<–2/”>a >A computer program is a collection of instructions that performs a specific task when executed by a computer. A computer requires programs to function and typically executes the program’s instructions in a central processing unit.A computer program is usually written by a computer programmer in a programming language.
Computer language or programming language is a coded syntax used by computer programmers to communicate with a computer. Computer language establishes a flow of Communication between Software programs. The language
enables a computer user to dictate what commands the computer must perform to process data. These languages
can be classified into following categories :-
1. Machine language
2. Assembly language
3. High level language
Machine language is a computer programming language consisting of binary or hexadecimal instructions which a computer can respond to directly.
Assembly language is a low-level programming language for a computer, or other programmable device, in which there is a very strong (but often not one-to-one) correspondence between the language and the architecture’s machine code instructions.
High level language is a programming language such as C, FORTRAN, or Pascal that enables a programmer to write programs that are more or less independent of a particular type of computer. Such languages are considered high-level because they are closer to human languages and further from machine languages.
Open-source software (OSS) is computer software with its source code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose.,
Data science is a field that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data scientists use their skills to solve complex problems, make predictions, and improve decision-making.
Data engineering is the process of collecting, cleaning, and organizing data so that it can be used for analysis. Data engineers build and maintain the systems that collect and store data, and they develop the tools that data scientists use to analyze it.
Data analytics is the process of exploring and analyzing data to extract meaningful insights. Data analysts use their skills to identify trends, patterns, and relationships in data. They use this information to make better decisions, improve business performance, and solve problems.
Data visualization is the process of representing data in a visual format, such as charts, graphs, and maps. Data visualization can help people to understand complex data sets more easily. It can also be used to communicate findings to others.
Data mining is the process of extracting patterns from data. Data miners use their skills to identify patterns that would be difficult to find by looking at the data manually. They use this information to make predictions, improve decision-making, and solve problems.
Machine Learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning algorithms are used to train computers to perform tasks such as Classification, prediction, and clustering.
Artificial Intelligence is a field of computer science that focuses on creating intelligent agents, which are systems that can reason, learn, and act autonomously. Artificial intelligence techniques are used in a wide range of applications, including Robotics, natural language processing, and computer vision.
Natural language processing is a field of computer science that deals with the interaction between computers and human language. Natural language processing techniques are used to understand and process human language, such as text, speech, and handwriting.
Computer vision is a field of computer science that deals with the extraction of meaningful information from digital images or Videos. Computer vision techniques are used to recognize objects, track motion, and understand scene structure.
Big data is a term used to describe the large and complex datasets that are generated by modern technology. Big data challenges traditional data processing techniques and requires new approaches to data storage, analysis, and visualization.
Cloud computing is the on-demand delivery of IT Resources over the Internet with pay-as-you-go pricing. Cloud computing Services, such as Infrastructure-2/”>INFRASTRUCTURE as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), enable businesses to scale their IT resources up or down as needed without having to make large upfront investments in hardware and software.
Data security is the protection of data from unauthorized access, use, disclosure, disruption, modification, or destruction. Data security is important to protect the privacy of individuals, the confidentiality of business information, and the Integrity of critical systems.
Data ethics is the study of the moral and ethical issues that arise in the collection, use, and sharing of data. Data ethics is important to ensure that data is used in a responsible and ethical manner.
Data governance is the set of policies, processes, and procedures that ensure the effective management of data. Data governance is important to ensure that data is accurate, complete, and up-to-date. It also helps to ensure that data is used in a consistent and compliant manner.
Data management is the process of collecting, storing, and organizing data in a way that makes it accessible and useful. Data management is important to ensure that data is available when it is needed and that it can be used to support decision-making.
Data Warehousing is the process of collecting and storing data from multiple sources in a central repository. Data warehouses are used to support business intelligence and analytics applications.
Data marts are smaller, subject-specific data warehouses. Data marts are often used by business units or departments to support their specific needs.
Data lakes are unstructured data repositories that are used to store large amounts of data. Data lakes are often used for big data analytics and machine learning applications.
Data pipelines are the systems that move data from one place to another. Data pipelines are used to collect data from sources, transform it into a format that can be used by applications, and load it into data warehouses or data lakes.
Data models are the conceptual representations of data. Data models are used to describe the structure of data and the relationships between data Elements.
Data warehouse design is the process of designing a data warehouse. Data warehouse design includes identifying the data sources, determining the data requirements, and designing the data model.
Data mart design is the process of designing a data mart. Data mart design includes identifying the data sources, determining the data requirements, and designing the data model.
Data lake design is the process of designing a data lake. Data lake design includes identifying the data sources, determining the data requirements, and designing the data model.
Data
What is data science?
Data science is a field that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.
What are the different types of data science?
There are many different types of data science, but some of the most common include:
- Descriptive data science: This type of data science focuses on describing the data, such as finding trends and patterns.
- Predictive data science: This type of data science focuses on predicting future outcomes, such as predicting customer behavior or predicting the weather.
- Prescriptive data science: This type of data science focuses on recommending actions, such as recommending products to customers or recommending treatments for patients.
What are the different tools and technologies used in data science?
There are many different tools and technologies used in data science, but some of the most common include:
- Programming languages: Data scientists often use programming languages such as Python, R, and SQL to manipulate and analyze data.
- Machine learning algorithms: Data scientists often use machine learning algorithms to extract insights from data.
- Data visualization tools: Data scientists often use data visualization tools to communicate their findings to others.
What are the different career paths in data science?
There are many different career paths in data science, but some of the most common include:
- Data scientist: Data scientists are responsible for extracting insights from data.
- Data analyst: Data analysts are responsible for cleaning and organizing data.
- Data engineer: Data engineers are responsible for building and maintaining data pipelines.
- Machine learning engineer: Machine learning engineers are responsible for building and maintaining machine learning models.
What are the challenges of data science?
Some of the challenges of data science include:
- Data quality: Data scientists often have to deal with dirty or incomplete data.
- Modeling complexity: Data scientists often have to build complex models to extract insights from data.
- Interpretability: Data scientists often have to make their models interpretable so that others can understand them.
- Communication: Data scientists often have to communicate their findings to others, which can be challenging.
What is the future of data science?
The future of data science is very bright. Data science is being used in a wide variety of industries, and the demand for data scientists is expected to grow significantly in the coming years.
Which of the following is not a type of data?
(A) Text
(B) Image
(C) Video
(D) Data ProgrammeWhich of the following is not a data format?
(A) CSV
(B) JSON
(C) XML
(D) Data ProgrammeWhich of the following is not a data storage method?
(A) File system
(B) Database
(C) Cloud storage
(D) Data ProgrammeWhich of the following is not a data analysis tool?
(A) Spreadsheet
(B) Data visualization tool
(C) Statistical software
(D) Data ProgrammeWhich of the following is not a data science skill?
(A) Data cleaning
(B) Data wrangling
(C) Data analysis
(D) Data ProgrammeWhich of the following is not a data ethics principle?
(A) Transparency
(B) Accountability
(C) Privacy
(D) Data ProgrammeWhich of the following is not a data governance principle?
(A) Data quality
(B) Data security
(C) Data retention
(D) Data ProgrammeWhich of the following is not a data protection law?
(A) GDPR
(B) CCPA
(C) HIPAA
(D) Data ProgrammeWhich of the following is not a data science job title?
(A) Data scientist
(B) Data engineer
(C) Data analyst
(D) Data ProgrammeWhich of the following is not a data science certification?
(A) Google Data Analytics Professional Certificate
(B) IBM Data Science Professional Certificate
(C) Microsoft Certified Data Scientist Associate
(D) Data Programme