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

Short note on Artificial Inteligence

Artificial Intelligence is a branch of computer science concerned with making an intelligent machine behave like a human. The term "A.I." was introduced by John McCarthy in 1956. He was the designer of the language LISP(List Processing). LISP is the high-level programming language. We know about the computer that given a set of rules written in the programming language of the computer, the A.I. systems should obey the rules strictly. So, human scientists can test their theories about human behavior by converting their rules to a computer program and observing if the computer's behavior in executing these programs is like the natural behavior of a human being, or at least that small subset of human behavior they are studying. A computer scientist can look at modeling human behavior as a challenge to their programming abilities. If a person can do something, they can write a computer program that does the same thing. The aim of artificial intelligence is to try to make a computer perform tasks that humans tend to be good at.

 

The seeds of modern A.I. were planted by classical philosophers who attempted to explain the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the digital programmable computer in the 1940s, the machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain. The field of A.I. research was founded at a workshop held on the campus of Dartmouth College during the summer of 1956s. Those who attended the workshop would become the leaders of A.I. research for decades. Many of them predicted that a machine as intelligent as a human being would exist in not more than a generation, and they were provided with millions of dollars to make this vision come true. Eventually, it became evident that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from Congress, the U.S. and British Governments stopped giving funds to un-directed research into artificial intelligence, and the difficult years that followed would later be known as an "A.I. winter." Seven years later, a visionary initiative by the Japanese government inspired governments and industry to provide A.I. with billions of dollars. Still, by the late 80s, the investors became disillusioned and withdrew funding again. Interest and funding in A.I. boomed in the first decades of the 21st century when machine learning was successfully applied to many problems in academia and industry. As in previous "A.I. summers," some observers predicted the imminent arrival of artificial general intelligence (a machine with intellectual capabilities that exceed the abilities of human beings)

 

In 1956:- The first Dartmouth College summer A.I. conference is organized by John McCarthy, Marvin Minsky, Nathan Rochester of IBM(Deep Blue chess machine / international business machines (doubt)), and Claude Shannon. The name artificial intelligence is used for the first time as the topic of the second Dartmouth Conference, organized by John McCarthy. The first demonstration of the LT (Logic Theorist) was written by Allen Newell, J.C. Shaw, and Herbert A. Simon (Carnegie Institute of Technology) but the present name that is institute Carnegie Mellon University. This is often called the first A.I. program.

 

In 1957:-The general problem solver (GPS) was demonstrated by Newell, Shaw, and Simon.

 

In 1958-1960:- John McCarthy invented the Lisp programming language. Herbert Gelernter and Nathan Rochester described a theorem prove in geometry that exploits a semantic model of the domain in diagrams of typical cases. Teddington Conference on the Mechanization of thought processes was held in the U.K., and among the papers presented were John McCarthy's programs with common sense. 

 

In 1959:- John McCarthy and Marvin Minsky founded the MIT AI Lab. Margaret Masterman and colleagues at the University of Cambridge design semantic nets for machine translation. Ray Solomonoff lays the foundations of a mathematical theory of A.I., introducing universal Bayesian methods for inductive inference and prediction. Man-Computer Symbiosis by J.C.R. Licklider.

 

In 1961-2000:- James Slagle wrote the first symbolic integration program in lisp, which solved calculus problems at the college level. He referred to sufficiently powerful formal systems are either inconsistent or allow for formulating true theorems un-provable by any theorem-proving A.I. deriving all provable theorems from the axioms. Since humans can see the truth of such theorems, machines were deemed inferior. Unimation's industrial robot animate worked on a general motors automobile assembly line. Thomas Evans demonstrated that computers could solve the same analogy problems as are given on I.Q. tests. Leonard and Charles published a pattern recognition program that generates, evaluates, and adjusts its operators, which described one of the first machine learning programs that could acquire and modify features. Danny Bobrow's dissertation project M.A. shows that computers can understand natural language well enough to solve algebra word problems correctly. Bertram Raphael's MIT dissertation on the program demonstrates the power of a logical representation of knowledge for question-answering systems. J. Alan Robinson invented a mechanical proof procedure, the resolution method, which allowed programs to work efficiently with formal logic as a representation language. It was a popular toy at A.I. centers on the ARPANET when a version that simulated the dialogue of a psychotherapist was programmed. Edward Feigenbaum initiated general a ten-year effort to develop software to deduce the molecular structure of organic compounds using scientific instrument data. It was the first expert system. In 1967, the first successful knowledge-based program for scientific reasoning, and 1968 the first successful knowledge-based program in mathematics. In 1969 Roger Stanford defined the conceptual dependency model for natural language understanding and the first semantics-driven machine translation program. 1970 Jaime Carbonell developed scholar, an interactive program for computer-assisted instruction based on semantic nets as the representation of knowledge. Bill Woods described augmented transition network (ATN) as a representation for natural language understanding. 1973 the assembly robotics group at the University of Edinburgh builds a Freddy robot capable of using visual perception to locate and assemble models. 1975 The Meta-Dendral learning program reported new results in chemistry (some rules of mass spectrometry), the first scientific discovery by a computer to be published in a peer-review refereed journal. 1978 Herbert A. Simon won the "Nobel" prize in economics for the theory of bounded rationality, one of the milestones of A.I. known as satisfying. That year, The molten program, written by Mark Stefik and Peter Friedland, demonstrated that an object-oriented programming representation of knowledge could be used to plan gene-cloning experiments. 1979 The Stanford cart, developed by Hans Moravec, becomes the first computer-controlled, autonomous vehicle when it successfully traverses a room and circumnavigates the Stanford AI Lab. The late 1970 demonstrates the power of the ARPANET for scientific collaboration. 1980s Lisp machines were developed and marketed. First expert system shells and commercial applications. 1980 first national conference of the American Association for artificial intelligence (AAAI) was held at Stanford. 1981 Danny Hillis designs the connection machine, which utilizes parallel computing to bring new power to A.I. and computation in general. 1982 The fifth generation computer systems project, an initiative by Japan's ministry of international trade and industry. 1986 the Ernst Dickmanns at Bundeswehr University of Munich builds the first robot cars, driving up to 55 mph on empty streets. Founders of the firm with the underlying engine developed by Paul Tarvydas. The alacrity system also included a small financial expert system that interpreted financial statements and models. The early 1990s is powerful enough to create a championship-level game-playing program by competing favorably with world-class players by TD-Gammon. 1990s major advances in all areas of A.I., with significant demonstrations in machine learning, intelligent tutoring, case-based reasoning, multi-agent planning, scheduling, uncertain reasoning, data mining, natural language understanding and translation, vision, virtual reality, games, and other topics. 1991 DART scheduling application deployed in the first gulf war paid back DARPA's investment of 30 years in A.I. research. 1993 Ian Horswill extended behavior-based robotics by creating Polly, the first robot to navigate using vision and operate at animal-like speeds (1 meter/second). 1995 (No Hands Across America), a semi-autonomous car drove coast-to-coast across the united states with computer-controlled steering for 2,797 miles (4,501 km) of the 2,849 miles (4,585 km). The throttle and brakes were controlled by a human driver. In the late 1990s, Web crawlers and other AI-based information extraction programs become essential in the widespread use of the World Wide Web. Demonstration of an intelligent room and emotional agents at MIT's A.I. lab. Initiation of work on the oxygen architecture, which connects mobile and stationary computers in an adaptive network.

 

In 2001-2016:- 2004 NASA's robotic exploration rovers spirit and opportunity autonomously navigate the surface of Mars.2004 DARPA introduces the DARPA grand challenge requiring competitors to produce autonomous vehicles for prize money. 2005 Honda's ASIMO robot, an artificially intelligent humanoid robot, can walk as fast as a human, delivering trays to customers in restaurant settings. 2005 blue brain is born, a project to stimulate the brain in molecular detail. 2009 google builds a self-driving car. 2010 Microsoft launched Kinect for Xbox 360, the first gaming device to track human body movement, using just a 3D camera and infra-red detection, enabling users to play their Xbox 360 wireless. The award-winning machine learning for human motion capture technology for this device was developed by the computer vision group at Microsoft research. 2011 Apple's Siri, google's google now, and Microsoft's Cortana are smartphone apps that use natural language to answer questions, make recommendations, and perform actions. 2013 NEIL, the never-ending image learner, is released at Carnegie Mellon University to compare and analyze relationships between different images constantly. 2015 an open letter to ban the development and use of autonomous weapons signed by Hawking, Musk, Wozniak, and 3,000 researchers in A.I. and robotics.

 

Application of A.I.

 

1. Nature Language Process:- A computer system capable of understanding a message in the natural language would seem to require both the contextual knowledge and the process for making the inferences (from this contextual knowledge and the news) assumed by the message generator. Some progress has been made toward computer systems of this sort for understanding spoken and written fragments of language. Fundamental to the development of such a system are specific A.I. ideas about the structure for representing contextual knowledge and particular techniques for making inferences.

 

2. Expert consulting systems:- A.I. methods have also been employed to develop an automatic consulting system. These systems provide human users with an expert conclusion about specialized subject areas. Automated consulting systems have been built that can diagnose diseases, evaluate potential ore deposits, suggest structures for complex organic chemicals, and even provide advice about how to use another computer system.

 

3. Robotics:- Research on robots or robotics has helped to develop many A.I. ideas. It has led to several techniques for modeling the state of the world and describing the process of charge from one world state to another. It has led to a better understanding of how to generate a plane for action sequences and monitor the execution of these plans. Complex robot control problems have forced us to develop methods for planning at lower levels of abstraction, ignoring details and then planning at lower and lower levels, where details become essential. 

 

4. Automatic Programming:- The task of writing a computer program is related both to theorem proving and to robotics. Much of the primary research is in automatic programming—theorem proving and robot problem-solving overlaps. In a sense, existing complete already do automatic programming. The task is a full source code specification of what a program is to accomplish, and they write an object code program to do it.

Analysis and Design of Algorithms Multiple choice questions

 1.Recursion is similar to which of the following?
 Switch Case
 Loop
 If-else
 if elif else

2.In recursion, the condition for which the function will stop calling itself is ____________
 Best case
 Worst case
 Base case
 There is no such condition

3.Consider the following code snippet:
void my_recursive_function()
{
my_recursive_function();
}
int main()
{
my_recursive_function();
return 0;
}

What will happen when the above snippet is executed?
 The code will be executed successfully and no output will be generated
 The code will be executed successfully and random output will be generated
 The code will show a compile-time error
 The code will run for some time and stop when the stack overflows

4.What is the output of the following code?
void my_recursive_function(int n)
{
if(n == 0)
return;
printf("%d ",n);
my_recursive_function(n-1);
}
int main()
{
my_recursive_function(10);
return 0;
}
 10
 1
 10 9 8 ... 1 0
 10 9 8 ... 1

5.What is the base case for the following code?
void my_recursive_function(int n)
{
if(n == 0)
return;
printf("%d ",n);
my_recursive_function(n-1);
}
int main()
{
my_recursive_function(10);
return 0;
}

 Return
 printf(“%d “, n)
 if(n == 0)
 My_recursive_function(n-1)

6.How many times is the recursive function called, when the following code is executed?
void my_recursive_function(int n)
{
if(n == 0)
return;
printf("%d ",n);
my_recursive_function(n-1);
}
int main()
{
my_recursive_function(10);
return 0;
}

 9
 10
 11
 12

7.Which of the following statements is true?
 Recursion is always better than iteration
 Recursion uses more memory compared to iteration
 Recursion uses less memory compared to iteration
 Iteration is always better and simpler than recursion

8.What will be the output of the following code?
int cnt=0;
void my_recursive_function(int n)
{
if(n == 0)
return;
cnt++;
my_recursive_function(n/10);
}
int main()
{
my_recursive_function(123456789);
printf("%d",cnt);
return 0;
}

 123456789
 10
 0
 9

9.
void my_recursive_function(int n)
{
if(n == 0)
{
printf("False");
return;
}
if(n == 1)
{
printf("True");
return;
}
if(n%2==0)
my_recursive_function(n/2);
else
{
printf("False");
return;
}
}
int main()
{
my_recursive_function(100);
return 0;
}

 True
 False

10.What is the output of the following code?
int cnt = 0;
void my_recursive_function(char *s, int i)
{
if(s[i] == '\0')
return;
if(s[i] == 'a' || s[i] == 'e' || s[i] == 'i' || s[i] == 'o' || s[i] == 'u')
cnt++;
my_recursive_function(s,i+1);
}
int main()
{
my_recursive_function("thisisrecursion",0);
printf("%d",cnt);
return 0;
}

 6
 9
 5
 10

11.What is the output of the following code?
void my_recursive_function(int *arr, int val, int idx, int len)
{
if(idx == len)
{
printf("-1");
return ;
}
if(arr[idx] == val)
{
printf("%d",idx);
return;
}
my_recursive_function(arr,val,idx+1,len);
}
int main()
{
int array[10] = {7, 6, 4, 3, 2, 1, 9, 5, 0, 8};
int value = 2;
int len = 10;
my_recursive_function(array, value, 0, len);
return 0;
}

 3
 4
 5
 6

12.______is the first step in solving the problem
 Understanding the Problem
 Identify the Problem
 Evaluate the Solution
 None of these

13.______is the last step in solving the problem
 Understanding the Problem
 Identify the Problem
 Evaluate the Solution
 None of these

14.Following is true for understanding of a problem
 Knowing the knowledgebase
 Understanding the subject on which the problem is based
 Communication with the client
 All of the above

15.The six-step solution for the problem can be applied to
I. Problems with Algorithmic Solution
II. Problems with Heuristic Solution
 Only I
 Only II
 Both I and II
 Neither I nor II

16.______ solution requires reasoning built on knowledge and experience
 Algorithmic Solution
 Heuristic Solution
 Random Solution
 None of these

17.The correctness and appropriateness of ___________solution can be checked very easily.
 Algorithmic solution
 Heuristic solution
 Random solution
 None of these

18.The branch of computer that deals with heuristic types of problem is called _________________.
 System software
 Real time software
 Artificial intelligence
 None of these

19.Artificial Intelligence makes use of following prominently
 Database
 Data Warehouse
 Knowledge base
 None of these

20.Naming convention for variable is followed in company because ____________.
 It enhances readability
 It allows to work without conflicts
 It enhances the efficiency
 All of the above

Basic Questions and Answers on Artificial Intelligence

1) What is AI?

Systems that think like humans

Systems that think rationally

Systems that act like humans

Systems that act rationally

2) Define an agent.

     An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.

3) What is an agent function?

   An agent’s behavior is described by the agent function that maps any given percept sequence  to an action.

4) Differentiate an agent function and an agent program.

Agent Function Agent Program

An abstract mathematical description A concrete implementation,running on the agent Architecture.

5) What can Ai do today?

 

 

6) What is a task environment? How  it is specified?

Task environments   are essentially the "problems" to which rational agents are the "solutions."

A Task environment is specified using PEAS (Performance, Environment,    Actuators, Sensors) description.

     

7) Give an example of PEAS description for an automated taxi.

 

8) Give PEAS description for different agent types.

9) List the properties of task environments.

 Fully observable vs. partially observable.

 Deterministic vs. stochastic.

 Episodic vs. sequential

 Static vs, dynamic.

 Discrete vs. continuous.

 Single agent vs. multiagent.

10) Write a function for the table driven agent.

11) What are the four different kinds of agent programs?

Simple reflex agents;

Model-based reflex agents;

Goal-based agents; and

Utility-based agents.

12) Explain a simple reflex agent with a diagram.

Simple reflex agents

 The simplest kind of agent is the simple reflex agent. These agents select actions on the basis AGENT

of the current percept, ignoring the rest of the percept history.

 

13) Explain with a diagram the model based reflex agent.

13a)  Explain with a diagram the goal based reflex agent.

Knowing about the current state of the environment is not always enough to decide what

to do. For example, at a road junction, the taxi can turn left, turn right, or go straight on.

The correct decision depends on where the taxi is trying to get to. In other words, as well

as a current state description, the agent needs some sort of goal information that describes

situations that are desirable-for example, being at the passenger's destination.

13b) What are utility based agents?

Goals alone are not really enough to generate high-quality behavior in most environments.

For example, there are many action sequences that will get the taxi to its destination (thereby

achieving the goal) but some are quicker, safer, more reliable, or cheaper than others.

A utility function maps a state (or a sequence of states) onto a real number, which

describes the associated degree of happiness.

13c) What are learning agents?

A learning agent can be divided into four conceptual components, as shown in Fig-

 2.15. The most important distinction is between the learning element, which is re-

ELEMENT sponsible for making improvements, and the performance element, which is responsible for

selecting external actions. The performance element is what we have previously considered

to be the entire agent: it takes in percepts and decides on actions. The learning element uses

CRITIC feedback from the critic on how the agent is doing and determines how the performance

element should be modified to do better in the future.

 

Searching Techniques

14) Define the problem solving agent.

           A Problem solving agent is a goal-based agent . It decide what to do by finding sequence of actions that lead to desirable states. The agent can adopt a goal and aim at satisfying it. 

Goal formulation is the first step in problem solving.

      

15) Define the terms goal formulation and problem formulation.

               Goal formulation,based on the current situation and the agent’s performance measure,is the first step in problem solving. 

                       The agent’s task is to find out which sequence of actions will get to a goal state.

Problem formulation is the process of deciding what actions and states to consider given a goal

16) List the steps involved in simple problem solving agent.

(i) Goal formulation

(ii) Problem formulation

(iii) Search

(iv) Search Algorithm

(v) Execution phase

17) Define search and search algorithm.

                    The process of looking for sequences actions from the current state to reach the goal state is called search.

The search algorithm takes a problem as input and returns a solution in the form of action sequence. Once a solution is found,the execution phase consists of carrying out the recommended action..

18) What are the components of well-defined problems?

o The initial state that the agent starts in . The initial state for our agent of example problem is described by In(Arad)

o A Successor Function returns  the possible actions available to the agent. Given a state x,SUCCESSOR-FN(x) returns a set of {action,successor} ordered pairs where each action is one of the legal actions in state x,and each successor is a state that can be reached from x by applying the action.

      For example,from the state In(Arad),the successor function for the Romania  

       problem would return 

{ [Go(Sibiu),In(Sibiu)],[Go(Timisoara),In(Timisoara)],[Go(Zerind),In(Zerind)] }

o Thr goal test determines whether the given state is a goal state.

o A path cost function assigns numeric cost to each action. For the Romania problem the cost of path might be its length in kilometers.

19) Differentiate toy problems and real world problems.

TOY PROBLEMS REAL WORLD PROBLEMS

A toy problem is intended to illustrate various problem solving methods. It can be easily used by different  researchers to compare the performance of algorithms.

A real world problem is one whose solutions people actually care about.

20) Give examples of real world problems.

(i) Touring problems

(ii) Travelling Salesperson Problem(TSP)

(iii) VLSI layout

(iv) Robot navigation

(v) Automatic assembly sequencing

(vi) Internet searching

21) List the criteria to measure the performance of different search strategies.

o Completeness : Is the algorithm guaranteed to find a solution when there is one?

o Optimality : Does the strategy find the optimal solution?

o Time complexity : How long does it take to find a solution?

o Space complexity : How much memory is needed to perform the search?

22) Differentiate Uninformed Search(Blind search) and Informed Search(Heuristic Search) strategies.

Uninformed or Blind Search Informed or Heuristic Search

o No additional information beyond that provided in the problem definition

o Not effective

o No information about number of steps or path cost o More effective

o Uses problem-specific knowledge beyond the definition of the problem itself.

23) Define Best-first-search.

Best-first search is an instance of the general TREE-SEARCH or GRAPH-SEARCH algorithm in which a node is selected for expansion based on the evaluation function f(n ). Traditionally,the node with the lowest evaluation function is selected for expansion.