Implement Mapreduce with suitable example using MongoDB.
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The document implements a MapReduce operation using MongoDB to count the number of posts by each user with status "active" stored in a collection called c1. Four documents were inserted into the c1 collection. A MapReduce operation was run with a mapping function to emit each user_name with a count of 1, and a reducing function to sum the counts. The results of the operation were stored in a collection called "post_total". The find operation then displays the four documents inserted into the original c1 collection.
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information control and Security systembhavesh lande Chapter 9 addresses information system security and control, focusing on organizational threats such as malware, phishing, and denial-of-service attacks. It explores technologies for managing security, including encryption, firewalls, and virtual private networks, while emphasizing the importance of confidentiality, authentication, and message integrity. Additionally, effective security management strategies are discussed, highlighting the need for risk assessment and disaster recovery planning.
information technology and infrastructures choices
information technology and infrastructures choicesbhavesh lande The document discusses the management of information systems in business, government, and society, focusing on IT infrastructure, its components, and decision-making processes. It outlines learning objectives such as understanding technology infrastructures and making appropriate infrastructure decisions. The document also covers infrastructure components including hardware, software, networks, and cloud computing solutions, as well as the concept of virtualization.
ethical issues,social issues
ethical issues,social issuesbhavesh lande The document discusses several ethical and social issues concerning information systems. It covers topics like privacy, workplace monitoring, the concept of an information panopticon, challenges of e-waste management, and how information systems can impact work practices and worker health/alienation. Specific issues addressed include privacy concerns for both individuals and governments, the various ways workplaces monitor employees, how this relates to power dynamics, and the health and environmental problems caused by improper e-waste disposal.
managing inforamation system
managing inforamation systembhavesh lande The document discusses key challenges in managing IT functions, including the modern dynamic IT environment, centralization vs decentralization debates, standardization, security issues, technology selection criteria, vendor management, the role of the CIO, and IT governance models. It notes the first, second, and third order impacts of introducing IT to an organization and examines debates around centralized vs decentralized infrastructure and standardization. It also outlines factors to consider in technology selection, the vendor management lifecycle, and different IT governance archetypes involving business and IT leadership.
• E-commerce, e-business ,e-governance
• E-commerce, e-business ,e-governancebhavesh lande The document discusses the management of information systems in business, government, and society, with a focus on e-commerce technology and electronic governance. It outlines the evolution of e-commerce, including the dotcom boom and bust, and explains various aspects of online business, such as payment technologies, direct selling, auctions, and e-governance. Additionally, it highlights the differences between B2B and B2C e-commerce and the advantages of e-business and e-governance for enhancing organizational efficiency.
IT and innovations
IT and innovations bhavesh lande The document outlines the key drivers of innovation in information technology (IT) and its impact on businesses, government, and society. It discusses various innovation theories such as Moore's Law, disruptive innovation, and creative destruction, emphasizing the importance of IT for competitive advantage. Additionally, it highlights the challenges managers face due to rapid IT innovations and the types of business innovations enabled by these technologies.
organisations and information systems
organisations and information systemsbhavesh lande The document discusses the management of information systems in modern organizations, emphasizing their roles and challenges in business, government, and society. It covers the evolution of information technology, the importance of digital networks, and the structure of knowledge organizations. Additionally, it outlines the managerial challenges and considerations when designing and implementing information systems.
IT stratergy and digital goods
IT stratergy and digital goodsbhavesh lande The document discusses key topics relating to managing information systems in business, including:
1) The competitive environment of business and factors that influence competition like partnering, bargaining power, barriers to entry, and threats of substitutes.
2) How information goods have properties that are expensive to produce but cheap to reproduce, and how technology lock-in, switching costs, and network externalities impact information goods.
3) How information systems can enable a company's competitive strategy and support activities in the value chain.
aggregation and indexing with suitable example using MongoDB.
aggregation and indexing with suitable example using MongoDB.bhavesh lande Implement aggregation and indexing with suitable example
using MongoDB.
Unnamed PL/SQL code block: Use of Control structure and Exception handling i...
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database application using SQL DML statements: all types of Join, Sub-Query ...
database application using SQL DML statements: all types of Join, Sub-Query ...bhavesh lande The document outlines a practical problem requiring the design of at least 10 SQL queries using DML statements, focusing on various types of joins, sub-queries, and views. It includes detailed examples of queries performed on an 'emp' table, demonstrating the use of sub-queries to filter and join data based on specific conditions. Additionally, the document describes the creation and manipulation of 'orange' and 'apple' tables, including the insertion of values and various join operations between them.
database application using SQL DML statements: Insert, Select, Update, Delet...
database application using SQL DML statements: Insert, Select, Update, Delet...bhavesh lande The document details the creation and management of SQL queries for a database application, specifically focusing on employee ('emp') and department ('dept') tables. It includes commands for inserting records, querying employee salaries within a certain range, and aggregating data such as minimum, maximum, and average salaries grouped by department. Furthermore, various SQL functionalities are demonstrated, including selection conditions, grouping, and arithmetic operations on the data.
Design and Develop SQL DDL statements which demonstrate the use of SQL objec...
Design and Develop SQL DDL statements which demonstrate the use of SQL objec...bhavesh lande The document outlines a practical exercise involving SQL DDL statements to create and manipulate SQL objects like tables, views, indexes, sequences, and synonyms. It details steps to create a database, define tables for student records, alter them, and create various views based on specific criteria. Additionally, it covers the implementation of auto-increment functionality in sequences and the creation and management of indexes.
working with python
working with pythonbhavesh lande Linear regression and logistic regression are two machine learning algorithms that can be implemented in Python. Linear regression is used for predictive analysis to find relationships between variables, while logistic regression is used for classification with binary dependent variables. Support vector machines (SVMs) are another algorithm that finds the optimal hyperplane to separate data points and maximize the margin between the classes. Key terms discussed include cost functions, gradient descent, confusion matrices, and ROC curves. Code examples are provided to demonstrate implementing linear regression, logistic regression, and SVM in Python using scikit-learn.
applications and advantages of python
applications and advantages of pythonbhavesh lande The document discusses various popular Python applications and projects, highlighting datasets like wine quality prediction, loan prediction, time series analysis, Twitter classification, recommendation engines, and handwritten recognition with MNIST. It outlines the advantages of Python over other programming languages, emphasizing its cost-effectiveness, efficiency, and ease of use, which makes it a preferred choice for data scientists and big organizations like Google and NASA. Overall, Python's robustness has established it as a key player in data science and machine learning applications.
introduction of python in data science
introduction of python in data sciencebhavesh lande Python is an open-source high-level programming language developed in the 1980s by Guido Rossum, designed to be flexible, portable, and easy to learn. It features dynamic typing, extensive libraries, and a unique syntax that uses whitespace for code blocks. Python has evolved through numerous versions, with Python 3.7.1 being the latest stable release, making it a preferred language for data science and application development.
tools
toolsbhavesh lande Jupyter Notebook is a popular open-source tool that allows users to create documents containing code, equations, visualizations, and text. It supports Python, R, Scala, and Julia and is commonly used for tasks like data cleaning, transformation, modeling, and visualization. R Studio is also open-source and used for operations on data using the R language, including packages for manipulation and visualization. SAS was one of the first analytics tools and was designed for descriptive and predictive analytics. It has been used for over 40 years for statistical analysis and decision making.
data scientists and their role
data scientists and their rolebhavesh lande Data scientists are skilled professionals who analyze both structured and unstructured data to derive insights, employing various quantitative skills such as mathematics, statistics, and coding in languages like R and Python. Unlike data analysts who primarily focus on data processing and mining, data scientists create models for machine learning and data visualization to aid decision-making. Despite high demand for data scientists, there exists a skills gap, particularly among freshers who may lack the practical experience that experienced candidates possess.
applications
applicationsbhavesh lande Data science has significant applications in various IT fields, including recommender systems that suggest content based on user habits and preferences, and voice/image recognition technologies like AI cameras and virtual assistants. Additionally, it plays a crucial role in spam and fraud detection, improving the efficiency of email management and preventing fraudulent activities. These applications highlight data science's value in solving everyday problems and enhancing user experience.
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