidly growing in the information technology sector. They have different tasks and responsibilities towards this data which is why they are taken as two different career paths and high-demand career paths. Data scientists and data analysts required a few mutual skills such as programming and mathematics. They have different responsibilities corresponding to database manipulation and data processing.
Skills for a data scientist:
Mathematics: For a data scientist, a few mathematical domains such as Probability and Statistics and Linear Algebra are extremely important.
Programming: Programming languages play a major role in the tasks of a data scientist. A good proficiency in Python, Java, SQL, and MATLAB proves to be helpful.
Machine learning: Machine learning is used by data scientists to clean and interpret data and extract new ideas from it.
Data platforms: In addition to the above-mentioned skills, learning data platforms like Apache and Spark is necessary.
Skills for data analyst:
Programming language: An intermediate or expert-level proficiency in Python or R is encouraged for data analysts.
Excel and SQL: For managing, cleaning, collecting, and displaying data efficiently, knowledge of SQL and Excel is required in data analysis.
Professional tools: A strong or even medium command of software like Tableau and Power BI proves to be helpful in handling data.
Responsibilities:
The responsibilities of a data scientist and data analyst are compared as follows:
Which one to choose? Which is better?
Now this question can be answered only by yourself. What your interest is and what are your skills? While choosing a career, you can consider scope and salary within your community. Both skills are highly valued at the moment and take into account technical knowledge.
So are you ready to earn being a data analyst? Or maybe a data scientist!
