INTRODUCTION TO PROGMMING R23

UNIT-I :
Introduction to Programming and Problem Solving :
History of Computers, Basic organization of a computer: ALU, input-output units, memory, program counter, Introduction to Programming Languages, Basics of a Computer Program- Algorithms, flowcharts (Using Dia Tool), pseudo code. Introduction to Compilation and Execution, Primitive Data Types, Variables, and Constants, Basic Input and Output, Operations, Type Conversion, and Casting. Problem-solving techniques: Algorithmic approach, characteristics of algorithm, Problem solving strategies: Top-down approach, Bottom-up approach, Time and space complexities of algorithms.

Unit-1 material

UNIT-II :
Control Structures :
Simple sequential programs Conditional Statements (if, if-else, switch), Loops (for, while, do- while) Break and Continue.

Unit-2 material

UNIT-III :
Arrays and Strings :
Arrays indexing, memory model, programs with array of integers, two dimensional arrays, Introduction to Strings.

Unit-3 material

UNIT-IV :
Pointers & User Defined Data types :
Pointers, dereferencing and address operators, pointer and address arithmetic, array manipulation using pointers, User-defined data types-Structures and Unions.

Unit-4 material

UNIT-V :
Functions & File Handling :
Introduction to Functions, Function Declaration and Definition, Function call Return Types and Arguments, modifying parameters inside functions using pointers, arrays as parameters. Scope and Lifetime of Variables, Basics of File Handling

Unit-5 material

Comments

DWDM lab exp2

Explore machine learning tool“WEKA”//2.a// Explore WEKA Data Mining/Machine Learning Toolkit. ANS: WEKA(Waikato Environment for Knowledge Analysis) an open-source software provides tools for data preprocessing, implementation of several Machine Learning algorithms, and visualization tools so that we can develop machine learning techniques and apply them to real-world data mining problems. Features of WEKA - Preprocessor – Most of the Data is Raw. Hence, Preprocessor is used to clean the noisy data. Classify – After preprocessing the data, we assign classes or categories to items. Cluster – In Clustering, a dataset is arranged in different groups/clusters based on some similarities. Associate – Association rules highlight all the associations and correlations between items of a dataset. Select Attributes – Every dataset contains a lot of attributes; only significantly valuable attributes are selected for building a good model. Visualize – In Visualization, different plot matrices ...

DWDM 2b ,2c experiment

//2b// Downloading and/or installation of WEKA data mining toolkit. Go to the Weka website, http://www.cs.waikato.ac.nz/ml/weka/ , and download the software. Select the appropriate link corresponding to the version of the software based on your operating system and whether or not you already have Java VM running on your machine. The link will forward you to a site where you can download the software from a mirror site. Save the self-extracting executable to disk and then double click on it to install Weka. Answer yes or next to the questions during the installation. Click yes to accept the Java agreement if necessary. After you install the program Weka should appear on your start menu under Programs (if you are using Windows). Running Weka from the start menu select Programs, then Weka. You will see the Weka GUI Chooser. Select Explorer. The Weka Explorer will then launch. //2c// Understand the features of WEKA toolkit such as Explorer, Knowledge Flow interface, Experimenter, comm...

Dwdm 1exp 2 bit

1.experiment 2 . Design multi-dimensional data models namely Star, Snowflake and Fact Constellation schemas forany one enterprise (ex. Banking, Insurance, Finance, Healthcare, manufacturing, Automobiles, sales etc). What is Schema? Schema is a logical description of the entire database. Star Schema: A star schema is the elementary form of a dimensional model, in which data are organized into facts and dimensions . This dimension table contains the set of attributes. The following diagram shows the sales data of a company with respect to the four dimensions, namely time, item, branch, and location . There is a fact table at the center. It contains the keys to each of four dimensions . Snowflake Schema: Some dimension tables in the Snowflake schema are normalized. The normalization splits up the data into additional tables. Fact Constellation Schema: A Fact constellation means two or more fact tables sharing one or more dim...

Jhansi Rani Kunaparaju

Search This Blog