Dylan  Iqbal

Dylan Iqbal

1597292220

Building Your Toolbox [4 of 51]

Every craftsperson needs a toolbox, and this is no different for a developer. You’ll need to have both Node.js installed to run your code, and an editor, such as Visual Studio Code, to create and edit it. We’re going to talk through what you’ll need and where to find it.

#javascript #programming #developer #web-development

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Building Your Toolbox [4 of 51]

The Best Way to Build a Chatbot in 2021

A useful tool several businesses implement for answering questions that potential customers may have is a chatbot. Many programming languages give web designers several ways on how to make a chatbot for their websites. They are capable of answering basic questions for visitors and offer innovation for businesses.

With the help of programming languages, it is possible to create a chatbot from the ground up to satisfy someone’s needs.

Plan Out the Chatbot’s Purpose

Before building a chatbot, it is ideal for web designers to determine how it will function on a website. Several chatbot duties center around fulfilling customers’ needs and questions or compiling and optimizing data via transactions.

Some benefits of implementing chatbots include:

  • Generating leads for marketing products and services
  • Improve work capacity when employees cannot answer questions or during non-business hours
  • Reducing errors while providing accurate information to customers or visitors
  • Meeting customer demands through instant communication
  • Alerting customers about their online transactions

Some programmers may choose to design a chatbox to function through predefined answers based on the questions customers may input or function by adapting and learning via human input.

#chatbots #latest news #the best way to build a chatbot in 2021 #build #build a chatbot #best way to build a chatbot

Riyad Amin

Riyad Amin

1571046022

Build Your Own Cryptocurrency Blockchain in Python

Cryptocurrency is a decentralized digital currency that uses encryption techniques to regulate the generation of currency units and to verify the transfer of funds. Anonymity, decentralization, and security are among its main features. Cryptocurrency is not regulated or tracked by any centralized authority, government, or bank.

Blockchain, a decentralized peer-to-peer (P2P) network, which is comprised of data blocks, is an integral part of cryptocurrency. These blocks chronologically store information about transactions and adhere to a protocol for inter-node communication and validating new blocks. The data recorded in blocks cannot be altered without the alteration of all subsequent blocks.

In this article, we are going to explain how you can create a simple blockchain using the Python programming language.

Here is the basic blueprint of the Python class we’ll use for creating the blockchain:

class Block(object):
    def __init__():
        pass
    #initial structure of the block class 
    def compute_hash():
        pass
    #producing the cryptographic hash of each block 
  class BlockChain(object):
    def __init__(self):
    #building the chain
    def build_genesis(self):
        pass
    #creating the initial block
    def build_block(self, proof_number, previous_hash):
        pass
    #builds new block and adds to the chain
   @staticmethod
    def confirm_validity(block, previous_block):
        pass
    #checks whether the blockchain is valid
    def get_data(self, sender, receiver, amount):
        pass
    # declares data of transactions
    @staticmethod
    def proof_of_work(last_proof):
        pass
    #adds to the security of the blockchain
    @property
    def latest_block(self):
        pass
    #returns the last block in the chain

Now, let’s explain how the blockchain class works.

Initial Structure of the Block Class

Here is the code for our initial block class:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()

As you can see above, the class constructor or initiation method ( init()) above takes the following parameters:

self — just like any other Python class, this parameter is used to refer to the class itself. Any variable associated with the class can be accessed using it.

index — it’s used to track the position of a block within the blockchain.

previous_hash — it used to reference the hash of the previous block within the blockchain.

data—it gives details of the transactions done, for example, the amount bought.

timestamp—it inserts a timestamp for all the transactions performed.

The second method in the class, compute_hash , is used to produce the cryptographic hash of each block based on the above values.

As you can see, we imported the SHA-256 algorithm into the cryptocurrency blockchain project to help in getting the hashes of the blocks.

Once the values have been placed inside the hashing module, the algorithm will return a 256-bit string denoting the contents of the block.

So, this is what gives the blockchain immutability. Since each block will be represented by a hash, which will be computed from the hash of the previous block, corrupting any block in the chain will make the other blocks have invalid hashes, resulting in breakage of the whole blockchain network.

Building the Chain

The whole concept of a blockchain is based on the fact that the blocks are “chained” to each other. Now, we’ll create a blockchain class that will play the critical role of managing the entire chain.

It will keep the transactions data and include other helper methods for completing various roles, such as adding new blocks.

Let’s talk about the helper methods.

Adding the Constructor Method

Here is the code:

class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()

The init() constructor method is what instantiates the blockchain.

Here are the roles of its attributes:

self.chain — this variable stores all the blocks.

self.current_data — this variable stores information about the transactions in the block.

self.build_genesis() — this method is used to create the initial block in the chain.

Building the Genesis Block

The build_genesis() method is used for creating the initial block in the chain, that is, a block without any predecessors. The genesis block is what represents the beginning of the blockchain.

To create it, we’ll call the build_block() method and give it some default values. The parameters proof_number and previous_hash are both given a value of zero, though you can give them any value you desire.

Here is the code:

def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
 def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block

Confirming Validity of the Blockchain

The confirm_validity method is critical in examining the integrity of the blockchain and making sure inconsistencies are lacking.

As explained earlier, hashes are pivotal for realizing the security of the cryptocurrency blockchain, because any slight alteration in an object will result in the creation of an entirely different hash.

Thus, the confirm_validity method utilizes a series of if statements to assess whether the hash of each block has been compromised.

Furthermore, it also compares the hash values of every two successive blocks to identify any anomalies. If the chain is working properly, it returns true; otherwise, it returns false.

Here is the code:

def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True

Declaring Data of Transactions

The get_data method is important in declaring the data of transactions on a block. This method takes three parameters (sender’s information, receiver’s information, and amount) and adds the transaction data to the self.current_data list.

Here is the code:

def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True

Effecting the Proof of Work

In blockchain technology, Proof of Work (PoW) refers to the complexity involved in mining or generating new blocks on the blockchain.

For example, the PoW can be implemented by identifying a number that solves a problem whenever a user completes some computing work. Anyone on the blockchain network should find the number complex to identify but easy to verify — this is the main concept of PoW.

This way, it discourages spamming and compromising the integrity of the network.

In this article, we’ll illustrate how to include a Proof of Work algorithm in a blockchain cryptocurrency project.

Finalizing With the Last Block

Finally, the latest_block() helper method is used for retrieving the last block on the network, which is actually the current block.

Here is the code:

def latest_block(self):
        return self.chain[-1]

Implementing Blockchain Mining

Now, this is the most exciting section!

Initially, the transactions are kept in a list of unverified transactions. Mining refers to the process of placing the unverified transactions in a block and solving the PoW problem. It can be referred to as the computing work involved in verifying the transactions.

If everything has been figured out correctly, a block is created or mined and joined together with the others in the blockchain. If users have successfully mined a block, they are often rewarded for using their computing resources to solve the PoW problem.

Here is the mining method in this simple cryptocurrency blockchain project:

def block_mining(self, details_miner):
            self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awarded with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)

Summary

Here is the whole code for our crypto blockchain class in Python:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()
    def __repr__(self):
        return "{} - {} - {} - {} - {}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()
    def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
    def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block
    @staticmethod
    def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True
    def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True        
    @staticmethod
    def proof_of_work(last_proof):
        pass
    @property
    def latest_block(self):
        return self.chain[-1]
    def chain_validity(self):
        pass        
    def block_mining(self, details_miner):       
        self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awared with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)  
    def create_node(self, address):
        self.nodes.add(address)
        return True
    @staticmethod
    def get_block_object(block_data):        
        return Block(
            block_data['index'],
            block_data['proof_number'],
            block_data['previous_hash'],
            block_data['data'],
            timestamp=block_data['timestamp']
        )
blockchain = BlockChain()
print("GET READY MINING ABOUT TO START")
print(blockchain.chain)
last_block = blockchain.latest_block
last_proof_number = last_block.proof_number
proof_number = blockchain.proof_of_work(last_proof_number)
blockchain.get_data(
    sender="0", #this means that this node has constructed another block
    receiver="LiveEdu.tv", 
    amount=1, #building a new block (or figuring out the proof number) is awarded with 1
)
last_hash = last_block.compute_hash
block = blockchain.build_block(proof_number, last_hash)
print("WOW, MINING HAS BEEN SUCCESSFUL!")
print(blockchain.chain)

Now, let’s try to run our code to see if we can generate some digital coins…

Wow, it worked!

Conclusion

That is it!

We hope that this article has assisted you to understand the underlying technology that powers cryptocurrencies such as Bitcoin and Ethereum.

We just illustrated the basic ideas for making your feet wet in the innovative blockchain technology. The project above can still be enhanced by incorporating other features to make it more useful and robust.

Learn More

Thanks for reading !

Do you have any comments or questions? Please share them below.

#python #cryptocurrency

I am Developer

1611112146

Codeigniter 4 Autocomplete Textbox From Database using Typeahead JS - Tuts Make

Autocomplete textbox search from database in codeigniter 4 using jQuery Typeahead js. In this tutorial, you will learn how to implement an autocomplete search or textbox search with database using jquery typehead js example.

This tutorial will show you step by step how to implement autocomplete search from database in codeigniter 4 app using typeahead js.

Autocomplete Textbox Search using jQuery typeahead Js From Database in Codeigniter

  • Download Codeigniter Latest
  • Basic Configurations
  • Create Table in Database
  • Setup Database Credentials
  • Create Controller
  • Create View
  • Create Route
  • Start Development Server

https://www.tutsmake.com/codeigniter-4-autocomplete-textbox-from-database-using-typeahead-js/

#codeigniter 4 ajax autocomplete search #codeigniter 4 ajax autocomplete search from database #autocomplete textbox in jquery example using database in codeigniter #search data from database in codeigniter 4 using ajax #how to search and display data from database in codeigniter 4 using ajax #autocomplete in codeigniter 4 using typeahead js

Dylan  Iqbal

Dylan Iqbal

1630408920

Big Data Visualization: What, Why, Tips and Tools

Wondering what is big data visualization and how you can apply it for your business? Here's a guide to help you get started.

Because we live in a data-driven society, it’s likely that you’re constantly bombarded with complex sets of data that you need to transmit to your coworkers in an easy-to-grasp way.

The challenge is that almost no one wants to look at large lists of numbers and data, and important information can be easily lost within the midst of chaotic spreadsheets. But there is a solution, and that is big data visualization.

Today, we’ll be covering what big data visualization is and why it’s important, different big data visualization techniques you can use, tips and tricks for creating easily intelligible large data sets and the best big data visualization tools you can use.

By the end of this article, you’ll feel like a real data scientist and be competent in creating pie charts, bar charts, heat maps, histograms, interactive charts and more for big data visualization.

So let’s get into it, shall we?

Table of Contents

What is Big Data Visualization?

Why is Data Visualization Important in Big Data?

What Are the Types of Big Data Visualization?

5 Big Data Visualization Tips for Beginners

4 Tools for Big Data Visualization

---

What is Big Data Visualization?

Big data visualization is the representation of large sets of data through visual aids, whether that be through pie charts, heat maps, bar charts or any other kind of chart types or visual representation.

Analyzing and understanding large data sets and data analytics is no easy task and it can be especially difficult trying to relay that same information to colleagues who are not data-driven or data scientists.

That’s where big data visualization comes in. By transforming your large data sets into visually appealing infographics or interactive charts, you can easily convey your data points to fellow decision-makers.

When your data is plotted out on graphs in a visual way and metrics are made easily readable, no data gets lost in the mix, no matter how large or small, and it makes decision-making for the future a breeze.

Because you can’t make adequate decisions or advance significantly without analyzing your raw data, it’s important that companies use great data visualization methods to keep everyone in the loop.

Let’s take BMW for example.

Image Source

In 2020, BMW was able to track the number of sales for electric cars that they had and then compare it to other car companies’ sales, but not only.

They also were able to track the countries that bought the highest amount of their electric cars.

Image Source

This is a prime example of big data visualization in action. When you track your analytics and data, you can see where your wins are and when to celebrate or where your losses are and how you can make adjustments for the future.

Now, imagine for a moment that all this information was just written out plainly on a spreadsheet and had unstructured data all over it.

It would be hard to understand and assess how the company is doing and would take a long time to communicate to employees how their work has affected the sales of the cars.

This is why visualizing big data is so important. With just a glance and within seconds, you can easily see what cars are selling best and in what countries.

No time is wasted going through spreadsheets and trying to make sense of unstructured data — just visual analytics laid out for all to see and understand.

 

Why is Data Visualization Important in Big Data?

We live in a time where the internet and social media have exploded at an extraordinary rate, and information can be gathered within seconds and at the tips of anyone’s fingers.

With the rise of this technological era, it’s important that data can be visualized and consumed quickly and efficiently — especially since the human brain now has an attention span of about 8 seconds, according to this study by the Technical University of Denmark.

Because companies, businesses and organizations can gather data more quickly than ever, this means that they need to be able to visualize that data in an equally quick and easily consumable way.

The best way to efficiently communicate your ever-coming, new data is through visualizing big data. This will bring your complex data to life and anyone who looks at it will be able to understand and grasp it with just a glance.

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Take the image above as an example. With just a quick look at the statistics that are clearly visualized, you can make a data-based assumption.

Now, imagine if this data was just written out plainly on a spreadsheet. It would take much longer to understand and make an assumption based on the numbers.

By using big data visualization techniques, you’ll be able to get the most value from your data and analytics and make sure that everyone who says your data analysis will be able to interpret, understand and use your data. This, in turn, will help your company excel.

When you use data visualization techniques, it will optimize your use of data, help decision making and planning go smoothly, you’ll be able to identify and mitigate risk, extract loads of useful data and insights and improve your overall strategy and direction of your company.

There are no losses to using a visual representation of data, only wins. But there are lots of different types of data visualization that you can use.

Let’s discuss the different types of big data visualization and assess which one will work best for you.

 

What Are the Types of Big Data Visualization?

There are lots of different types of data visualization that data analysts like to use and depending on the amount of data. A data analyst may choose to use a pie chart to express their numerical data or a bar chart.

When looking at big data analytics regarding locations, one might choose to use an interactive heat map or maybe a pivot table.

We’re going to look at 8 common types of big data visualization and some data visualization examples for each to help you decipher which one will work best for you.

 

Type #1: Line Charts

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A line chart, also known as a line graph, is a graphic representation of data that plots a fixed value on one side and a variable on the other.

A line chart is a fantastic way to represent the relationship of data. You can use a line chart to represent changes and fluctuations of things within a certain period of time.

 

Type #2: Bar Charts

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A bar chart, also known as a bar graph, uses bars to compare different data points or data sets.

Many data scientists will use bar charts to visually represent their data analysis. You can use a bar chart to compare large amounts of data, fluctuations of quantities or different categories.

The taller the bar, the larger the numerical value and vice versa.

 

Type #3: Pie Charts

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Pie charts, donut charts, circle graphs or whatever you choose to call them, are representations of data that are split into smaller segments and sizes to represent their numerical value.

When you use a pie chart, it becomes easy to see and compare how the different segments relate and differ from each other.

When using a pie chart, try not to overload it with too many different values. When you split the pie chart into more than 7 segments, it can become difficult to understand the data.

 

Type #4: Heat Maps

Image Source

A heat map is a visual representation of data that is laid out on a map or table and uses different nuances and intensities of colors to represent its data.

Using a heat map can be especially helpful when you need to analyze data that seems to be never-ending. When you have an extremely wide value range, using a heat map makes it much more simple to quickly visualize and analyze large amounts of complex data at a glance.

 

Type #5: Histograms

histogram - weights of newborns

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A histogram is a graphical and visual representation of complex data sets and the frequency of said numerical data displayed through bars.

Histograms are very similar to bar graphs but vary in the fact that they mostly focus on the repeated frequency of numerical data.

Type #6: Scatter Plots

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A scatter plot, scatter chart or scatter graph, is a diagram that uses dots to represent and emphasize the different values of two or more numeric variables on an X and Y-axis.

Scatter plots are extremely useful to use when you have multiple large data sets and you want to know how they relate to each other and compare the importance of each value.

 

Type #7: Treemaps

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Treemaps are the visual representation of hierarchical data by using color-coded rectangles.

Users can use treemaps as a method to compare multiple sets of data and reflect the weight of each value in a project.

 

Type #8: Funnel Charts

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Funnel charts are typically used in sales and represent the different stages that your users or customers go through during the sales process and demonstrate decreasing values as they move through your funnel.

By using a funnel chart, you can accurately see where you are losing or gaining your customers during the sales process.

 

5 Big Data Visualization Tips for Beginners

Now that we’ve covered what big data visualization is, its importance and 9 different types of data visualization, you may feel like you’re a professional in data science.

Now that you’re familiar with the basics of data visualization, it’s time that we equip you with some of our best data visualization techniques.

Here are our top 5 best data visualization techniques for you to use when creating a visual representation of your data.

 

Tip #1: Use a Powerful Data Visualization Tool

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You can’t create powerful graphs without a powerful data visualization tool.

Sure, you could use something like Google charts, but to create unique, engaging charts, you’ll want to use a data visualization tool like Visme that's packed with amazing functionality.

Visme is a powerful data visualization tool with many integration functionalities. As you can see in the image above, you can create everything from funnel charts and tables to interactive data maps and graphs in this editor.

When you need to visualize big data, Visme is the way to go. When you create a graph in our big data visualization tool, your data can be updated in real-time with our integration tools.

You can import all your data from Google Sheets, Microsoft Excel, Google Analytics and other data sources, then see it come to life automatically on your project while you sit back and relax.

Visme also has many open-source elements and graphics for you to use to keep your infographic intriguing. To have the perfect interactive data visualization, you can use word clouds, tables, treemaps, animated characters and graphic design elements and more to implement into your design.

They’re also a powerhouse filled with lots of useful and educational tutorials on how to create the perfect chart for your raw data. Visme also has lots of tutorials for all things graphic design.

So why not use a tool that has everything you need for creating visuals for your data analysis and tons of tutorials to go with it? You can start your free account with Visme today and start living out your data analyst dreams.

It’s important to use a strong data visualization software for your data analysis and presentations. Stick around and soon we’ll get into our list of best tools for big data visualization.

 

Tip #2: Pick the Correct Form of Big Data Visualization

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When it comes to visualizing your data, you need to make sure that you choose the correct chart type.

Because there are so many different ways to display your data, you need to weigh out the cons and pros of each and find out which one will work best for your infographic or presentation.

Take for example pie charts and bar graphs.

When you analyze data that is very different, you might want to use a pie chart. But if you want to represent data entries that are close together, you could use a bar chart for that.

If you’re trying to create data visualization for sales, you could use a funnel chart, pyramid chart or cone chart for that.

Each different visualization method has its time and place, and you need to analyze your data and think about what method will work best for your respective data.

Refer above to the “Big Data Visualization Types” section above to see which one will suit you best.

 

Tip #3: Make Sure Your Data is Easily Comprehensible

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The whole point of big data visualization is to make it easy to understand at a glance.

It won’t be easily intelligible if you just start piling in large amounts of unstructured data and simply hope for the best. Or imagine you have tens of tiny little numbers on a bar graph that no one can see or read.

You need to make sure that anyone on your team, whether a data scientist or not, can understand what you’re trying to convey at a glance.

You can do this by using clear and bold text, contrasting font colors and background colors, not adding too many values to one chart and using compelling images to highlight your point, just like in the example above.

By adding too much text or too many values to a single graph, you risk confusing your audience even more. So keep it as simple and concise as possible.

 

Tip #4: Always Use Legends to Further Explain Your Data

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Using legends is absolutely vital for making your data easy to understand, so whether you’re creating a pie chart or bar graph, make sure you’re using a legend.

A legend is an area of your design that further explains each segment of your chart.

Many times people will assign a color to a segment in their chart, just like in the example above, and on the side add a little graphic element that explains what each color represents.

The legend is responsible for keeping the audience engaged and understanding everything you’re trying to convey.

 

Tip #5: Use Multiple Charts for Big Data

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If you have a large amount of data that needs to be conveyed to your team, try using multiple graphs to do so.

Incorporating tons of data into a single chart will only make it hard for the human brain to stay on track and focus to try and understand what you want to share.

The best rule of thumb to follow here is KISS — keep it simple, stupid.

So instead of simply adding all your data to one pie chart and making it have 30 pie slices, why not create multiple graphs and break it down into bite-sized pieces? Pun intended.

By creating multiple matching charts, you can keep your data easily intelligible, cohesive and right on brand.

Just like in the example above, you can clearly understand all the data that’s being displayed because it is written out on two different donut charts.

You want to make sure your information is understandable by anyone at a glance, and you can do so by breaking down your data.

 

4 Tools for Big Data Visualization

Now that you know essentially all there is to know about big data visualization, it’s time you choose a tool that will help you create those visuals.

We’ll be covering 4 data visualization software you can use to get the job done.

Let’s jump right into it.

 

Tool #1: Visme

Start visualizing data with beautiful charts and graphs!

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If you want to create compelling and professional data visualization, then you need a tool like Visme.

Visme makes it easy for both designers and non-designers alike to visualize their data in interactive and engaging ways.

For example, you can create incredible animated charts, add your own audio files to them that you can record right within the editor, add tons of professionally design data widgets and import all your data from third-party websites such as Microsoft Excel, Google sheets etc.

The best part? You can save endless amounts of time and effort by using one of our hundreds of customizable templates for displaying your data.

Simply scroll through tons of professionally design templates for charts and data choose one that suits your style.

Everything can be customized on each and every template and you can even add your own brand colors, logo and font to keep everything right on-brand with your other designs.

Not only can you create loads of beautiful charts, graphs and infographics with Visme, but you can also create anything else design-related. You can create presentations, infographics, multi-page reports and proposals, branded social graphics and more.

If you’re looking for a powerful data visualization tool with high functionality for many other types of designs, Visme is the one for you. Plus, you can create a free account and use for as long as you like — no trial period or hidden costs!

 

Tip #2: Tableau

Tableau is an interactive data visualization software with a focus on business intelligence. Their goal is to help people make data that can be easily understood by anyone.

Tableau is a tool that is used in the business intelligence industry and it can help you simplify raw data into a simple format. With drag and drop functionalities, you can create data visualization fairly quickly and then share it with others.

In Tableau you can create lots of different data visualizations, from a correlation matrix to a simple bar graph.

Another plus for the software is that you can infuse the Tableau dashboard with artificial intelligence and machine learning from Aible.

You can start a free trial with Tableau, but it is a bit pricey after your trial is up. At $70/month billed annually, you’ll have to make sure you absolutely love the product before buying it.

 

Tool #3: Microsoft’s Power BI

Power BI by Microsoft is a business analytics service that helps you create interactive data visualizations.

Whether your data is on an Excel spreadsheet an on-premises hybrid data warehouses, Power BI will help you bring that data together to create reports and graphs to share with your team.

There are three versions of Power BI that you can use: the desktop app, the mobile app or their website.

You can use Power BI to help you visualize big data with your team by using some of their other popular apps like Microsoft Excel and work together in real-time to create compelling data.

Power BI has some basic templates that you can use to get a jump start on creating your data.

Power BI is quite affordable, coming in at $9.99/month.

If you’re not completely sold on using Power BI, let’s move on to our next tool.

 

Tool #4: Datawrapper

Datawrapper is an online tool that you can use to create data visualizations that are interactive and responsive, with no code or programming languages like python or javascript required.

With big users like the New York Times and the UN, they do have quite a few things to boast about.

Data wrapper is an open-source and easy-to-use data visualization software where you can create basic charts and graphs, maps and line charts that can be embedded into your website.

As for the price, you can use their free plan and create lots of charts, maps and tables, but they will be watermarked and there are a few other inconveniences that come with the free plan.

The next plan comes in at $599/month, which is definitely on the pricey side.

And that concludes our list of 4 tools for data visualization.

 

Now Over to You

If you want a data visualization software that will help you convey your data in a fun and engaging way, then you most likely will love using Visme.

Not only is Visme a powerful data visualization tool, but it’s so much more. You can use Visme to create all of your graphic design needs, from sales presentations to pitch decks, social media posts, infographics, videos, eBooks and more.

What are you waiting for? Create your free account today and free your inner data scientist.

Originally published at https://visme.co

#datavisualization #bigdata

What is BUILD Finance (BUILD) | What is BUILD Finance token | What is BUILD token

BUILD Finance DAO

The document is non-binding. Some information may be outdated as we keep evolving.

BUILD Philosophy

BUILD Finance is a decentralised autonomous venture builder, owned and controlled by the community. BUILD Finance produces, funds, and manages community-owned DeFi products.

There are five core activities in which the venture BUILDers engage:

  1. Identifying business ideas,
  2. Organising teams,
  3. Sourcing capital,
  4. Helping govern the product entities, and
  5. Providing shared services.

BUILD operates a shared capabilities model, where the DAO provides the backbone support and ensures inter-entity synergies so that the product companies can focus on their own outcomes.

BUILD takes care of all organisational, hiring, back/mid office functions, and the product companies focus on what they can do best, until such time where any individual product outgrows the DAO and becomes fully self-sustainable. At that point, the chick is strong enough to leave the nest and live its own life. The survival of the fittest. No product entity is held within DAO by force.

Along the way, BUILD utilises the investment banking model, which, in its essence, is a process of creating assets, gearing them up, and then flipping them into a fund or setting them as income-generating business systems, all this while taking fees along the way at each step. BUILD heavily focuses on integrating each asset/product with each other to boost productive yield and revenues. For example, BUILD’s OTC Market may be integrated with Metric Exchange to connect the liquidity pools with the trading traffic. The net result – pure synergy that benefits each party involved, acting in a self-reinforcing manner.

El Espíritu de la Colmena (The Spirit of the Beehive)

BUILD is a hive and is always alive. While some members may appear more active than others, there’s no central source of control or “core teams” as such. BUILD is work in progress where everyone is encouraged to contribute.

Following the natural free market forces, BUILD only works on those products that members are wanting to work on themselves and that they believe have economic value. Effectively, every builder is also a user of BUILD’s products. We are DeFi users that fill the gaps in the ecosystem. Any member can contribute from both purely altruistic or ultra-mercantile intentions – it’s up to the wider community to decide what is deemed valuable and what product to support. The BUILD community is a sovereign individual that votes with their money and feet.

BUILD members = BUILD users. It’s that simple.

$BUILD TOKEN

Tokenomics

$BUILD token is used as a governance token for the DAO. It also represents a pro-rata claim of ownership on all DAO’s assets and liabilities (e.g. BUILD Treasury and $bCRED debt token).

The token was distributed via liquidity mining with no pre-sale and zero founder/private allocation. The farming event lasted for 7 days around mid-Sep 2020. At the time, BUILD didn’t have any products and held no value. Arguably, $BUILD has still zero value as it is not a legal instrument and does not guarantee or promise any returns to anyone. See the launch announcement here https://medium.com/@BUILD_Finance/announcing-build-finance-dc08df585e57​

Initial supply was 100,000 $BUILD with 100% distributed via fair launch. Subsequently, the DAO unanimously voted to approve minting of extra 30,000 $BUILD and allocate them as:

  • 15,000 $BUILD (11.5%) to the founding member of the DAO (@0xdev0) with 1-year gradual vesting, and
  • 15,000 $BUILD (11.5%) to the DAO treasury as development funds.

For the proposal of the above see: https://forum.letsbuild.finance/t/proposal-2-fund-the-development-of-defi-lending-platform/24​

The voting took place at a later retired web-page https://vote.letsbuild.finance. The governance has since moved to Snapshot (link below). The results of the old proposals are not visible there, however, on-chain voting contract can be see here: https://etherscan.io/address/0xa8621477645f76b06a41c9393ddaf79ddee63daf#readContract​

$Build Token Repartition

Vesting Schedule

Minting keys are not burnt = $BUILD supply is not fixed as token holders can vote on minting new tokens for specific reasons determined by the token holders. For example, the DAO may mint additional tokens to incentivise usage of its products, which would, in turn, increase the value flow or TVL. Dilution is not in the economic benefit of the token holders, hence any such events has to be considered carefully.

Access to minting function is available via on-chain governance. A safe buffer is established in a form of the contract-enforced 24 hour delay, which should provide a sufficient time for the community to flag. Meaning that before such a transaction could be executed, everyone would be able to act in advance by withdrawing their funds / exiting from BUILD. Any malicious minting would, theoretically, result in an immediate market sell-off of $BUILD, making it economically detrimental to do such an action. This makes it highly improbable that any malicious minting would be performed_._

GOVERNANCE

All components of the BUILD DAO and the control over its have been decentralised:

  • All contracts (incl. the Treasury and Basis Gold) can be operated by $BUILD holders with on-chain proposals (see https://docs.build.finance/build-knowledge-base/on-chain-voting);
  • All social accounts (Discord, Telegram, and Twitter) are managed by multiple moderators;
  • All frontends (Metric Exchange, Basis Gold, and the BUILD homepage) are auto-deployed and managed by multiple devs.

TREASURY & DEVELOPMENT

BUILD DAO Treasury

The BUILD treasury has over $400k that can be managed by on-chain proposals and used in whichever way the community desires. For example, to hire developers. Having a functioning product, enough funds in the treasury and a fully decentralised governance has been a long-term goal since the inception in September 2020, and now it’s finally here.

Current holdings are (might be outdated):

  • Capital budget (dev / incentives fund) - 11,025 $BUILD (~$94k);
  • Operational budget (product development) - 204,300 $aDAI;
  • Ownership stake - 200,000 $METRIC (~$84k);
  • Ownership stake - 199,900 $UPDOWN(~$62k);
  • Ownership stake - 5,400 $HYPE (~$1.3k);
  • Ownership stake - 2% of $BSGS supply.
  • TOTAL: ~$445k

Funding of the Development

In an early stage, the development will be funded by an allocation of bCRED debt tokens for development expenses. After the first product was built (i.e. Metric Exchange), the DAO sold 5,000 $BUILD for 203,849 $DAI which will now be used for funding of other products or a combination of revenue + a smaller token allocation. This is up to the community to decide.

Smart Contract Audit

Contracts are not audited. It’s up to the BUILD community governance to decide how to spend our funds. If the community wants to spend any amount for auditing, a voting proposal can be initiated. As with any decisions and proposals, the cost-benefit analysis must be employed in regards to the economical sense of spending any funds on audit vs developing more products and expanding our revenue streams.

DAO Liabilities and $bCRED

$bCRED is a token that allowed the DAO to reward members for work before the DAO source sufficient funds. Effectively, $bCRED is a promissory note or an IOU to give back $DAI at 1:1 ratio, when the DAO starts generating revenues. Read more about $bCRED here: https://medium.com/@BUILD_Finance/what-is-bcred-b97e4cc75f8c.

“BUILDER” User Role in Discord

Since Discord is our primary coordination mechanism, we need to make effort to keep it focused on producing value. During the launch of METRIC, we’ve doubled our total number of users! This made it very difficult for existing users to explain what BUILD is about to new users and created a lot of confusion.

To help improve the quality of conversations, we’ve introduced a new user role called BUILDer. BUILDers will have write-access to product development channels while everyone else will only be able to read them. This should keep those product changes focused on actual productive conversations and make them more informative.

“GUARDIAN” Role in Discord

To increase our collective output as a community, a governance vote introduced an incentivisation mechanism for community contribution, tipping, and other small projects using our unique bCRED token (but may change in the future as required). These tokens are stewarded by active community members — “guardians’’ — who are free to allocate these funds to tip people for proactive work. Current guardians are @Son of Ishtar and @0xdev0, although anyone can propose the tip for anyone else. For more details see Proposal #15.

Hence, Guardians are defined as members of the DAO who are entrusted with a community budget for tipping other members for performing various small tasks.

PRODUCT SUITE & ROADMAP

  • Metric Exchange - is a DEX aggregator that allows for limit orders trading for any ERC-20 token via 0x relayer. Development continues with the product owner SHA_2048 and inputs from vfat. Live at metric.exchange.
  • Basis Gold - a synthetic, algorythmically-adjusted token pegged to the price of gold (sXAU) with elastic supply. Live at https://basis.gold/.
  • Updown Finance - binary options for volatility trading instrument (alpha is live at updown.finance).
  • Vortex - a lending & borrowing platform, which will target the long tail of assets that are currently not served by the existing DeFi money markets. Aiming to launch by March’2021.

The other immediate focus right now will be to make good use of our newly available funding and hire several product managers for other projects.

Please note that nothing is here set in stone. Just like any other start-up, we’ll keep experimenting, learning, and evolving. What’s listed here is just our current trajectory but it might change at any point.

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#blockchain #cryptocurrency #build finance #build