This document contains solutions to questions from a computer science examination. It includes questions on topics like Python, Pandas, SQL, data visualization, and computer networks. The solutions demonstrate how to write Python code to create and manipulate dataframes, plot charts, and perform SQL queries. Examples of network topologies and devices like switches, modems, and gateways are also provided. The document aims to test students' understanding of key concepts in informatics practices.
Question Paper Code 065 informatic Practice New CBSE - 2021 FarhanAhmade
This document provides instructions for a test paper containing 40 questions divided into two parts (Part A and Part B). Part A has two sections - Section I containing 21 short answer questions and Section II containing two case study questions with subparts. Part B is divided into three sections testing different skills with options and varying mark values. The document outlines the question types, number of questions, distribution of marks and provides general instructions for answering the paper.
Caliban: Functional GraphQL Library for ScalaPierre Ricadat
Caliban is a library for GraphQL in Scala. It was designed with the goal of reducing boilerplate to a minimum while exposing a purely functional interface. In this talk, we’ll discover how to create a simple GraphQL API from the ground up, then we’ll dig into advanced features such as query optimization and middlewares. Finally, we will take a look at the recently released GraphQL client support.
Dapper caches query information like SQL statements and parameters to improve performance when materializing objects from query results. The cache is stored in a ConcurrentDictionary that is never flushed, so it could cause memory issues with dynamically-generated SQL. Queries using parameters are preferred since the cache key depends on the SQL and parameters, allowing caching of the execution plan. Buffering determines if all rows are loaded into memory before iterating. QueryMultiple is used for queries returning multiple result sets. Dirty tracking with interfaces allows Dapper to detect whether updates actually changed data to skip unnecessary SQL generation.
Characteristics of Java and basic programming constructs like Data types, Variables, Operators, Control Statements, Arrays are discussed with relevant examples
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
---------------------------------------------------------------
Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
---------------------------------------------------------------
Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Question Paper Code 065 informatic Practice New CBSE - 2021 FarhanAhmade
This document provides instructions for a test paper containing 40 questions divided into two parts (Part A and Part B). Part A has two sections - Section I containing 21 short answer questions and Section II containing two case study questions with subparts. Part B is divided into three sections testing different skills with options and varying mark values. The document outlines the question types, number of questions, distribution of marks and provides general instructions for answering the paper.
Caliban: Functional GraphQL Library for ScalaPierre Ricadat
Caliban is a library for GraphQL in Scala. It was designed with the goal of reducing boilerplate to a minimum while exposing a purely functional interface. In this talk, we’ll discover how to create a simple GraphQL API from the ground up, then we’ll dig into advanced features such as query optimization and middlewares. Finally, we will take a look at the recently released GraphQL client support.
Dapper caches query information like SQL statements and parameters to improve performance when materializing objects from query results. The cache is stored in a ConcurrentDictionary that is never flushed, so it could cause memory issues with dynamically-generated SQL. Queries using parameters are preferred since the cache key depends on the SQL and parameters, allowing caching of the execution plan. Buffering determines if all rows are loaded into memory before iterating. QueryMultiple is used for queries returning multiple result sets. Dirty tracking with interfaces allows Dapper to detect whether updates actually changed data to skip unnecessary SQL generation.
Characteristics of Java and basic programming constructs like Data types, Variables, Operators, Control Statements, Arrays are discussed with relevant examples
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
---------------------------------------------------------------
Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
---------------------------------------------------------------
Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
This document discusses methods in Java programming. It defines methods as collections of statements grouped together to perform operations. Methods can take parameters and return values. When a method is invoked, an activation record is created on the call stack to store its parameters and variables. The document discusses defining methods, passing arguments, return values, and method overloading. It also provides examples of tracing method calls and return values on the call stack.
09 a1ec01 c programming and data structuresjntuworld
This document contains questions for an exam on C programming and data structures. It asks students to answer any 5 of the following 8 questions:
1. Explain functions of preprocessor, compiler, linker and provide a flowchart to find maximum and minimum of 3 numbers.
2. Write C expressions for various mathematical operations and determine differences between C terms. Provide a program to check if a number is prime.
3. Write recursive and iterative functions to calculate power and provide a program to print currency denominations for a given amount.
4. Trace a sample program and determine its output.
5. Provide structures for college data and a function to calculate student strength in a college.
6.
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
This document provides an overview of effective numerical computation in NumPy and SciPy. It discusses how Python can be used for numerical computation tasks like differential equations, simulations, and machine learning. While Python is initially slower than languages like C, libraries like NumPy and SciPy allow Python code to achieve sufficient speed through techniques like broadcasting, indexing, and using sparse matrix representations. The document provides examples of how to efficiently perform tasks like applying functions element-wise to sparse matrices and calculating norms. It also presents a case study for efficiently computing a formula that appears in a machine learning paper using different sparse matrix representations in SciPy.
Kaggle Winning Solution Xgboost algorithm -- Let us learn from its authorVivian S. Zhang
This document provides an overview of XGBoost, an open-source gradient boosting framework. It begins with introductions to machine learning algorithms and XGBoost specifically. The document then walks through using XGBoost with R, including loading data, running models, cross-validation, and prediction. It discusses XGBoost's use in winning the Higgs Boson machine learning competition and provides code to replicate its solution. Finally, it briefly covers XGBoost's model specification and training objectives.
Applying Linear Optimization Using GLPKJeremy Chen
A brief introduction to linear optimization with a focus on applying it with the high-quality open-source solver GLPK.
Originally prepared for an intra-department sharing session.
This document provides details about a student project titled "Multifunctional Tools" created using Python. The project allows users to perform various mathematical and logical operations through a graphical user interface. It includes functions for calculations, string manipulation, ASCII conversions, checking vowels/consonants, palindromes, prime numbers and more. The project was created by the student to provide a single platform for different operations and help users with schoolwork. It makes use of Python modules and functions along with a Tkinter GUI.
This document provides a lab manual for the course GE3171 - Problem Solving and Python Programming Laboratory (REG-2021). It contains details of various programming exercises to be completed as part of the course curriculum. The exercises cover topics like:
1. Developing flow charts and Python programs for real-life problems like electricity billing, retail shop billing, etc.
2. Python programming using simple statements, expressions, and calculations.
3. Scientific problems using conditionals and iterative loops to generate number series and patterns.
4. Implementing applications using lists, tuples to represent library items, car components, construction materials.
5. Implementing applications using sets and dictionaries for language analysis and car
This document discusses using machine learning with R for data analysis. It covers topics like preparing data, running models, and interpreting results. It explains techniques like regression, classification, dimensionality reduction, and clustering. Regression is used to predict numbers given other numbers, while classification identifies categories. Dimensionality reduction finds combinations of variables with maximum variance. Clustering groups similar data points. R is recommended for its statistical analysis, functions, and because it is free and open source. Examples are provided for techniques like linear regression, support vector machines, principal component analysis, and k-means clustering.
This document provides an overview of a machine learning course that teaches Pandas basics. The course aims to teach students how to handle and visualize data, apply basic learning algorithms, develop supervised and unsupervised learning techniques, and build machine learning models. The document outlines the course objectives, outcomes, syllabus including data preprocessing, feature extraction, and data visualization techniques. It also provides references for further reading.
Log Analytics in Datacenter with Apache Spark and Machine LearningPiotr Tylenda
Presented during DataMass Summit 2017.
http://summit2017.datamass.io/
https://www.youtube.com/watch?v=eGJfhHPdhuo
Data center workloads produce a significant amount of log data which has to be analyzed in order to discover any potential issues. We present an automated text mining approach for workload monitoring and data analytics, which is a combination of machine learning and big data processing. This session provides an overview of a data pipeline based on key components such as Apache Kafka, Apache Spark and generalized version of k-means algorithm.
Log Analytics in Datacenter with Apache Spark and Machine LearningAgnieszka Potulska
This document discusses using Apache Spark and machine learning for log analytics in a data center. It covers collecting workload logs in Kafka and analyzing them using Spark Streaming, ELK stack, and Spark machine learning. Key techniques discussed include TF-IDF, word2vec, k-means clustering algorithm, and visualizing clustered logs. The document provides an example PySpark pipeline for preprocessing logs, creating word embeddings, and running k-means clustering on the data.
In this article you will learn hot to use tensorflow Softmax Classifier estimator to classify MNIST dataset in one script.
This paper introduces also the basic idea of a artificial neural network.
We have analysed the Ecommerce Purchase .csv file and tried using all basic Python, Numpy and Pandas libraries to come up a insights. Enjoy the code snippet and leave us a feedback
The document discusses defining and using methods in Java. It defines what a method is and its key components like the method signature, return type, parameters, and body. It then demonstrates a sample max method to return the maximum of two numbers and traces the steps of invoking the method from the main method, including passing arguments, executing the method body, and returning the result. The document aims to explain the basics of methods in Java, including how to define reusable methods and invoke them to perform certain tasks.
The document discusses defining and using methods in Java. It defines what a method is and its key components like the method signature, return type, parameters, and body. It then demonstrates a sample max method to return the maximum of two numbers and traces the steps of invoking the method from the main method, including passing arguments, executing the method body, and returning the result. The document aims to explain the basics of methods in Java, including how to define reusable methods and invoke them to perform certain tasks.
This document discusses parameters and graphics in Python. It covers:
- Using constants by declaring variables at the top of code
- Drawing graphics using the DrawingPanel module
- Defining functions with parameters and default parameter values
- Drawing shapes, lines, polygons using methods like create_rectangle, create_oval, and animation using the sleep function.
The document contains 20 practice exercises involving Java programming concepts like variables, data types, operators, methods, and control flow. The exercises include writing code to calculate mathematical expressions, convert between temperature scales, find averages, and determine output based on different logical and relational expressions. Sample code is provided and students are asked questions to test their understanding of Java syntax and program execution order.
This document contains sections on arrays in C programming including: declaring and initializing arrays; passing arrays to functions; sorting arrays; and computing statistics like mean, median, and mode from array data. Key points include: arrays allow grouping related data under one name; elements are accessed via subscript notation; arrays can be passed to functions by reference, allowing the function to modify the original array; sorting algorithms like bubble sort rearrange array elements into order; and common statistics like average, middle value, and most frequent value can be calculated from array data.
Metric learning is an area of machine learning which aims to learn a distance (or similarity) measure between samples for a given task. In this presentation, I will start by briefly introducing the main ideas of metric learning and some of its applications, and show a concrete example of using metric-learn, the metric learning library in Python. I will then highlight the importance of making a machine learning package compatible with scikit-learn and discuss the challenges in the specific case of metric-learn, in particular regarding API constraints. Finally, we will dig into metric-learn's code to illustrate the main design choices, and emphasize some general issues (such as test design) that require special care when developing a machine learning toolbox.
https://github.com/metric-learn/metric-learn
This document discusses methods in Java programming. It defines methods as collections of statements grouped together to perform operations. Methods can take parameters and return values. When a method is invoked, an activation record is created on the call stack to store its parameters and variables. The document discusses defining methods, passing arguments, return values, and method overloading. It also provides examples of tracing method calls and return values on the call stack.
09 a1ec01 c programming and data structuresjntuworld
This document contains questions for an exam on C programming and data structures. It asks students to answer any 5 of the following 8 questions:
1. Explain functions of preprocessor, compiler, linker and provide a flowchart to find maximum and minimum of 3 numbers.
2. Write C expressions for various mathematical operations and determine differences between C terms. Provide a program to check if a number is prime.
3. Write recursive and iterative functions to calculate power and provide a program to print currency denominations for a given amount.
4. Trace a sample program and determine its output.
5. Provide structures for college data and a function to calculate student strength in a college.
6.
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
This document provides an overview of effective numerical computation in NumPy and SciPy. It discusses how Python can be used for numerical computation tasks like differential equations, simulations, and machine learning. While Python is initially slower than languages like C, libraries like NumPy and SciPy allow Python code to achieve sufficient speed through techniques like broadcasting, indexing, and using sparse matrix representations. The document provides examples of how to efficiently perform tasks like applying functions element-wise to sparse matrices and calculating norms. It also presents a case study for efficiently computing a formula that appears in a machine learning paper using different sparse matrix representations in SciPy.
Kaggle Winning Solution Xgboost algorithm -- Let us learn from its authorVivian S. Zhang
This document provides an overview of XGBoost, an open-source gradient boosting framework. It begins with introductions to machine learning algorithms and XGBoost specifically. The document then walks through using XGBoost with R, including loading data, running models, cross-validation, and prediction. It discusses XGBoost's use in winning the Higgs Boson machine learning competition and provides code to replicate its solution. Finally, it briefly covers XGBoost's model specification and training objectives.
Applying Linear Optimization Using GLPKJeremy Chen
A brief introduction to linear optimization with a focus on applying it with the high-quality open-source solver GLPK.
Originally prepared for an intra-department sharing session.
This document provides details about a student project titled "Multifunctional Tools" created using Python. The project allows users to perform various mathematical and logical operations through a graphical user interface. It includes functions for calculations, string manipulation, ASCII conversions, checking vowels/consonants, palindromes, prime numbers and more. The project was created by the student to provide a single platform for different operations and help users with schoolwork. It makes use of Python modules and functions along with a Tkinter GUI.
This document provides a lab manual for the course GE3171 - Problem Solving and Python Programming Laboratory (REG-2021). It contains details of various programming exercises to be completed as part of the course curriculum. The exercises cover topics like:
1. Developing flow charts and Python programs for real-life problems like electricity billing, retail shop billing, etc.
2. Python programming using simple statements, expressions, and calculations.
3. Scientific problems using conditionals and iterative loops to generate number series and patterns.
4. Implementing applications using lists, tuples to represent library items, car components, construction materials.
5. Implementing applications using sets and dictionaries for language analysis and car
This document discusses using machine learning with R for data analysis. It covers topics like preparing data, running models, and interpreting results. It explains techniques like regression, classification, dimensionality reduction, and clustering. Regression is used to predict numbers given other numbers, while classification identifies categories. Dimensionality reduction finds combinations of variables with maximum variance. Clustering groups similar data points. R is recommended for its statistical analysis, functions, and because it is free and open source. Examples are provided for techniques like linear regression, support vector machines, principal component analysis, and k-means clustering.
This document provides an overview of a machine learning course that teaches Pandas basics. The course aims to teach students how to handle and visualize data, apply basic learning algorithms, develop supervised and unsupervised learning techniques, and build machine learning models. The document outlines the course objectives, outcomes, syllabus including data preprocessing, feature extraction, and data visualization techniques. It also provides references for further reading.
Log Analytics in Datacenter with Apache Spark and Machine LearningPiotr Tylenda
Presented during DataMass Summit 2017.
http://summit2017.datamass.io/
https://www.youtube.com/watch?v=eGJfhHPdhuo
Data center workloads produce a significant amount of log data which has to be analyzed in order to discover any potential issues. We present an automated text mining approach for workload monitoring and data analytics, which is a combination of machine learning and big data processing. This session provides an overview of a data pipeline based on key components such as Apache Kafka, Apache Spark and generalized version of k-means algorithm.
Log Analytics in Datacenter with Apache Spark and Machine LearningAgnieszka Potulska
This document discusses using Apache Spark and machine learning for log analytics in a data center. It covers collecting workload logs in Kafka and analyzing them using Spark Streaming, ELK stack, and Spark machine learning. Key techniques discussed include TF-IDF, word2vec, k-means clustering algorithm, and visualizing clustered logs. The document provides an example PySpark pipeline for preprocessing logs, creating word embeddings, and running k-means clustering on the data.
In this article you will learn hot to use tensorflow Softmax Classifier estimator to classify MNIST dataset in one script.
This paper introduces also the basic idea of a artificial neural network.
We have analysed the Ecommerce Purchase .csv file and tried using all basic Python, Numpy and Pandas libraries to come up a insights. Enjoy the code snippet and leave us a feedback
The document discusses defining and using methods in Java. It defines what a method is and its key components like the method signature, return type, parameters, and body. It then demonstrates a sample max method to return the maximum of two numbers and traces the steps of invoking the method from the main method, including passing arguments, executing the method body, and returning the result. The document aims to explain the basics of methods in Java, including how to define reusable methods and invoke them to perform certain tasks.
The document discusses defining and using methods in Java. It defines what a method is and its key components like the method signature, return type, parameters, and body. It then demonstrates a sample max method to return the maximum of two numbers and traces the steps of invoking the method from the main method, including passing arguments, executing the method body, and returning the result. The document aims to explain the basics of methods in Java, including how to define reusable methods and invoke them to perform certain tasks.
This document discusses parameters and graphics in Python. It covers:
- Using constants by declaring variables at the top of code
- Drawing graphics using the DrawingPanel module
- Defining functions with parameters and default parameter values
- Drawing shapes, lines, polygons using methods like create_rectangle, create_oval, and animation using the sleep function.
The document contains 20 practice exercises involving Java programming concepts like variables, data types, operators, methods, and control flow. The exercises include writing code to calculate mathematical expressions, convert between temperature scales, find averages, and determine output based on different logical and relational expressions. Sample code is provided and students are asked questions to test their understanding of Java syntax and program execution order.
This document contains sections on arrays in C programming including: declaring and initializing arrays; passing arrays to functions; sorting arrays; and computing statistics like mean, median, and mode from array data. Key points include: arrays allow grouping related data under one name; elements are accessed via subscript notation; arrays can be passed to functions by reference, allowing the function to modify the original array; sorting algorithms like bubble sort rearrange array elements into order; and common statistics like average, middle value, and most frequent value can be calculated from array data.
Metric learning is an area of machine learning which aims to learn a distance (or similarity) measure between samples for a given task. In this presentation, I will start by briefly introducing the main ideas of metric learning and some of its applications, and show a concrete example of using metric-learn, the metric learning library in Python. I will then highlight the importance of making a machine learning package compatible with scikit-learn and discuss the challenges in the specific case of metric-learn, in particular regarding API constraints. Finally, we will dig into metric-learn's code to illustrate the main design choices, and emphasize some general issues (such as test design) that require special care when developing a machine learning toolbox.
https://github.com/metric-learn/metric-learn
Similar to Informatics Practices (new) solution CBSE 2021, Compartment, improvement examination. (20)
Engineering Mathematics for 4th Semester. FarhanAhmade
In these notes, you will get all the best and simple explanations of topics for 4th Semester Engineering aspirants. This includes examples and the best ways to solve all the tricky questions.
Topics covered in this notes are:-
1. Introduction to Graph Theory.
2. Planner and Trees.
3. Recurrence Relations.
4. Joint Random Variables.
5. Numerical Methods.
Discrete mathematical structure complete notes of 3rd semester B.tech.FarhanAhmade
In this, you will get the questions and answers to the problems of maths of the 3rd semester of B.tech (CS, IT, CTIS).
Topics Covered in this notes are:
1. Mathematical Logic.
2. Relations and Functions.
3. Combinations.
4. Induction, Recursion.
5. Group and Coding Theory.
Short note on Peripheral devices Operating System.FarhanAhmade
This 3 page document discusses peripheral devices. It was written by Mohammed Farhan, a student with ID 19BBTIT051 studying IT (CT/IS). The document covers peripheral devices over its 3 pages but provides no other details.
In this, you will get
1. Binary to decimal and vice versa too.
2. Fractions.
3. Complements. (1's and 2's).
4. Binary addition.
5. Binary Subtraction.
6. Micro-operations.
This document appears to be a 5-page computer networks assignment belonging to Mohammed Farhan, a student with ID 19BBTIT051 studying IT (CT/IS). Each page contains the same header information identifying the assignment, student name and ID, and course of study.
This document appears to be a 4 page computer networks assignment belonging to Mohammed Farhan, a student with ID 19BBTIT051 studying IT (CT/IS). Each page of the assignment contains the student's name, ID, and program of study.
Book referencing single, two, three and multiple authors; Newspaper referenci...FarhanAhmade
The document is a 15-page assignment on referencing for Mohammed Farhan's Professional Communication-III class. It includes his name, student ID, program of study, and semester on each page. The assignment focuses on referencing skills.
This document is a 16 page report on cloud computing submitted by Mohammed Farhan to CMR University. It includes a title page, certificate page, acknowledgments, table of contents, list of illustrations, and 6 pages of the report itself. The report provides an introduction to cloud computing, discusses cloud computing basics like types of clouds and stakeholders. It also covers cloud architecture, advantages of cloud computing, and applications of cloud computing in India like e-governance and rural development. References and the professor's approval are also included.
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This document describes the design of an autonomous firefighting robot. The robot uses various sensors and components to locate and extinguish fires on its own without human assistance. It is equipped with IR fire sensors to detect the direction of fires, a motor driver and DC motors to navigate towards the fire, and a servo motor mounted water pump to extinguish fires. The goal is to create a robot that can reduce fire accidents and save lives and property through fully autonomous fire detection and response.
This document describes a radiation detector project using an Arduino Uno. The project detects radiations and frequencies in the surrounding environment. The key components are an Arduino Uno, radiation sensor, jumper wires, buzzer, LED, breadboard, and 220 ohm resistor. The radiation sensor detects radioactive radiation and other signals. When radiation is detected, the buzzer will sound and LED will light up. The Arduino Uno controls the components and functions. The purpose is to check for radiation from devices, towers, and other sources in the environment.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
3. Informatics Practices (New) Solution
CBSE – 2021 Compt., Improvement Examination
Page 3 of 13
df=pd.DataFrame(t, index=[10,20,30,40,50,60])
print(df)
Output: -
rollno Name Age marks class
10 1 Krishna 15 70.4 11A
20 2 Pranshu 14 60.9 12B
30 3 Gurusha 14 80.3 11B
40 4 Arpit 15 87.5 12B
50 5 Rani 16 67.8 12B
60 6 Aurobindo 15 86.0 11B
1).Print (df. iloc [1])
2).Print(df[‘marks’].max ())
3).df. insert (loc=2, column=’fee’, values=[3200,3400,4500,3100,3200,4000])
4).df. drop (‘Age’, axis=1, inplace = True)
5).df. rename (columns={‘marks’:’Term1’}, inplace = True)
Q.23. click for codes to create table.
1). select * from HOTEL where Location = ‘London’;
2). select Hotel_Id, H_Name, Location, Room_Type, Price, Star from HOTEL where Price >
6000 order by Price;
3). select H_Name from HOTEL where H_Name like “%e”;
4). select count (distinct Room_type) from HOTEL;
5). select H_Name from HOTEL order by H_Name desc;
4. Informatics Practices (New) Solution
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Q.24.
import pandas as pd
h= ['H','a','p','p','y']
s = pd.Series(data=h)
print(s[0:3])
Output: -
0 H
1 a
2 p
dtype: object
Q.25.(A). Erro: SQL is case sensitive language. In given command the starting with
uppercase and the continue with lower case cause the error.
Solution: select emp_name from emp where comm = NULL;
OR SELECT emp_name FROM emp WHERE comm = NULL;
In the solution, all the functions are in same case from starting to end except the table date
names.
(B). No, because WHERE clause specifies search conditions for the rows returned by the
Query and limits rows to a specific row-set. If a table has huge number of records and if
someone wants to get the particular records then using ‘where’ clause is useful. Whereas
GROUP BY clause summaries identical rows into a single/distinct group and returns a
single row with the summary for each group, by using appropriate Aggregate function in the
SELECT list, like COUNT(), SUM(), MIN(), MAX(), AVG(), etc.
Having clause is used to restrict the number of records returned by the Group by clause.
Q.26. The count (*) returns all rows whether column contains NULL value or not, while
count (<Column Name>) returns the number of rows except NULL valued row / rows.
E.g.: - >> create table school (St_name char (25), Class int (3), Attendance int (4));
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>> select count (*) from school where Attendance < 45;
It will show all the name and classes which have attendance less than 45.
Q.27.
import pandas as pd
A=pd.Series([2,4,6], index = [0,1,2])
B=pd.Series([1,3,5], index = [1,2,3])
print(A)
print(B)
1).S = (A+B)
2).S = (A*B)
Q.28.
1).5.
2).Sun Cream 678
Beauty Cream 5400
Beauty Cream 1200
Q.29. (A).1). Substr (“Innovation”,3,4)
nova
2). ation
(B). 1). 3.
2).1.
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Q.30.
import pandas as pd
stu = {'Admno':[1001,1002,1003,1004,1005],
'Firstname':['Amit','Rohit','Shyam','Manan','Raman'],
'Lastname':['Sehgal','Malik','Bhatia','Gupta','Bajaj'],
'Age':[18,17,18,16,18],
'Pretest':[87,89,90,79,922],
'Posttest':[67,78,84,69,70]}
d=pd.DataFrame(stu)
print(d)
1). d.loc[::-1].head()
2). g = {'Admno':1006, 'Firstname':'Sujal', 'Lastname':'Sharma', 'Age':17, 'Pretest':87,
'Posttest':89}
d = d.append(g,ignore_index=True)
print(d)
Q.31. VoIP stands for (Voice Over Internet Protocol) is the technology that converts your
voice into a digital signal, allowing you to make a call directly from a computer, VoIP phone, or
other data – driven devices. Examples of software / apps are: Skype, IMO, Instagram,
Facebook, etc.
Q.32.There is main health hazard that can occur due to excessive use of computer / smart
phone screes,
→ Physical health Effects:
1. Eyestrain,
2. Poor Posture,
3. Sleeping Problems,
4. Reduced Physical Activities.
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→ Psychological Effects:
1. Isolation,
2. Depression,
3. Irritation,
4. Decrement in creativity thinking skills.
Q.33.
1. URL stands for Uniform Resource Locator,
2. It was invented with the development of www by Tim Berners – Lee.
3. A URL is a web address of a given unique resource on the web.
4. It is the mechanism used by browsers to retrieve any published resource on the web.
5. E.g.: - https://www.google.com . (Https://) – Protocol; (www.google.com) – Domain
name.
Q.34.
import pandas as pd
S1 = pd.Series(data=[2,3,1])
print(S1)
1).
0 8
1 27
2 1
dtype: int64
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2).
0 6
1 9
2 3
dtype: int64
Q.35. (A). → For copyright: -
• It exists, without me doing anything to assert it, from the moment of creation.
• Unless explicitly assigned, or surrendered, it persists regardless of license chosen
for the software.
• It grants the creator very specific legal rights and remedies (although, these may
vary by jurisdiction).
• Most forms of copyright have a defined duration (usually life + 'n' years).
• In an open-source project, every contributor retains copyright in their own
contributions.
→For the License: -
• It is a legal document
• I have to explicitly choose, or create, the license. It does not apply automatically.
• It grants users of the software specific, and limited, rights.
• Unless stated in the License itself, or until it is revoked, it remains enforceable in
perpetuity.
(B). Intellectual property rights are the rights given to persons over the creations of
their minds. They usually give the creator an exclusive right over the use of his/her creation
for a certain period of time.
The protection of Intellectual Property Rights (IPR) is important for the economy and for
its further growth in areas such as research, innovation and employment. Effective IPR
enforcement is also essential to health and safety. ... For these reasons, IP rights are worth
protecting, both domestically and internationally.
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Q.36. (A).import matplotlib.pyplot as plt
import numpy as np
plt.plot([1.0,2.0,3.0,4.0,5.0], [12,14,13,14.8,18.9])
plt.xlabel('Tests')
plt.ylabel('Marks Secured')
plt.show()
(B).import matplotlib. pyplot as pl
info = ['Gold', 'Silver', 'Bronze', 'Total']
Australia = [80,59,59,198]
pl.bar(info,Australia)
pl.title("AUSTRIALIA MEDAL PLOT")
pl.xlabel("Medals Won by Austraila")
pl.ylabel("Marks won")
pl.show()
*Questions for visually impaired:
(A). Data visualization is the graphical representation of information and data. By
using visual elements like charts, graphs, and maps, data visualization tools provide an
accessible way to see and understand trends, outliers, and patterns in data.
In the world of Big Data, data visualization tools and technologies are essential to analyze
massive amounts of information and make data-driven decisions.
Like matplotlib , Pandas, etc.
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(B).1). Plot (): - The plot () function in pyplot module of matplotlib library is used to make
a 2D hexagonal binning plot of points x, y. Parameters: This method accept the following
parameters that are described below: x, y: These parameters are the horizontal and vertical
coordinates of the data points. x values are optional.
2). Show (): - The show () function in pyplot module of matplotlib library is used to display
all figures.
3). Savefig (): - Savefig () As the name suggests Savefig () method is used to save the figure
created after plotting data. The figure created can be saved to our local machines by using
this method.
Q.37. 1). select House, count(House)
-> from School
-> group by House
-> having count(House) >2;
2). select avg (Percentage) from School;
3). select min (Percentage) from School where Class = 10;
Q.38.(A).import pandas as pd
d = {'country':['Italy', 'Spain','Greece', 'France', 'Portugal'],
'population':[61,46,11,65,10],
'percent':[0.83,0.63,0.15,0.88,0.14]}
df1 = pd.DataFrame(d, index=['IT','ES','Gr','Fr','Po'])
print(df1)
Output: -
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(B). 1).print(df1[df1.columns[0:2]])
2). print (df1[(df1[“population”]>40)])
3).df1.drop(df1.tail(2).index, inplace=True)
Q.39.(A). 1).select dayname(‘2000-07-05’);
2).select lcase(“e-mail-id”);
3).select length(“King”);
4).select substring (“King”,1,1);
5).select avg (marks) from class;
(B). 1).select substring (Itemname,1,3) from ITEM;
2).select monthname (Stockdate) from ITEM; //given
date is not correct format to use ‘monthname’ function. You have to change the format in
‘YYYY-MM-DD’ then you will able to get the correct output.
3).select sum (Price) from ITEM;
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4).select avg (Price) from ITEM;
5).select round (Price,2) from ITEM;
Q.40. a).Star Topology.
b).Administrative Wing (W1), because at W1 all of the record will store and easy to
access in all conditions.
c). LAN, because it covers up to 10 kms’ of radius.
d).As per suggested layout separate repeaters need not to be installed as each
building or wing, will be having a hub that acts as repeater. One hub per wing is
enough.
e).School can use cloud-based applications for sharing any type of data, like: Google
drive, One Drive, AWS drive services, etc. to share their files and make it save for future
use.
*Questions for visually impaired:
(a). A network switch connects devices (such as computers, printers, wireless access points) in
a network to each other, and allows them to 'talk' by exchanging data packets. Switches
can be hardware devices that manage physical networks, as well as software-based virtual
devices.
(b). Bus Topology and Star Topology.
(c). A modulator-demodulator, or simply a modem, is a hardware device that converts data
from a digital format, intended for communication directly between devices with specialized
wiring, into one suitable for a transmission medium such as telephone lines or radio.
(d). A gateway is a network node used in telecommunications that connects two
networks with different transmission protocols together. Gateways serve as an entry and
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exit point for a network as all data must pass through or communicate with the gateway prior
to being routed.
(e). Voice over Internet Protocol (VoIP), is a technology that allows you to make voice calls
using a broadband Internet connection instead of a regular (or analog) phone line.