What Is Critical Thinking?
Critical thinking is the ability to think clearly and rationally about what to do or what to believe. It includes the ability to engage in reflective and independent thinking. Someone with critical thinking skills is able to do the following :
- understand the logical connections between ideas
- identify, construct and evaluate arguments
- detect inconsistencies and common mistakes in reasoning
- solve problems systematically
- identify the relevance and importance of ideas
- reflect on the justification of one's own beliefs and values
Critical thinking is not a matter of accumulating information. A person with a good memory and who knows a lot of facts is not necessarily good at critical thinking. A critical thinker is able to deduce consequences from what he knows, and he knows how to make use of information to solve problems, and to seek relevant sources of information to inform himself.
Critical thinking is the ability to think clearly and rationally about what to do or what to believe. It includes the ability to engage in reflective and independent thinking. Someone with critical thinking skills is able to do the following :
- understand the logical connections between ideas
- identify, construct and evaluate arguments
- detect inconsistencies and common mistakes in reasoning
- solve problems systematically
- identify the relevance and importance of ideas
- reflect on the justification of one's own beliefs and values
Critical thinking is not a matter of accumulating information. A person with a good memory and who knows a lot of facts is not necessarily good at critical thinking. A critical thinker is able to deduce consequences from what he knows, and he knows how to make use of information to solve problems, and to seek relevant sources of information to inform himself.
How To Write Efficient Algorithms?
- Improving time complexity
- Improving space complexity
The lesser time your algorithm takes to give result for bigger values the lesser is the time complexity and the same thing applies to space complexity too.
- Use efficient data structure according to problem statement
- Make use of hash tables
- Make use of memoization techniques
- Use dynamic programming
- Avoid recursion in general
- Avoid repetitive calculation of results for subproblems, rather store them once calculated
- Use bitwise operations
- Operate on bit level rather than byte level to save space wherever possible
- Improving time complexity
- Improving space complexity
The lesser time your algorithm takes to give result for bigger values the lesser is the time complexity and the same thing applies to space complexity too.
- Use efficient data structure according to problem statement
- Make use of hash tables
- Make use of memoization techniques
- Use dynamic programming
- Avoid recursion in general
- Avoid repetitive calculation of results for subproblems, rather store them once calculated
- Use bitwise operations
- Operate on bit level rather than byte level to save space wherever possible
Who Was The First Computer Programmer?
Ada Lovelace is considered as the first programmer, who worked with Sir Charles Babbage on the Analytical Engine ( First Generation general purpose mechanical computer partly built by English inventor Charles Babbage). When the Government of Britain refused any further investment on the Differential machine (A mechanical machine which could compute mathematical tables), in 1834 Mr. Babbage moved on to constructing this Analytical Engine.
Ada met Mr. Babbage (Cambridge Mathematics Professor) and Babbage was very impressed with her Knowledge of Mathematics. They corresponded for years during the development of Analytical Machine.
Mr. Babbage gave a lecture on this machine in the University of Turin which was transcribed in French. Ada was commissioned to translate the transcript to english. Lecture was delivered in 1842 and in 1843 her transcript was published.
Ada suggested the input which had to be provided to the machine to generate calculate Bernoulli number. This is considered as the first program.
Ada Lovelace is considered as the first programmer, who worked with Sir Charles Babbage on the Analytical Engine ( First Generation general purpose mechanical computer partly built by English inventor Charles Babbage). When the Government of Britain refused any further investment on the Differential machine (A mechanical machine which could compute mathematical tables), in 1834 Mr. Babbage moved on to constructing this Analytical Engine.
Ada met Mr. Babbage (Cambridge Mathematics Professor) and Babbage was very impressed with her Knowledge of Mathematics. They corresponded for years during the development of Analytical Machine.
Mr. Babbage gave a lecture on this machine in the University of Turin which was transcribed in French. Ada was commissioned to translate the transcript to english. Lecture was delivered in 1842 and in 1843 her transcript was published.
Ada suggested the input which had to be provided to the machine to generate calculate Bernoulli number. This is considered as the first program.
What Is Aspect-Oriented Programming(AOP)?
Aspect-oriented programming (AOP) is a programming paradigm that aims to increase modularity by allowing the separation of cross-cutting concerns.
It does so by adding additional behavior to existing code without modifying the code itself, instead separately specifying which code is modified via a "pointcut" specification, such as "log all function calls when the function's name begins with 'set'".
This allows behaviors that are not central to the business logic (such as logging) to be added to a program without cluttering the code, core to the functionality. AOP forms a basis for aspect-oriented software development.
AOP includes programming methods and tools that support the modularization of concerns at the level of the source code, while "aspect-oriented software development" refers to a whole engineering discipline.
Aspect-oriented programming (AOP) is a programming paradigm that aims to increase modularity by allowing the separation of cross-cutting concerns.
It does so by adding additional behavior to existing code without modifying the code itself, instead separately specifying which code is modified via a "pointcut" specification, such as "log all function calls when the function's name begins with 'set'".
This allows behaviors that are not central to the business logic (such as logging) to be added to a program without cluttering the code, core to the functionality. AOP forms a basis for aspect-oriented software development.
AOP includes programming methods and tools that support the modularization of concerns at the level of the source code, while "aspect-oriented software development" refers to a whole engineering discipline.
Who Is A Brogrammer?
"Brogrammer", a portmanteau of bro and programmer, is a slang term for a stereotypically masculine programmer.
It is often used pejoratively, but some programmers self-describe themselves as a brogrammer positively as a word for "sociable or outgoing programmer", and it also tends to represent a subculture within the greater tech industry.
"Brogrammer", a portmanteau of bro and programmer, is a slang term for a stereotypically masculine programmer.
It is often used pejoratively, but some programmers self-describe themselves as a brogrammer positively as a word for "sociable or outgoing programmer", and it also tends to represent a subculture within the greater tech industry.
What Is Machine Learning?
Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task.
Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task.
What Is Big Data?
Big data refers to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.
Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.
Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value.
Big data refers to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.
Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.
Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value.