Is there a difference between Bsc data science and Bsc data analytics? If so , whats the difference between them and which cousrse is good to study and has a good scope

hello,

while many people use the terms data sciences and data analytics changeably, there is a major difference between them. both of the functions of these two professions are highly interconnected, though. here are some of the major differences between the two:

• scope: macro for data science, micro for data analytics.
• goal: the goal of a data scientist is to ask the right questions , and for a data analytics professional is to find actionable data.
• areas of work: machine learning, artificial intelligence, search engine engineering analytics, corporate analytics are the major fields of data science. healthcare, gaming, industry, travel are the major fields for data analytics professionals.
• both of these professions require the use of big data analysis.
• average starting salary for a data scientist is 6-10 lakhs per annum. in data analytics, this value is 4-8 lakhs per annum.
• data scientists gather large amounts of structured/unstructured data and convert it into actionable insights. they identify the data analytics techniques that are responsible for the growth of organizations. however, data analysts work in  analyzing business data to identify correlations among them and discover valuable patterns from different data points. They also work with customer algorithm models and personalize them to fit  customer requirements.

Thankyou.

Data Analytics vs. Data Science

While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists , on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Working in Data Analytics

The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems . They analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs: e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue, etc.

Data analysts have a range of fields and titles (https://www.northeastern.edu/levelblog/2016/04/06/what-roles-do-data-analysts-have/) , including (but not limited to) database analyst, business analyst (https://www.northeastern.edu/graduate/blog/what-does-a-business-analyst-do/) , market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients (https://www.northeastern.edu/graduate/blog/blog-how-to-tell-stories-with-data/) .

Typical Background

Data analysts can have a background in mathematics and statistics, or they can supplement a non-quantitative background by learning the tools needed to make decisions with numbers. Some data analysts choose to pursue an advanced degree, such as a master’s in analytics (https://www.northeastern.edu/graduate/program/master-of-professional-studies-in-analytics-boston-14859/) , in order to advance their careers.

Skills and Tools

Top data analyst skills (https://www.northeastern.edu/graduate/blog/data-analyst-skills/) include data mining/data warehouse, data modeling, R (https://www.northeastern.edu/graduate/blog/r-vs-excel/) or SAS, SQL (https://www.northeastern.edu/graduate/blog/sql-data-analyst/) , statistical analysis, database management & reporting, and data analysis.

Roles and Responsibilities

Data analysts are often responsible for designing and maintaining data systems and databases, using statistical tools to interpret data sets, and preparing reports that effectively communicate (https://www.northeastern.edu/graduate/blog/communicating-with-data/) trends, patterns, and predictions based on relevant findings.

Working in Data Science

Data scientists, on the other hand, estimate the unknown by asking questions, writing algorithms, and building statistical models. The main difference between a data analyst and a data scientist is heavy coding. Data scientists can arrange undefined sets of data using multiple tools (https://www.datascienceweekly.org/articles/what-tools-do-employers-want-data-scientists-to-know) at the same time, and build their own automation systems and frameworks.

Typical Background

Drew Conway (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) , data science expert and founder of Alluvium, describes a data scientist as someone who has mathematical and statistical knowledge, hacking skills, and substantive expertise. As such, many data scientists hold degrees such as a master’s in data science (https://www.northeastern.edu/graduate/program/master-of-science-in-data-science-5256/) .

Skills and Tools

These include machine learning, software development, Hadoop, Java, data mining/data warehouse, data analysis, python, and object-oriented programming

Roles and Responsibilities

Data scientists are typically tasked with designing data modeling processes, as well as creating algorithms and predictive models to extract the information needed by an organization to solve complex problems.