Data Science vs. Business Analytics

Key Differences Between Data Science and Business Analysis:

Here are some of the key differences between data scientists and business analysts.

1. Data science is the science of studying data using statistics, algorithms and technologies, and business analysis is the statistical study of business data.

2. Data science is a relatively recent development in analytics, but business analytics has existed since the late 19th century.

3 Data science requires a lot of programming skills, but business analysis doesn't require a lot of programming.

4. Data science is an important subset of business analysis. Therefore, anyone with data science skills can do business analysis, but not vice versa.

5. Taking data science one step ahead of business analysis is a luxury. However, business analysis is needed for companies to understand how it works and gain insights.

6. Analytical Data Science results cannot be used for everyday business decision making, but business analysis is essential for critical administrative decision making.

7. Data science does not answer obvious questions. Questions are almost common. However, business analysis mainly answers very specific questions about finance and business.

8. Data science can answer questions that can be used for business analysis, but not the other way around.

9. Data science uses both structured and unstructured data, while business analytics primarily uses structured data.

10. Data science has the potential to make a big leap, especially with the advent of machine learning and artificial intelligence, while business analysis is still slow.

11. Unlike business analysts, data scientists don't come across a lot of dirty data.

12. In contrast to business analysis, data science relies heavily on data availability.

13. Investing in data science The cost of is high and business analysis is low.

14. Data science can keep up with today's data. Data is growing and diverging into many data types. Data scientists have the necessary skills to handle it. However, commercial analysts do not own it.


Data Science and Business Analytics Comparison Table

Below is the comparison table between Data Scientist and Business Analytics.

Comparison base	Data Science	Business Analytics
Coining of Term	In 2008, DJ Patil and Jeff Hammerbacher from LinkedIn and Facebook, respectively, invented the term Data Scientist.	Since Frederick Winslow Taylor's implementation in the late 1800s, business analytics has been in use.
Concept	Data inference, algorithm development, and data-driven systems are all interdisciplinary fields.	To derive insights from business data, statistical principles are used.
Application-Top 5 Industries	·         Technology ·         Financial ·         Mix of fields ·         Internet-based ·         Academic	·         Financial ·         Technology ·         Mix of fields ·         CRM/Marketing ·         Retail
Coding	Coding is needed. Traditional analytics approaches are combined with a solid understanding of computer science in this subject.	There isn't a lot of coding involved. Statistically orientated.
Languages Recommendations	C/C++/C#, Haskell, Java, Julia, Matlab, Python, R, SAS, Scala, SQL	C/C++/C#, Java, Matlab, Python, R SAS, Scala, SQL
Statistics	Following the creation and coding of algorithms, statistics is used at the end of the analysis.	The entire investigation is based on statistical principles.
Work Challenges	·         • Business decision-makers do not employ data science results. ·         • Inability to adapt results to the decision-making process of the company. ·         • There is a lack of clarity about the questions that must be answered with the data set provided. ·         • Data is unavailable or difficult to obtain. ·         • IT needs to be consulted.	·         • There is a notable lack of domain expert involvement. ·         • Unavailability of/difficult access to data  ·         • Dirty data ·         • Concerns about privacy ·         • Insufficient finances to purchase meaningful data sets from outside sources. ·         • Inability to adapt results to the decision-making process of the company. ·         • There is a lack of clarity about the questions that must be answered with the data set provided. ·         • Tools have limitations. ·         • IT needs to be consulted.
Data Needed	Both structured and unstructured data.	Predominantly structured data.
Future Trends	Machine Learning and Artificial Intelligence	Cognitive Analytics, Tax Analytics

Database Management System Multiple choice questions

1) In the context of data warehousing, data map or ‘data about data’ is known as …………….
a) tuple
b) attribute
c) metadata
d) teradata

2) Which of the following normal form deals with the anomaly of multivalue dependency?
a) 1NF
b) 2NF
c) 4NF
d) 5NF

3) Which of the following normal form deals with the repeating group anomaly?
a) 1NF
b) 2NF
c) 4NF
d) 5NF

4) …………… is a database language used to define data structures in SQL?
a) Sybase
b) Oracle
c) DDL
d) MySQL

5) The ………………… is a database file that contains metadata.
a) data index
b) data repository
c) data dictionary
d) none

6)Which of the following is not the state of a transaction in the database?
a) Active
b) Committed
c) Terminated
d) Nullified

7) The …………… file saves the recovery scenario in Quick Test Professional (QTP) environment
a) QRS
b) JPEG
c) DOC
d) TSR

8) Which of the following is not a command is SQL?
a) WHERE
b) ORDER BY
c) REMOVE
d) SELECT

9) How should we traverse an oracle database to retrieve a row in the quickest possible time?
a) Full table scan
b) Unique index
c) Primary key
d) Table access by row id

10) Which of the following is a concurrency control protocol?
a) Locked-based protocol
b) two-phase locking protocol
c) Timestamp ordering protocol
d) all

11) Which of the following normal forms deals with the anomaly of partial functional dependency on the composite key?
a) 1NF
b) 2NF
c) 4NF
d) 5NF

12) ………………… represents the structure of the database in a formal language acceptable by the DBMS.
a) Database schema
b) ER diagram
c) Database table
d) None

13) Which of the following statements are used to create indexes in the database?
a) CREATE INDEX
b) BUILD INDEX
c) WRITE INDEX
d) None’

14)Which of the following is a database recovery technique?
a) Check-pointing
b) Transaction rollback
c) Shadow paging
d) All

15)Which of the following is a foreing key constraint?
a)Referential integrity
b) Domain integrity
c) Entity integrity
d) All

16) Which of the following trains are important to select a primary key?
a) Unique
b) Not Null
c) Fully functional dependency
d) Indexed
e) All

17) What are various types of DBMS?
a) ORBDMS
b) Client server DBMS
c) Distributed DBMS
d) OODBMS
e) All

18) Which of the following are the types of integrity constraints enforced by RDBMS?
a) Entity integrity
b) Domain integrity
c) Referential integrity
d) All

19) The data model which describes how the data is actually stored is :
a) internal model
b) external model
c) logical model
d) none of these

20) Data about data is normally termed as :
a) directory
b) data bank
c) meta data
d) none of the above

21. In an object-oriented model, one object can access data of another object by passing:
a. Instance variable
b. Message
c. Variable
d. None of these

22. A view of database that appears to an application program is known as:
a. Schema
b. Subschema
c. virtual table
d. none of the above

23. An abstraction concept for building composite object from their component object is called:
a. Specialization
b. Normalization
c. Generalization
d. Aggregation

24. A set of objects that share a common structure and
a common behavior is called:
a. Object
b. Class
c. Entity
d. None of these

25. Every weak entity set can be converted into a strong entity set by:
a. using generalization
b. adding appropriate attributes
c. using aggregation
d. none of the above

26. The number of entities to which another entity can be associated via a relationship set is expressed as:
a. Entity
b. Cardinality
c. Schema
d. Attributes

27. Relations produced from an E-R model will always be in:
a. First normal form
b. Second normal form
c. Third normal form
d. Fourth normal form

28. In ER model the details of the entities are hidden from the user. This process is called:
a. Generalization
b. Specialization
c. Abstraction
d. none of these above

29. The file organization that provides very fast access to any arbitrary record of a file is:
a. Ordered file
b. Unordered file
c. Hashed file
d. B-tree

30. What is not true about a view?
a. It is a definition of a restricted portion of the database
b. It is a security mechanism
c. It is always updatable like any other table
d. All are true

31. In a relational database a referential integrity constraint can be specified with the help of
a. primary key
b. foreign key
c. secondary key
d. none of the above

32. A super key is a set of one or more attributes that, taken collectively, allow us
a. to identify uniquely an entity in the entityset
b. to make the key most powerful for fasterretrieval
c. to increase effectiveness of database access
d. none of the above

33. 4NF is designed to cope with:
a. Transitive dependency
b. Join dependency
c. Multi valued dependency
d. None of these

34. Every Boyee-Codd normal form is in
a. First normal form
b. Second normal form
c. Third normal form
d. All of the above

35. Which command is used to remove all rows from a table?
a. Delete
b. Remove
c. Truncate
d. Both [A] and [B]

36. Which of the following is an aggregate function in SQL?
a. Union
b. Like
c. Group By
d. Max

37. Which command is used to add a column to an existing table?
a. Create
b. Update
c.Alter
d. None of these

38. A deadlock exists in the system if and only if the wait for graph:
a. has a cycle in it
b. has a path from first node to last node
c. is a tree
d. none of the above

39. Rollback of transactions is normally used to:
a. recover from transaction failure
b. update the transaction
c. retrieve old records
d. repeat a transaction

40. Prevention of access to the database by unauthorized users is referred to as:
a. Integrity
b. Productivity
c. Security
d. Reliability