Kotlin Tutorial for Beginners - Kotlin Data Types

The most fundamental data type in Kotlin is Primitive data type and all others are reference types like array and string. Java needs to use wrappers (java.lang.Integer) for primitive data types to behave like objects but Kotlin already has all data types as objects.

There are different data types in Kotlin

1. Integer Data type
2. Floating-point Data Type
3. Boolean Data Type
4. Character Data Type

Integer Data Type:

These data types contain the contain integer values.

Let’s write a program to represent all the integer data types and their min and max value.

// Kotlin code 
fun main(args : Array<String>) { 
	var myint = 35
	//add suffix L for long integer 
	var mylong = 23L 
	
	println("My integer ${myint}") 
	println("My long integer ${mylong}") 

	var b1: Byte = Byte.MIN_VALUE 
	var b2: Byte = Byte.MAX_VALUE 
	println("Smallest byte value: " +b1) 
	println("Largest byte value: " +b2) 

	var S1: Short = Short.MIN_VALUE 
	var S2: Short = Short.MAX_VALUE 
	println("Smallest short value: " +S1) 
	println("Largest short value: " +S2) 

	var I1: Int = Int.MIN_VALUE 
	var I2: Int = Int.MAX_VALUE 
	println("Smallest integer value: " +I1) 
	println("Largest integer value: " +I2) 

	var L1: Long = Long.MIN_VALUE 
	var L2: Long = Long.MAX_VALUE 
	println("Smallest long integer value: " +L1) 
	println("Largest long integer value: " +L2) 
} 

Output:

My integer 35
My long integer 23
Smallest byte value: -128
Largest byte value: 127
Smallest short value: -32768
Largest short value: 32767
Smallest integer value: -2147483648
Largest integer value: 2147483647
Smallest long integer value: -9223372036854775808
Largest long integer value: 9223372036854775807

Floating-Point Data Type:

These data type used to store decimal value or fractional part.

Let’s write a program to represent both the floating-point data type and their min and max value.

// Kotlin code 
fun main(args : Array<String>) { 
	var myfloat = 54F				 // add suffix F for float 
	println("My float value ${myfloat}") 

	var F1: Float = Float.MIN_VALUE 
	var F2: Float = Float.MAX_VALUE 
	println("Smallest Float value: " +F1) 
	println("Largest Float value: " + F2) 

	var D1: Double = Double.MIN_VALUE 
	var D2: Double = Double.MAX_VALUE 
	println("Smallest Double value: " + D1) 
	println("Largest Double value: " + D2) 
} 

Output:

My float value 54.0
Smallest Float value: 1.4E-45
Largest Float value: 3.4028235E38
Smallest Double value: 4.9E-324
Largest Double value: 1.7976931348623157E308

Boolean Data Type:

Boolean data type represents only one bit of information either true or false. The Boolean type in Kotlin is the same as in Java. These operations disjunction (||) or conjunction (&&) can be performed on boolean types.

Let’s write a program to represent the boolean data types.

// Kotlin code 
fun main(args : Array<String>){ 
if (true is Boolean){ 
		print("Yes,true is a boolean value") 
	} 
} 

Output:

Yes, true is a boolean value

Character Data Type:

Character data type represents the small letters(a-z), Capital letters(A-Z), digits(0-9) and other symbols.

Let’s write a program to represent character data type.

// Kotlin code 
fun main(args : Array<String>){ 
	var alphabet: Char = 'C'
	println("C is a character : ${alphabet is Char}") 
} 

Output:

C is a character : true

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Learn Data Science Tutorial - Full Course for Beginners. DO NOT MISS!!!

Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You’ll learn the important elements of data science. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.
⭐️ Course Contents ⭐️
⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science

• Welcome (1.1)
• Demand for Data Science (2.1)
• The Data Science Venn Diagram (2.2)
• The Data Science Pathway (2.3)
• Roles in Data Science (2.4)
• Teams in Data Science (2.5)
• Big Data (3.1)
• Coding (3.2)
• Statistics (3.3)
• Business Intelligence (3.4)
• Do No Harm (4.1)
• Methods Overview (5.1)
• Sourcing Overview (5.2)
• Coding Overview (5.3)
• Math Overview (5.4)
• Statistics Overview (5.5)
• Machine Learning Overview (5.6)
• Interpretability (6.1)
• Actionable Insights (6.2)
• Presentation Graphics (6.3)
• Reproducible Research (6.4)
• Next Steps (7.1)

⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)

• Welcome (1.1)
• Metrics (2.1)
• Accuracy (2.2)
• Social Context of Measurement (2.3)
• Existing Data (3.1)
• APIs (3.2)
• Scraping (3.3)
• New Data (4.1)
• Interviews (4.2)
• Surveys (4.3)
• Card Sorting (4.4)
• Lab Experiments (4.5)
• A/B Testing (4.6)
• Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)

• Welcome (1.1)
• Spreadsheets (2.1)
• Tableau Public (2.2)
• SPSS (2.3)
• JASP (2.4)
• Other Software (2.5)
• HTML (3.1)
• XML (3.2)
• JSON (3.3)
• R (4.1)
• Python (4.2)
• SQL (4.3)
• C, C++, & Java (4.4)
• Bash (4.5)
• Regex (5.1)
• Next Steps (6.1)

⌨️ Part 4: Mathematics (4:01:09)

• Welcome (1.1)
• Elementary Algebra (2.1)
• Linear Algebra (2.2)
• Systems of Linear Equations (2.3)
• Calculus (2.4)
• Calculus & Optimization (2.5)
• Big O (3.1)
• Probability (3.2)

⌨️ Part 5: Statistics (4:44:03)

• Welcome (1.1)
• Exploration Overview (2.1)
• Exploratory Graphics (2.2)
• Exploratory Statistics (2.3)
• Descriptive Statistics (2.4)
• Inferential Statistics (3.1)
• Hypothesis Testing (3.2)
• Estimation (3.3)
• Estimators (4.1)
• Measures of Fit (4.2)
• Feature Selection (4.3)
• Problems in Modeling (4.4)
• Model Validation (4.5)
• DIY (4.6)
• Next Step (5.1)

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=ua-CiDNNj30&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=7
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object

a**=25+**85j

type**(a)**

output**:<class’complex’>**

b**={1:10,2:“Pinky”****}**

id**(b)**

output**:**238989244168

Built-in data types in Python

a**=str(“Hello python world”)****#str**

b**=int(18)****#int**

c**=float(20482.5)****#float**

d**=complex(5+85j)****#complex**

e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**

f**=tuple((“python”,“easy”,“learning”))****#tuple**

g**=range(10)****#range**

h**=dict(name=“Vidu”,age=36)****#dict**

i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**

j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**

k**=bool(18)****#bool**

l**=bytes(8)****#bytes**

m**=bytearray(8)****#bytearray**

n**=memoryview(bytes(18))****#memoryview**

Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger

age**=**18

print**(age)**

Output**:**18

Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).

String

The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.

“Hello”+“python”

output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

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