With the recent surge in the data field, Data science and data analyst are become the talk of the town. These are two hottest tracks in the big data world. It is hard to know which is better: data science or data analysis.
I think there is a lot of confusion, let’s dig deep and understand the difference between data science and data analysis.
Let’s Jump in:
Involving automated methods to analyze huge amounts of data, Data Science creates new branches and influences areas of humanities and social science. It is already creating powerful new solutions and opportunities with data driven decisions. There is no surprise that data scientists are in high demand. From startups to fortune organizations, this role has a hot commodity. It also brings scores of benefits with it. Want to know, what they are? Let’s have a glance one by one.
To learn more about data science, read our blog on – What is data science?
- Certifies you with in demand big data technologies
- A door to better career path in the leading locations of the world
- Data science course gets you prepared to be in the top fortune organizations
- Directs the action based on trends which in return help defining goals
- Identifies and refines target audiences
- Qualifies to occupy new positions
Disadvantages Of Data Science
- Data science is great but not a decision maker
- Performance totally depends on the quality of leader
- A lot to learn in short amount of time
- Life in a constant uncertainty
Data analysis encompasses algorithms and mathematics formulas while working with data. There are many types of data analysis that defines the nature of data being analyzed. Data can be gathered by several methods like audio recordings, interviews, file notes and more. Data analysis can help and take the business intelligence to the next level. Data analyst role requires high technical skills to focus on statistics, formulas and complex databases to analyze data like OLAP, SQL, Data mining, etc.
- Helping organizations to spend their advertising budget with maximum effect
- Enabling the implementation of preventative measures
- Increasing flexibility in order to react to change in the business and the market
- Identifying the important trends and business problems
- Providing better awareness regarding the habits of audience
- Building better business relationship
Also Read>> Data Science vs. Big Data vs. Data Analytics
Disadvantages of Data analysis
- Not figuring out statistical importance
- Analyzing data is a tedious and complicated process
- Unwanted traffic removing and having clear data
- Unable to measure accuracy
- Letting bias influence your results
Also Read>> Online Learning Vs Traditional Learning!
“A data analyst role is to address business problems but data scientist not only addresses problems but take up the issues that have the most business value.”
“Data scientist needs skills like java, MapReduce and python whereas Data Analyst requires deep understanding of SQL and analytics.”
“Data analyst job requires looking at the known from the new perspectives while data scientist job requires estimating the unknown.”
Regardless of the difference between a data science and a data analysis, both have some pros and cons. It is time to get started with the certified project based training by NaukriLearning to gain strong skills to convert data into a business story.