Java Keywords with Their Definitions and Complete Java Terminology with Definitions

 

Prepared By : Prof. Uday Shah (HOD-IT)

Java Keywords and Their Definitions

A

• abstract – Used to declare a class or method as abstract. Abstract classes cannot be instantiated, and abstract methods must be implemented by subclasses.

• assert – Used for debugging; tests a boolean condition, and throws an AssertionError if the condition is false.

B

• boolean – Data type that can hold only two values: true or false.

• break – Exits a loop or a switch statement immediately.

B (byte)

• byte – Data type that stores an 8-bit signed integer (range: -128 to 127).

C

• case – Defines a branch in a switch statement.

• catch – Used with try to handle exceptions.

• char – Data type for storing a single 16-bit Unicode character.

• class – Used to define a class.

• const – Reserved but not used in Java (for compatibility with C/C++).

• continue – Skips the current iteration of a loop and moves to the next.

D

• default – Specifies the default branch in a switch or provides a default implementation in an interface.

• do – Starts a do-while loop.

D (double)

• double – Data type for storing a 64-bit double-precision floating-point number.

E

• else – Specifies a block of code to execute if an if condition is false.

• enum – Declares a set of named constants.

• extends – Used in class inheritance to indicate a superclass.

F

• final – Used to declare constants, prevent method overriding, or prevent inheritance of a class.

• finally – A block in exception handling that always executes after try and catch.

• float – Data type for storing a 32-bit single-precision floating-point number.

• for – Used to start a for loop.

G

• goto – Reserved but not used in Java.

I

• if – Executes a block of code if a condition is true.

• implements – Indicates that a class implements an interface.

• import – Allows importing other classes or packages.

• instanceof – Tests whether an object is an instance of a specific class or subclass.

• int – Data type for storing a 32-bit signed integer.

• interface – Declares an interface.

• long – Data type for storing a 64-bit signed integer.

N

• native – Declares that a method is implemented in platform-dependent code using JNI.

• new – Creates new objects.

• null – Represents a null reference.

P

• package – Defines a package (namespace) for a group of related classes.

• private – Access modifier; accessible only within the same class.

• protected – Access modifier; accessible within the same package and subclasses.

• public – Access modifier; accessible from anywhere.

R

• return – Exits from a method and optionally returns a value.

S

• short – Data type for storing a 16-bit signed integer.

• static – Used for class-level variables or methods that belong to the class rather than instances.

• strictfp – Ensures floating-point calculations follow IEEE 754 standards.

• super – Refers to the superclass’s methods, variables, or constructors.

• switch – Starts a multi-branch selection statement.

• synchronized – Ensures that only one thread can access a block of code or method at a time.

T

• this – Refers to the current object.

• throw – Used to explicitly throw an exception.

• throws – Declares exceptions that a method might throw.

• transient – Prevents a variable from being serialized.

• try – Starts a block of code for exception handling.

• true – Boolean literal representing logical truth.

V

• var – Used for local variable type inference (Java 10+).

• void – Specifies that a method does not return any value.

• volatile – Indicates that a variable's value will be modified by different threads.

W

• while – Starts a while loop.

Complete Java Terminology with Definitions

A – C

Term	Definition
Abstract Class	A class declared with abstract keyword; cannot be instantiated and may contain abstract methods.
Abstract Method	A method declared without implementation, meant to be overridden in a subclass.
API (Application Programming Interface)	A set of classes and methods provided to interact with the Java library or other services.
Applet	A Java program embedded in a web page that runs in a browser or applet viewer. (Deprecated in modern Java)
AWT (Abstract Window Toolkit)	A set of APIs for creating GUI applications in Java.
Array	A fixed-size collection of elements of the same type stored in contiguous memory.
Bytecode	Platform-independent intermediate code generated by the Java compiler and executed by the JVM.
Class	A blueprint for creating objects; contains fields (variables) and methods.
Classpath	The location(s) from which the JVM or compiler loads classes and packages.
Constructor	A special method used to initialize objects when they are created.
Casting	Converting one data type to another, e.g., int to double (widening) or double to int (narrowing).
Checked Exception	Exceptions checked at compile time; must be declared in throws or handled using try-catch.
Collection Framework	A set of classes and interfaces (like List, Set, Map) for working with data collections.

D – F

Term	Definition
Delegation Event Model	A mechanism in Java for handling events in GUI applications.
Dynamic Binding	Method call resolution at runtime based on the object’s actual type.
Encapsulation	Bundling data and methods into a single unit (class) and restricting access via access modifiers.
Enum	A special Java type used to define collections of constants.
Exception	An event that disrupts the normal flow of a program; handled using try-catch-finally.
extends	Keyword used for class inheritance or interface extension.
final	Keyword used to declare constants, prevent inheritance, or prevent method overriding.
finalize()	A method called by garbage collector before reclaiming an object’s memory. (Deprecated in newer versions)
float	A 32-bit single-precision floating-point number.
Framework	A reusable set of libraries for building Java applications (e.g., Spring, Hibernate).

G – I

Term	Definition
Garbage Collection	Automatic memory management process that frees memory occupied by unused objects.
Generics	Enables type safety in collections and methods by parameterizing data types.
GUI (Graphical User Interface)	A user interface that includes buttons, menus, text fields, etc.
HashMap	A Java collection that stores key-value pairs using hashing for fast lookups.
Heap Memory	Runtime memory area where objects are stored.
HTTPServlet	A class in Java EE for handling HTTP requests and responses.
implements	Keyword used by a class to use an interface.
import	Keyword to include other Java classes or packages in a program.
Inheritance	Mechanism where a class acquires properties and behavior from another class.
Interface	A collection of abstract methods and constants used for defining a contract.
Instance Variable	A variable defined in a class for which each object has its own copy.

J – L

Term	Definition
JavaBeans	Reusable software components written in Java that follow specific naming conventions.
JDBC (Java Database Connectivity)	API for connecting and executing queries on a database.
JDK (Java Development Kit)	Includes JRE plus tools for Java development (compiler, debugger, etc.).
JIT (Just-In-Time Compiler)	Converts bytecode into native machine code at runtime for better performance.
JME (Java Micro Edition)	A version of Java designed for mobile and embedded devices.
JRE (Java Runtime Environment)	Software package that provides JVM and libraries for running Java applications.
JSP (JavaServer Pages)	Technology for creating dynamic web content using Java embedded in HTML.
JVM (Java Virtual Machine)	An engine that executes Java bytecode.
Local Variable	Variable declared within a method or block, accessible only there.
Loop	A control structure for executing a block of code repeatedly (for, while, do-while).

M – O

Term	Definition
main() Method	The entry point of a Java application.
Method Overloading	Defining multiple methods with the same name but different parameter lists.
Method Overriding	Redefining a superclass method in a subclass with the same signature.
Multithreading	Running multiple threads simultaneously for parallel execution.
MVC (Model-View-Controller)	Architectural pattern for separating business logic, UI, and data.
Nested Class	A class defined within another class.
NullPointerException	An exception thrown when trying to use an object reference that has not been assigned.
Object	An instance of a class containing data (fields) and behavior (methods).
Overloading	See Method Overloading.
Overriding	See Method Overriding.

P – R

Term	Definition
Package	A group of related classes and interfaces.
Polymorphism	The ability of an object to take many forms (method overloading and overriding).
PreparedStatement	JDBC statement used for executing parameterized SQL queries.
Primitive Data Types	Basic types like int, char, boolean, float, double, byte, short, long.
Private / Protected / Public	Access modifiers that control visibility of members.
Recursion	Method calling itself directly or indirectly.
Reference Variable	Variable that holds the memory address of an object.
Reflection	API for inspecting and modifying runtime behavior of applications.
ResultSet	JDBC object holding data retrieved from a database query.
RMI (Remote Method Invocation)	Java API for invoking methods on remote objects.
Runtime Exception	Exceptions that occur during program execution and are not checked at compile time.

S – U

Term	Definition
Servlet	Java class that handles HTTP requests and responses in a web application.
Session	A way to store user-specific data across multiple HTTP requests.
Socket	Endpoint for communication between two machines over a network.
Source Code	The .java file containing Java code written by the programmer.
Static	Keyword indicating that a field or method belongs to the class rather than instances.
String	A sequence of characters in Java, represented by the String class.
super	Keyword to call a superclass constructor or method.
Swing	A set of lightweight Java APIs for building GUI applications.
Synchronization	Mechanism to control access to shared resources in multithreaded environments.
Thread	A lightweight subprocess for parallel execution.
Throwable	The superclass of all errors and exceptions in Java.
try-catch-finally	Exception handling blocks in Java.
URL (Uniform Resource Locator)	Address of a resource on the internet.
User Interface (UI)	The part of an application that the user interacts with.

V – Z

Term	Definition
Variable	A named location in memory used to store data.
Vector	A synchronized dynamic array in Java.
Virtual Machine	Software that executes programs like a physical machine (e.g., JVM).
volatile	Keyword to indicate a variable’s value can be modified by multiple threads.
Web Container	Part of a web server that interacts with Java Servlets and JSPs.
Wrapper Classes	Classes that convert primitive types into objects (Integer, Double, etc.).
XML (Extensible Markup Language)	Used for data storage and transfer, often in Java web services.
XSS (Cross-Site Scripting)	Security vulnerability Java web developers must prevent.
Yield	A thread method suggesting the scheduler to give execution to other threads.
ZIP Streams	Java I/O classes to read/write compressed files.

Different Charts in Tableau for MCA 3 or IT Students

Prepared By : Prof. Uday Shah (HOD-IT) 

Different Charts in Tableau

1. Line Chart

• A Line Chart in Tableau shows data points connected by straight lines, often used to show trends over time.

• It is ideal for time series analysis, such as monthly sales, daily temperature, or yearly revenue.

• The X-axis generally represents time, and the Y-axis represents the measure you want to analyse.

• You can create a Line Chart by dragging a date field to Columns and a measure to Rows.

• Tableau automatically draws the connecting lines between points.

• You can change line style (solid, dashed, dotted) from the "Marks" card.

• It supports multiple lines for comparing different categories.

• You can use color encoding to differentiate lines.

• Adding trend lines in Tableau provides statistical insights.

• Works well with continuous data, not just discrete categories.

• Example: Monthly profit trend over a year.

• Practical Use: To track company performance over time.

2. Bar Chart

• A Bar Chart uses rectangular bars to represent data values.

• The length of the bar is proportional to the value it represents.

• It is widely used for comparing categories.

• Horizontal or vertical bars can be chosen depending on clarity.

• Created by dragging a dimension to Rows and a measure to Columns.

• Can be stacked or clustered.

• Supports sorting to arrange values in ascending or descending order.

• You can add color for better visual separation.

• Labels can be displayed to show exact values.

• Works well for categorical data comparisons.

• Example: Sales by region.

• Practical Use: To see which product category performs best.

3. Pie Chart

• A Pie Chart shows proportions as slices of a circle.

• The size of each slice represents the contribution to the total.

• Created by dragging a dimension to Color and a measure to Angle.

• Works best when comparing a few categories.

• Too many slices make it hard to read.

• Can display labels for percentage values.

• Allows colour encoding for different slices.

• Often used for share analysis.

• Can be combined with legends for clarity.

• Example: Market share by brand.

• Practical Use: To quickly understand category proportions.

4. Bubble Chart

• A Bubble Chart displays data as circles (bubbles) of different sizes.

• The size represents a measure, and position can show two other measures.

• Created by selecting the Circle mark type in Tableau.

• Color can be added to represent categories or additional measures.

• Good for showing relationships between three variables.

• Can be used to identify outliers.

• Requires a scatterplot-like layout for best results.

• Example: Sales vs. Profit with bubble size as quantity sold.

• Can be interactive with filters and tooltips.

• Works well for multi-variable analysis.

• Practical Use: To find high-sales but low-profit products.

5. Bullet Chart

• A Bullet Chart is an enhanced bar chart with reference lines.

• It shows actual performance compared to a target.

• Created by selecting Bullet Graph from the "Show Me" panel.

• Useful for KPI tracking.

• Displays performance bands (poor, average, excellent).

• Can show multiple measures in the same chart.

• Works well in dashboard KPIs.

• Can be displayed horizontally or vertically.

• Example: Actual sales vs. sales target.

• Practical Use: For executive reports showing goal achievement.

6. Area Chart

• An Area Chart is similar to a line chart but with the area under the line filled with color.

• It emphasizes volume changes over time.

• Created by changing the mark type to Area.

• Can be stacked for multiple categories.

• Works well for cumulative data representation.

• Color can differentiate between categories.

• Often used for showing market trends.

• Example: Website traffic over months.

• Can be smoothed for a cleaner look.

• Practical Use: To visualize total growth trends.

7. Waterfall Chart

• A Waterfall Chart shows incremental increases or decreases in a measure.

• Created by selecting Gantt Bar and customizing table calculations.

• Useful for showing how a starting value changes to an ending value.

• Breaks down contributions step by step.

• Positive changes are usually green, negative changes are red.

• Example: Profit changes by month.

• Shows net effect after all changes.

• Can include subtotals.

• Practical Use: To explain profit/loss components.

8. Crosstab

• A Crosstab is a text table in Tableau.

• Displays data in rows and columns with numeric values.

• Created by selecting Text Table in "Show Me".

• Similar to an Excel pivot table.

• Supports sorting, filtering, and conditional formatting.

• Good for showing exact values.

• Example: Sales by region and product.

• Can be exported to Excel.

• Practical Use: To present tabular reports.

9. Histogram

• A Histogram shows frequency distribution of a measure.

• Groups continuous data into bins.

• Created by selecting Histogram in "Show Me".

• Automatically creates bin fields.

• Useful for statistical analysis.

• Example: Distribution of customer ages.

• Can adjust bin size for detail or summary.

• Highlights patterns in data.

• Practical Use: To analyse how data is spread.

:: Best of Luck ::

TableAU Calculations, Sort and Filter with TableAU for MCA 3 or IT Students

 Prepared By : Prof. Uday Shah (HOD - IT)

TableAU Calculations, Sort and Filter with TableAU

1. TableAU Calculations

• Definition: Tableau calculations are expressions written using Tableau’s syntax to manipulate, transform, or derive new values from existing data fields.

• Purpose: They allow you to create new measures or dimensions without changing the underlying source data.

• Types: Calculated Fields, Table Calculations, Level of Detail (LOD) Expressions.

• Flexibility: You can use arithmetic, logical, string, and date functions in calculations.

• Live Update: When the source data changes, calculated fields update automatically.

• Dynamic: They can be interactive with filters and parameters.

• Customization: Tailor data analysis beyond built-in functions.

• Reusability: Once created, they can be reused across multiple worksheets.

• Performance: Simple calculations are fast, but complex nested LODs may slow dashboards.

• Integration: Works with both live and extracted data sources.

• Output: Can return text, number, date, Boolean, or aggregated values.

• Example: Creating a “Profit Margin” field.

🔹 Practical Exercise:

• Formula:

  [Profit Margin] = ([Profit] / [Sales]) * 100
  

• Steps in Tableau:

  1. Open Global Superstore in Tableau.

  2. Go to Analysis → Create Calculated Field.

  3. Name it Profit Margin.

  4. Enter the above formula.

  5. Place it in Columns with Category in Rows.

• Expected Output: Bar chart showing Profit Margin % for each category.

2. TableAU Operators

• Definition: Operators are symbols that perform operations like arithmetic, comparison, and logical evaluations.

• Categories: Arithmetic (+ - * / %), Comparison (= != > < >= <=), Logical (AND OR NOT).

• Usage: Combine operators with fields for custom logic.

• Case Sensitivity: Operators themselves are not case sensitive, but string values are.

• Importance: Essential for IF statements, filtering, and conditional logic.

• Combination: Can be nested inside functions.

• Error Handling: Incorrect type matching (e.g., adding a string to a number) causes errors.

• Performance: Simple operators execute instantly.

• Practicality: Often used for ranking, segmentation, or data cleaning.

• Example: [Profit] > 0 AND [Region] = "Asia" returns only profitable Asian sales.

• Flexibility: You can use them in calculated fields or directly in filter conditions.

• Testing: Test small calculations before applying to large dashboards.

🔹 Practical Exercise:

• Create a field:

  [High Profit Asia] = IF [Profit] > 0 AND [Region] = "Asia" THEN "Yes" ELSE "No" END
  

• Drag it to Filters → Show only "Yes".

• Output: Visualization showing only profitable orders from Asia.

3. TableAU Functions

• Definition: Functions are predefined formulas that perform specific operations.

• Categories: String, Date, Number, Logical, Type Conversion, Aggregate, Window.

• String Example: UPPER([Customer Name]) → Makes names uppercase.

• Date Example: YEAR([Order Date]) → Extracts the year.

• Numeric Example: ROUND([Sales], 2) → Rounds sales to two decimal places.

• Logical Example: IF [Profit] > 0 THEN "Profit" ELSE "Loss" END.

• Aggregation Example: SUM([Sales]).

• Window Example: RUNNING_SUM(SUM([Sales])).

• Purpose: Increase flexibility in data manipulation.

• Usage: Can be nested inside other functions for complex analysis.

• Dynamic: Changes with filters and parameters.

• Testing: Use calculated field editor’s "Test" feature to check.

🔹 Practical Exercise:

• Create a field:

  Year-Sales = STR(YEAR([Order Date])) + " → " + STR(SUM([Sales]))
  

• Output: Display year along with total sales in one label.

4. Numeric Calculations

• Perform math operations like addition, subtraction, multiplication, division.

• Can aggregate (SUM, AVG, MIN, MAX) or transform (ROUND, CEILING, FLOOR).

• Example: Discounted Price = [Sales] - ([Sales] * [Discount]).

🔹 Practical Exercise:

• Calculate Discounted Price for each product.

• Output: Table showing product name and its discounted price.

5. String Calculations

• Manipulate text values (names, product IDs).

• Examples: LEFT, RIGHT, MID, CONCAT, UPPER, LOWER, TRIM.

• Use for data cleaning and formatting.

🔹 Practical Exercise:

• Create Customer Initials = LEFT([Customer Name], 1).

• Output: List of customers with their initials.

6. Date Calculations

• Extract date parts (DAY, MONTH, YEAR).

• Perform differences (DATEDIFF) or add intervals (DATEADD).

• Format dates (DATENAME).

🔹 Practical Exercise:

• Order Year = YEAR([Order Date]).

• Output: Chart showing sales per year.

7. Table Calculations

• Calculations done after aggregation (e.g., running totals, percent of total, rank).

• Dynamic based on table structure.

🔹 Practical Exercise:

• Apply Running Total to Sales.

• Output: Cumulative sales over time.

8. LOD Expressions

• Level of Detail: Control granularity of calculations.

• {FIXED [Region]: SUM([Sales])} ignores other filters.

• Useful for multi-level aggregation.

🔹 Practical Exercise:

• {FIXED [Category]: SUM([Profit])} → Profit per category regardless of sub-category filter.

9. Create Calculated Field

• Go to Analysis → Create Calculated Field.

• Enter name, formula, and validate.

🔹 Practical Exercise:

• Profit Ratio = [Profit] / SUM([Sales]).

• Output: Profit ratio per region.

10. IF Function

• Conditional logic: IF condition THEN result ELSE result END.

🔹 Practical Exercise:

• IF [Profit] >= 0 THEN "Profit" ELSE "Loss" END.

• Output: Color-coded profit/loss map.

11. CASE Function

• Simplifies multiple conditions.

🔹 Practical Exercise:

CASE [Region]
WHEN "Asia" THEN "A"
WHEN "Europe" THEN "E"
ELSE "Other"
END

• Output: Regions replaced with short codes.

📌 Sort and Filter with TableAU

12. Filtering in Visualization

• Filters limit data displayed.

• Can be applied to dimensions, measures, or both.

Practical: Filter orders where Sales > 500.

13. Basic Filters

• Directly drag field to Filters shelf.

Practical: Filter only "Furniture" category.

14. Quick Filter

• User-friendly on-screen filter.

Practical: Show Category quick filter.

15. Context Filter

• Acts as a master filter before others.

Practical: Context filter for "Asia" before other filters.

16. Conditional Filter

• Filters based on a condition.

Practical: Keep rows where Profit > 1000.

17. Slicing Filter

• Narrow down by a measure’s range.

Practical: Slice Sales between 200–1000.

18. Grouping

• Combine similar values.

Practical: Group "Chairs" & "Tables" into "Furniture Items".

19. Hierarchy

• Drill-down from higher to lower levels.

Practical: Region → Country → State hierarchy.

20. Sort Data

• Ascending or descending order by field.

Practical: Sort products by highest profit.

:: Best of Luck :: 

Working with Data Sources in Tableau for MCA Semester 3 or IT Students

 Prepared By : Prof. Uday Shah (HOD-IT)

Working with Data Sources in Tableau

1. Tableau – Connect to a Data Source

• Tableau allows you to connect to multiple data sources like Excel, CSV, SQL Server, Google Sheets, etc.

• From the Start Page, click on Connect in the left sidebar.

• Select the type of data you want to connect (e.g., Excel, Text, Server).

• Once selected, browse your file location or enter server credentials.

• Tableau loads the data into the Data Source Tab.

• You can preview data in a grid-like format before importing it.

• You can rename fields directly here for clarity.

• Multiple tables from the same file can be loaded.

• Drag tables into the canvas to define relationships.

• Data can be Live Connected or Extracted for performance improvement.

• Live Connection keeps data updated from source.

• Extract stores a snapshot for faster processing.

2. Connect with Text File

• Used to connect .txt or .csv files.

• In Tableau, click Connect → Text File.

• Select your CSV or TXT file.

• Tableau automatically detects delimiters (comma, tab, pipe).

• You can change delimiter type in connection settings.

• Supports UTF-8 encoding.

• Header rows can be detected automatically.

• Data preview is shown in the Data Source window.

• Column names and data types are inferred but editable.

• Very useful for importing large exported reports.

• Can be combined with joins if other sources are connected.

• Data Extracts can speed up processing for large text files.

3. Connect with Excel

• Most common data connection type in Tableau.

• Supports .xls and .xlsx formats.

• Go to Connect → Microsoft Excel.

• Select the file from your computer.

• All sheets are displayed on the left.

• Drag sheets into the canvas to load them.

• Multiple sheets can be joined/related.

• Data can be pivoted within Tableau.

• Field names and data types are inferred automatically.

• Can connect with named ranges in Excel.

• Large Excel files might need extracts for speed.

• You can refresh Excel data without re-importing.

4. Tableau Extracting Data

• Extracting creates a static snapshot of your data.

• Improves performance with large datasets.

• File is stored as .hyper format in Tableau.

• Supports filters to limit extracted data.

• Extracts can be refreshed manually or scheduled (Tableau Server).

• Allows offline analysis without original data connection.

• Reduces load time for dashboards.

• Enables better aggregation performance.

• Extracted data can still be filtered in Tableau.

• Can be optimized for faster query execution.

• Extracts can be published and shared.

5. Data Connecting in Tableau

• Tableau supports Live and Extract connections.

• Live connection always fetches the latest data from the source.

• Extract connection stores a cached version.

• You can connect to multiple data sources simultaneously.

• Tableau allows blending/joining between different sources.

• Connection type affects performance.

• You can switch between Live and Extract anytime.

• Live is better for real-time dashboards.

• Extract is better for speed and offline use.

• Secure connections can be set up for databases.

• Data refresh schedules are available in Tableau Server/Online.

6. Tableau Editing Metadata

• Metadata includes field names, data types, and descriptions.

• You can rename fields for better readability.

• Change data types (String, Date, Number).

• Hide unnecessary fields.

• Create calculated fields.

• Change default aggregation (SUM, AVG, COUNT).

• Set geographic roles for location-based fields.

• Add comments for future reference.

• Group fields into folders.

• Metadata changes don’t affect the original source.

• Can be reset to original source state.

7. Tableau Data Joining

• Joins combine tables within the same data source.

• Supports Inner, Left, Right, Full Outer joins.

• Join keys define how tables match.

• Can join multiple tables together.

• Joins happen at row level.

• Useful when tables share common fields.

• Join performance depends on data size.

• Data preview shows join results instantly.

• Multiple join clauses are possible.

• Avoid unnecessary joins to improve speed.

8. Tableau Data Blending

• Combines data from different data sources.

• Requires a primary and secondary data source.

• Linking is done on common fields.

• Primary source fields have blue check marks.

• Secondary source fields have orange check marks.

• Useful for combining different database types.

• Works at aggregated level (not row level like joins).

• Blending is slower than joining in same source.

• Blended fields can be used in same visualization.

• Limited by aggregation differences between sources.

9. Tableau Replacing Data Source

• Allows switching one data source with another.

• Used when data source location changes.

• Keeps existing visualizations intact.

• Both data sources must have matching field names/types.

• Avoids rebuilding dashboards from scratch.

• Useful for moving from development to production.

• Can replace Excel with SQL for performance.

• Tableau automatically maps matching fields.

• Non-matching fields need manual remapping.

• Old data source is removed after replacement.

:: Best of Luck ::

TableAU Basics for MCA Semester 3 or IT Students

Prepared By : Prof. Uday Shah (HOD-IT)

TableAU Basics

1. Download and Installation of Tableau

• Tableau offers Tableau Desktop, Tableau Public, and Tableau Prep for installation.

• Go to the official Tableau website (tableau.com) to download the installer.

• Tableau Public is free, while Tableau Desktop requires a license or a 14-day trial.

• Choose the correct operating system version (Windows or macOS).

• After downloading, run the installer file and follow the installation wizard.

• You may need administrator privileges to install Tableau.

• During installation, you can choose default file save locations.

• After installation, activate using a product key or login for Tableau Public.

• Tableau regularly provides updates for new features and bug fixes.

• Internet connection is required for license activation and Tableau Public usage.

• Installation process usually takes less than 5 minutes.

• Once installed, you can start connecting to data sources immediately.

2. Tableau Navigation

• Tableau interface consists of menu bar, toolbar, data pane, and workspace.

• Menu bar contains options like File, Data, Worksheet, Dashboard, Help.

• Toolbar provides quick access to frequently used commands.

• Data pane on the left displays connected data fields.

• Dimensions (blue fields) are categorical data, Measures (green fields) are numerical.

• Shelves (rows, columns, filters, marks) are used to build views.

• Marks card allows customization of color, size, label, and tooltips.

• Tabs at the bottom switch between worksheets, dashboards, and stories.

• Status bar shows summary info like number of marks, filter status, etc.

• You can rearrange panes and shelves for a custom workspace layout.

• Navigation is drag-and-drop friendly for creating visuals.

• Keyboard shortcuts make navigation faster (e.g., Ctrl+Z for undo).

3. Tableau Data Terminology

• Dimension: Qualitative fields like Category, Region, Product Name.

• Measure: Quantitative fields like Sales, Profit, Quantity.

• Discrete Data: Fixed values (blue pills) used for categories.

• Continuous Data: Ranges of values (green pills) used for measures.

• Table: A connected data sheet or database table.

• Field: A column in the dataset.

• Record: A row in the dataset.

• Calculated Field: Custom fields created with formulas.

• Aggregation: Summary functions like SUM, AVG, COUNT.

• Granularity: Level of detail in the dataset.

• Null Values: Missing data entries.

• Hierarchy: Group of related fields arranged for drill-down.

4. Tableau Design Flow

• Step 1: Connect to the data source (Excel, SQL, cloud, etc.).

• Step 2: Select relevant tables or sheets from the data.

• Step 3: Perform any necessary data preparation (joins, unions, filters).

• Step 4: Identify dimensions and measures to use.

• Step 5: Decide the type of visualization based on the question.

• Step 6: Drag dimensions and measures to shelves to create the chart.

• Step 7: Apply filters, sorting, and grouping to refine results.

• Step 8: Use color, labels, and size for better readability.

• Step 9: Combine multiple charts into a dashboard.

• Step 10: Add interactive filters or actions.

• Step 11: Format visuals for presentation.

• Step 12: Share or publish the final dashboard.

5. Tableau File Types

• .twb (Tableau Workbook): Contains instructions to build visualizations; does not store data.

• .twbx (Tableau Packaged Workbook): Includes workbook + data + images in one file.

• .tds (Tableau Data Source): Stores connection info and field definitions.

• .tdsx (Packaged Data Source): Data source + data included.

• .tde (Tableau Data Extract): Old extract format for storing data snapshots.

• .hyper: New high-performance extract format replacing .tde.

• .tms (Tableau Map Source): Stores map layer customizations.

• .tbm (Tableau Bookmark): Saves a single worksheet for reuse.

• .tps (Tableau Preferences): Stores custom color palettes.

• .tfl (Tableau Flow): Stores Tableau Prep workflow.

• .tflx (Packaged Flow): Tableau Prep flow + data included.

• .pdf, .png, .csv: Export formats for sharing static results.

6. Tableau Data Types

• String: Text values (e.g., "Product Name").

• Number (Whole): Integer values without decimals.

• Number (Decimal): Numeric values with decimals.

• Date: Calendar date values.

• Date & Time: Date values with time included.

• Boolean: True/False values.

• Geographical: Data with geographic meaning (Country, City, Postal Code).

• Cluster Group: Grouped fields used in analysis.

• Calculated: Custom generated field from existing fields.

• Currency: Number formatted as money.

• Percent: Number formatted as percentage.

• Duration: Time intervals.

7. Changing Data Type in Tableau

• Right-click the field in the Data Pane.

• Select Change Data Type.

• Choose from options: String, Number, Date, Boolean, Geographic Role.

• You can also change data type directly from data grid view.

• Changing data type affects how Tableau interprets and visualizes data.

• Incorrect data types can lead to wrong analysis results.

• Date formats can be switched between discrete (blue) and continuous (green).

• Number formats can be set as currency, percent, or decimal.

• Geographic roles allow map-based visualizations.

• Data type changes apply only to the current workbook.

• You can also use a Calculated Field to convert data type.

• Always check your visual after changing data type.

8. Show Me in Tableau

• Show Me is a panel in Tableau that suggests visualization types.

• Located in the top-right corner of the workspace.

• Displays chart types like bar, line, map, pie, scatter, tree map, etc.

• Suggests charts based on the fields selected.

• Greyed-out charts indicate insufficient data selection.

• Clicking on a chart type automatically rearranges fields to fit that chart.

• Helps beginners choose the right visualization quickly.

• Can be used as a learning guide for chart requirements.

• Works with both dimensions and measures.

• Supports more than 20 chart types.

• You can still customize the chart after using Show Me.

• Saves time in chart creation and experimentation.

:: Best of Luck :: 

Introduction to TableAU for MCA Semester 3 or IT Students

 Prepared By : Prof. Uday Shah (HOD-IT)

Introduction to TableAU

1. Introduction to Data Visualization

• Data Visualization is the process of representing data in graphical or pictorial formats.

• It transforms raw numbers and text into visuals like charts, graphs, and maps.

• The main aim is to make complex data easy to understand.

• Helps to identify patterns, trends, and relationships in the data quickly.

• Improves the ability to communicate data insights to both technical and non-technical audiences.

• Makes decision-making faster and more accurate by providing a visual context.

• Can be used in real-time monitoring of performance metrics.

• Supports exploratory data analysis by allowing interactive filtering and drill-down.

• Used widely in business, science, education, health, and government sectors.

• Popular visualization formats include bar charts, line graphs, pie charts, scatter plots, heat maps, and dashboards.

• Data visualization is an important step in Business Intelligence (BI) processes.

• It bridges the gap between data collection and decision-making.

2. Data Visualization Tools

• Software or platforms that help in creating data visuals easily.

• They allow data connection from different sources like Excel, SQL, Cloud services.

• Provide a drag-and-drop interface for building charts without coding.

• Offer a variety of chart types such as bars, lines, maps, treemaps, funnels, etc.

• Some tools support real-time dashboards for continuous data monitoring.

• Many tools have interactive features like filters, drill-down, and actions.

• Some tools focus on cloud-based sharing, while others offer on-premises deployment.

• Popular tools include Tableau, Power BI, Qlik Sense, Looker Studio, Excel, SAP Lumira.

• Tools differ in terms of cost, features, ease of use, and integration options.

• Some are free (Tableau Public, Google Data Studio) and others are paid (Power BI Pro, Tableau Desktop).

• The choice of tool depends on project needs, budget, and data size.

• Many tools allow exporting dashboards as PDF, images, or interactive files.

3. Difference between Power BI and Tableau

• Developer: Power BI is by Microsoft, Tableau by Tableau Software (Salesforce).

• Ease of Use: Power BI is generally easier for beginners, Tableau is better for advanced visualizations.

• Best For: Power BI suits quick business dashboards; Tableau suits in-depth analysis.

• Data Handling: Tableau handles very large datasets more efficiently than Power BI.

• Integration: Power BI works best with Microsoft tools; Tableau integrates widely with various platforms.

• Cost: Power BI is cheaper; Tableau is generally more expensive.

• Speed: Tableau’s Hyper engine is optimized for speed; Power BI can slow down with very large data.

• Visuals: Tableau offers more customization and variety in visualizations.

• Deployment: Power BI is cloud-first; Tableau offers both cloud and on-premises options.

• Community Support: Both have large communities; Tableau’s is more visualization-focused.

• Advanced Analytics: Tableau supports direct integration with R and Python more flexibly.

• Learning Curve: Power BI is simpler to start; Tableau requires a bit more learning for advanced use.

4. Introduction to Tableau

• Tableau is a Business Intelligence and Data Visualization tool.

• Allows users to create interactive dashboards and reports.

• Connects to multiple data sources like Excel, SQL, and cloud platforms.

• Works with both live data and in-memory extracts.

• Provides a drag-and-drop interface for easy visualization building.

• Enables users to explore data by filtering, sorting, and drilling down.

• Supports advanced analytics using calculated fields and integrations with R/Python.

• Used by industries like finance, healthcare, retail, and education.

• Can be used by non-technical users as well as analysts.

• Offers both free (Tableau Public) and paid (Tableau Desktop, Server, Online) versions.

• Dashboards created in Tableau can be shared across teams.

• Tableau focuses heavily on visual storytelling.

5. History of Tableau

• Founded in 2003 in Mountain View, California.

• Developed by Christian Chabot, Pat Hanrahan, and Chris Stolte. (ક્રિશ્ચિયન ચાબોટ , પૅટ હૅનરહૅન ,ક્રિસ સ્ટોલ્ટે)

• Originated as a project from Stanford University.

• Initially aimed to make databases and spreadsheets easier to understand.

• First version, Tableau Desktop, was released in 2004.

• Expanded product line with Tableau Server and Tableau Public.

• Gained global popularity for ease of use and visualization power.

• Regularly introduced new features in annual updates.

• Released Tableau Prep for data cleaning and preparation.

• Acquired by Salesforce in 2019 for $15.7 billion.

• Became one of the most widely used BI tools worldwide.

• Continues to evolve with AI-driven analytics and automation features.

6. Advantages of Tableau

• User-friendly drag-and-drop interface.

• Handles large datasets efficiently.

• Offers a wide range of visualizations.

• Connects to various data sources easily.

• Supports real-time data analysis.

• Allows creation of interactive dashboards.

• Integration with R, Python, and other tools.

• Offers mobile compatibility for dashboards.

• Strong community support and resources.

• Provides data blending and joining features.

• Supports both cloud and on-premises deployment.

• Enables quick sharing of visualizations.

7. Tableau Architecture

• Data Layer: Connects to various data sources (files, databases, cloud).

• Data Engine (Hyper): In-memory processing for speed.

• VizQL Server: Converts actions into visual queries and renders results.

• Application Server: Handles authentication and permissions.

• Gateway/Load Balancer: Manages requests from users.

• Repository: Stores metadata, permissions, and extracts.

• Cache Server: Improves performance by storing frequently used data.

• Backgrounder: Manages scheduled tasks like data refreshes.

• Client Layer: Access via Tableau Desktop, Server, Online, or Mobile.

• Admin Layer: Allows server configuration and monitoring.

• Security Layer: Protects data through user permissions.

• Visualization Layer: Presents interactive charts and dashboards.

8. Tableau Products

• Tableau Desktop: Main tool for creating reports and dashboards.

• Tableau Public: Free version for public sharing of dashboards.

• Tableau Server: On-premises sharing and collaboration tool.

• Tableau Online: Cloud-based version of Tableau Server.

• Tableau Reader: Free tool to view Tableau files locally.

• Tableau Prep Builder: For data cleaning and preparation.

• Tableau Mobile: Mobile app to access dashboards on the go.

• Tableau Bridge: Connects on-premises data to Tableau Online.

• Tableau VizQL Server: Converts user actions into visuals.

• Tableau Data Management Add-on: For advanced governance and cataloging.

• Tableau CRM: AI-powered analytics within Salesforce.

• Tableau Embedded Analytics: Embed dashboards into other applications.

:: Best Of Luck ::

Working with XML, Application Configuration and Web services for BCA Semester 5 or IT Students

 Prepared By :Prof. Uday Shah (HOD-IT)

Working with XML, Application Configuration and Web services

1) Concept of XML

• XML (Extensible Markup Language) is used for storing and transporting data in a structured way.

• It uses custom-defined tags to describe the data and its meaning.

• XML is platform-independent and language-neutral, making it ideal for data interchange.

• Data in XML is hierarchical, making it easy to represent complex relationships.

• It separates data from presentation, allowing different systems to share information.

• XML documents must be well-formed (properly nested and closed tags).

• It supports attributes and elements to describe data.

• XML is both human-readable and machine-readable.

• It is widely used in web services, configuration files, and data storage.

• XML can be validated using DTD (Document Type Definition) or XSD (XML Schema).

2) Features of XML

• XML is extensible, allowing developers to define custom tags.

• It supports Unicode, enabling data exchange in multiple languages.

• XML is self-descriptive; each data element is surrounded by tags.

• It is hierarchical, meaning data can be nested within elements.

• XML is text-based and platform-independent.

• It supports validation for ensuring data structure integrity.

• Data is transportable across different systems and platforms.

• XML can integrate easily with multiple technologies like .NET, Java, and databases.

• It is suitable for both online and offline data storage.

• XML is widely supported by APIs and parsers for manipulation and querying.

3) XML Parser

• An XML parser reads and processes XML documents into a usable format.

• Parsers check if XML documents are well-formed and valid.

• Two main types: DOM (Document Object Model) and SAX (Simple API for XML) parsers.

• DOM loads the entire XML document into memory, enabling random access.

• SAX parses XML sequentially and is memory efficient for large documents.

• Parsers can enforce schema or DTD validation to ensure structure correctness.

• .NET provides XmlReader, XmlDocument, and XPathDocument for XML parsing.

• Parsers allow reading attributes, elements, and text nodes programmatically.

• XML parsers are used heavily in web services and data integration.

• They simplify data extraction, transformation, and manipulation processes.

4) Reading Datasets from XML

• Datasets in ADO.NET can read structured data directly from XML.

• The ReadXml() method loads XML data into a DataSet.

• XML can include both schema and data, enabling strongly-typed structures.

• It is useful for importing data from external applications and services.

• DataTables within the DataSet are populated based on XML nodes.

• Relationships between tables can be preserved if defined in the XML.

• Reading from XML does not require an active database connection.

• Helps in working with offline datasets.

• Supports reading data incrementally or all at once.

• Can integrate XML data with existing database tables using merging.

5) Writing Datasets to XML

• Datasets can export data back into XML format for sharing or storage.

• The WriteXml() method writes the DataSet’s contents to XML.

• Optionally, the schema can be included using WriteXmlSchema().

• XML output is platform-independent and can be consumed by other systems.

• Useful for creating backup or cache files in applications.

• Relationships, constraints, and metadata can be preserved in the XML.

• Developers can choose to write only data or both data and schema.

• The resulting XML can be transformed using XSLT for presentation.

• Writing to XML allows easy synchronization with web services.

• Provides a consistent way to share data across different platforms.

6) Introduction to Web.Config

• Web.config is the configuration file for ASP.NET applications.

• It is an XML file that stores settings like connection strings and authentication details.

• Web.config resides in the root directory of the application.

• Changes to Web.config do not require recompiling the application.

• It supports hierarchical configuration (child folders can have their own Web.config).

• Sensitive information in Web.config can be encrypted for security.

• Multiple sections handle different aspects: system.web, appSettings, connectionStrings, etc.

• Developers can define custom sections for application-specific settings.

• IIS reloads the application automatically when Web.config is modified.

• Web.config ensures centralized management of application settings.

7) Common Configuration Sections in Web.Config

a) AppSettings

• Stores key-value pairs for application-wide settings.

• Values can be retrieved using ConfigurationManager.AppSettings.

• Useful for configuration values like API keys or environment names.

• Changes reflect instantly without redeployment.

• Supports custom keys for easy configuration.

• Best suited for lightweight settings.

• Avoid storing sensitive data unless encrypted.

• Easy to extend and manage for small configurations.

• Can be overridden in machine.config or child Web.config.

• Improves flexibility and maintainability.

b) Tracing

• Tracing enables monitoring of application execution for debugging.

• It can be turned on in Web.config with <trace enabled="true" />.

• Displays diagnostic information at the bottom of the page or in a trace viewer.

• Helps track control tree, session state, and execution time.

• Developers can insert trace statements in code using Trace.Write().

• Useful during development and troubleshooting.

• Can log custom messages to assist debugging.

• Should be disabled in production for security.

• Supports page-level and application-level tracing.

• Centralized configuration makes enabling/disabling easy.

c) Custom Errors

• Configures error-handling behavior for the application.

• Can display friendly error pages instead of raw error messages.

• <customErrors mode="On" defaultRedirect="ErrorPage.aspx" /> enables this feature.

• Supports mapping specific HTTP status codes to custom pages.

• Prevents exposing sensitive error details to end users.

• Helps maintain a professional look during errors.

• Developers can log the errors in the background.

• Works with global error handlers for comprehensive coverage.

• Easily configurable per application or folder.

• Essential for security and user experience.

d) Authentication and Authorization

• Authentication verifies user identity, Authorization checks permissions.

• Web.config supports multiple authentication modes: Windows, Forms, Passport.

• <authentication mode="Forms" /> enables forms-based login.

• <authorization> section controls access to directories and pages.

• Allows role-based and user-based access restrictions.

• Integration with ASP.NET Membership for user management.

• Centralizes security policy for the application.

• Can deny or allow anonymous access.

• Works in conjunction with session and cookies.

• Helps protect sensitive parts of the application.

8) Web Services

a) Basics of Web Services

• Web services enable communication between applications over the web.

• They are based on open standards like HTTP, XML, SOAP, and WSDL.

• Platform-independent and language-neutral.

• Useful for exposing functionality to external clients or systems.

• Support remote procedure calls via standard protocols.

• ASP.NET web services are implemented using .asmx files.

• Provide loosely coupled integration across applications.

• Enable data exchange between heterogeneous systems.

• Can be consumed by browsers, mobile apps, and other services.

• Web services support both synchronous and asynchronous operations.

b) Creating Web Service

• Create an .asmx file in ASP.NET to define the service.

• Decorate classes with [WebService] attribute and methods with [WebMethod].

• Methods in the service can be accessed remotely by clients.

• Configure namespaces and descriptions for the service.

• Services can use ADO.NET for database operations.

• Deploy the service on a web server (IIS).

• Ensure that the service is properly secured with authentication if needed.

• Test service endpoints using a browser or tools like Postman.

• Web services can return data in XML or JSON formats.

• Support for WSDL makes integration with other platforms easier.

c) Consume and Deployment of a Web Service

• Clients consume web services using service references or WSDL.

• In Visual Studio, add a web reference to the service URL.

• Proxy classes are generated automatically for communication.

• Methods of the service can then be called like local methods.

• Deployment involves placing the .asmx file and assemblies on a web server.

• Ensure proper configuration in IIS for the application.

• Services can be updated without changing the client code if contracts remain the same.

• Handle network errors and timeouts gracefully in client code.

• Secure services with SSL and token-based authentication.

• Test deployment thoroughly to ensure compatibility across platforms.

:: Best of Luck::