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As the technology game powers up, every other task can be handled easily. Like you need not even get up to get the things done and they’re done already. We have started living in an era where technology drives humanity. But every good thing brings in a con we need to deal with. Even technology has its own set of cons to look after. Big data, Artificial Intelligence or Machine Learning everything which has brought some ease has brought in some cons too. Probably we’re too much into the goodness as of now and so realizing it later would cause troubles. So, it’s better to keep them in mind and act.
As the technology game powers up, every other task can be handled easily. Like you need not even get up to get the things done and they’re done already. We have started living in an era where technology drives humanity. But every good thing brings in a con we need to deal with. Even technology has its own set of cons to look after. Big data, Artificial Intelligence or Machine Learning everything which has brought some ease has brought in some cons too. Probably we’re too much into the goodness as of now and so realizing it later would cause troubles. So, it’s better to keep them in mind and act. Big data has probably got many meanings. The huge amount of data is probably scary if not taken carefully. The rising volume, variety and velocity of data are what makes big data so astonishing. Technically, these three words define what exactly big data is. With the rising technology around, we hardly have some spare time to analyze what exactly this data does. Organizations would have huge repositories of data but how they use it matters? What analysis is it going through? What purpose is the data fulfilling? Because an organization would gather data from a variety of resources such as business transactions, social media handles or information sensors. Handling it in the past was an issue, but technologies such as Hadoop have made it even simpler now. But this has come up with risking things also. Having an ample amount of data seems to be harmless until we know where it is used actually and what purpose this data is serving. Let’s say some company collects data about stock price rises, product performances etc. So, keeping a track of this data is desirable, but pulling up other details about the investors like their family insurance reports, or health plans they have opted for is what marks danger. As a result, it becomes necessary to draw a line between what’s required and what’s irrelevantly collected. Big data although has held us high but at the same time has failed to keep up with the privacy laws. Cybercrimes and major data leaks are the most threatening issues the growing technology brings in. If we have a look at the major data thefts closely, we can clearly understand that the identity theft is an all-time risk. Such attacks have compromised more than 200 million records last year. These majorly include basic information leaks like name, address or phone number. And such leaks are generally classified as nuisance data breach by the index. As the world becomes more compact and technology dependent, we’re more prone to cyber-attacks. Data manipulation or data integrity attacks are becoming more popular. The worst part is we do not know how the organization would act further. This arguably poses more threats as in the hackers can manipulate the details and alter the entire systems. The confidentiality of data and data integrity should be of prime importance. The organization has to put some controlling measures like encryption or key management or user access management to ace with this. So, fighting the big data flood is not a joke, because it has brought in various risks to conquer. The big data storm we’re currently into has to be handled carefully such as we maintain balance on both sides of it. Not only privacy but organizing the unorganized data, its storage and retention, the cost management and the incompetent analysis are various other difficulties to focus on. Potentially big data is now more dangerous than a weapon which can bring you down. It’s gigantic; it’s complex and is always carrying a set of risks to deal with. However, proper planning and execution can help us fight this long battle.
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Why Do We Need To Learn Powershell?
PowerShell Tutorial
Suppose you know a little bit about Linux, which provides a very rich command interface. Because of Linux rich command, Linux was a preferred platform for software development. On the other hand, windows was mostly used for UI-based uses for non-development purposes. So finally, to control all these issues, Microsoft released PowerShell version 1 for the first time in 2006. The main goal of PowerShell was to provide command rich interface to developers where developers will be able to write scripts and automate various jobs. So initially, they developed PowerShell for Windows only, but after version 6, it started supporting macOS and Linux as well.
Why do we need to learn PowerShell?In Windows, it has DOS cmd, But if we need to do complex scripting and if we need to write any heavy scripts jobs, then the existing cmd is not good enough. PowerShell allows developers on Windows to write a script with controlling one computer to multiple remote computers at once. DOS is just a shell where PowerShell is a powerful scripting language that is completely based on .NET and is mostly used by my administrator to handle Networks and servers. On Windows, if you use DOS as cmd, you will be only checking ipconfig and some basic things, whereas by learning PowerShell, you will be a complete programmer. Because of its rich commands and object-based approach, it is a powerful tool for scripting.
Below are some points why we should learn PowerShell.
Consistency: The biggest benefit of PowerShell of the current version is that it is available for all Operating systems. So, for example, if you are developing the script on a computer X and after successfully testing your script on your computer X, you can share your script with another person who is going to run your script on his computer Y, which will work perfectly from the version 6 because PowerShell is available for all OS, ie. Windows, Linux, and macOS. So a script will work on different architecture as well. Other than Architecture, PowerShell also provides automation to administration tasks with better performance .
Interactive and scripting environments: The Powershell of Windows Prompt gives us a very interactive tool to access the command-line interface for scripting.
Object orientation: As it is totally written over the .NET, it will give us a complete Object-based approach to implementing it. So we are not just writing a command. It allows us to explore more.
Applications of PowerShellIt will be very useful for administrative management with PowerShell admin to delete, add and update users. We transfer heavy files from one computer to another to multiple network computers at once. If Admin has some task that he will run repetitively, then the Admin can use PowerShell to create a script and put it into job cycles where it will run at given intervals.
ExampleSuppose, In PowerShell, we want to see the process with name “nginx” and “node.”
PrerequisitesYou can install Powershell by MSI, and you should only need to learn the basics of programming like, if, for loops and variables and it’s an available rich set of commands. Even if you do not know much programming you can directly start with PowerShell.
Target AudienceDevelopers: A developer can have requirements to develop a tool where he may change his data for a running application regularly. For example, on any e-commerce website, we want to show the best-selling products. So the developer will write a script that will fetch data daily and update top-selling product details so that top-selling products will be visible to end customers.
Administrator: The administrator can write a script for automation of updating, deleting, and performing certain tasks on all the users regularly to avoid repetition of the same tasks.
How Small Businesses Look To Leverage Big Data And Data Analytics
Benefits of Big Data for Small Businesses Following are key benefits of big data for small businesses- 1. Quick Access to Information Big data makes the generated information available and accessible at all times for the businesses in real-time. Various tools have been designed for capturing user data and thus, businesses can accumulate the information in terms of customer behavior. This huge chunk of information is readily available for the businesses at their disposal and they can implement effective strategies for improving their prospects. 2. Tracking Outcomes of Decisions Businesses of any size can gain huge amounts of benefits from the data-driven analytics and this calls for the deployment of big data. Big data enable businesses to track the outcomes of their promotional strategies and giving the companies a clear understanding of what works well for them and improves their decisions to gain better results. Small businesses can tap on this information to know which of their brands are being perceived by their key customers. Based on this information, businesses can carry out accurate predictions regarding their techniques and at the same time minimize their risks. 3. Developing Better Products and Services Small businesses can use big data and analytics for determining the current requirements of their prospective customers. Big data can help in analyzing customer behavior based on their previous trends. A proper analysis of customer behavior and its associated data helps businesses to develop better products and services based on their past needs. Big data also determines the performance of certain products and services of the company and how they can be used to meet these demands. Big data now also allows the companies to test their product designs and determine flaws that may cause losses in case that product is marketed. Big data is also used for enhancing after-sales services like- maintenance, support, etc. 4. Cost-Effective Revenues How Small Businesses Use Data Analytics • One of the key applications of machine learning for small businesses is by using it for tracking their customers at various stages of the sales cycle. Small businesses have been using data analytics for determining exactly when a given segment of customers are ready to buy and when they’re going to do so. • Data analytics are also used for improving customer services. Machine learning tools are now able to analyze the conversations taking place between the sales team and customers across various channels. These can provide greater insights into some of the commonly faced issues by the customers and these can be leveraged for ensuring that customers have a great experience with a product/service/brand. • Data analytics have been providing the SMBs with detailed insights on operational aspects. Data analytics can be of great use when it comes to a detailed analysis of customer behavior. This, in turn, allows the business owners to learn the motivating factors for the consumers to buy products or services. This is of great value as the SMB owners can utilize this information for identifying the market channels to focus in the coming time and thus saving on the marketing spend and thus increasing the market revenue. Data Analytics Trends in 2023 for Small Businesses 1. Emergence of Deep Learning We have been generating huge volumes of data every day and it is estimated that the humans generate 2.5 quintillions of data. Machines have become more adept and deep learning capabilities are continuing to rise in the coming time. Often considered as a subset of machine learning, deep learning uses an artificial neural network that learns from the huge volume of data. Its working is considered to be similar to that of the human brain. This level of functionality helps the machines to solve high and complex problems with great degrees of precision. Deep learning has been helping small businesses in enhancing their decision-making capabilities and elevating the operations to the next level. Using deep learning, the chatbots are now able to respond with much more intelligence to a number of questions and ultimately creating helpful interactions with the customers. 2. Mainstreamed Machine Learning 3. Dark Data Dark data is used for defining those information assets that the enterprises collect, process or store but have failed to utilize. It is that data that holds value but gets eventually lost in the middle. Some common examples of dark data include- unused customer data, email attachments that are opened but left undeleted. It is estimated that dark data is going to constitute 93% of all data in the near future and various organizations look to formulate steps to utilize it.
Governments Leveraging Big Data Innovations To Tackle Coronavirus
The outbreak of coronavirus has taken many countries under its hood. Most of them are suffering from economic loss and a higher mortality rate. Amid this, governments are in a great dilemma – how to handle the circumstances around the falling economy and upsurging coronavirus infections. In order to get better hold onto situations across their countries, they are moving towards innovative technology adoption. Out of all the new-age technologies, big data and data analytics can serve with a great opportunity, where governments across various nations can understand the outbreak analytics. In South Korea , where initially the infections spun out of control, the government has been able to slow down the pandemic by using aggressive tracking tools. For example, the government has released a smartphone app that can track self-quarantine subjects to ensure that they do not leave their homes and maintain strict separation from other people, including family members. Those under quarantine can use the app to report their symptoms, and provide status updates to officials. Similarly, Israel is using mobile phone data to track the movements of those who have tested positive for the virus and to identify those who need to be quarantined. Moreover, across Bangladesh, the government has initiated a process to draw a digital map to track coronavirus cases and find out areas susceptible to contamination by using mobile users’ information — a move that may help portray the real picture of a possible outbreak. Under a self-reporting method, mobile users will get a short message (SMS) from their operators and in reply, they will share some of their health information. All the 16.62 crore mobile phone users in the country will start getting SMS from this morning and they will be asked to make a call to *3332# free of charge. During the 90-second call in the form of interactive voice response (IVR), users will reply to five questions about their age; whether they have breathing problems; if they have fever or cough; whether they have come in contact with someone who returned from abroad recently; and if they have come close to any coronavirus-infected people. Furthermore, one of the countries that have efficiently harnessed big data analytics to contain the spread of the Coronavirus pandemic is Taiwan . The country reported only about 50 odd cases by mid-March, while its neighbor (South Korea) had clocked close to 8000 cases. Taiwanese officials have conducted a detailed mapping of people who were infected and from whom they caught the infection. They integrated the Taiwan National Health Insurance databases with immigration and customs databases. Using all of this data, the Taiwanese Government could trace the 14-days travel histories and symptoms of its citizens. Further, international travelers were asked to scan a QR code. This redirected them to an online health declaration, which was used to provide contact information and symptoms. The US government is in active talks with Facebook, Google and a wide array of tech companies and health experts about how they can use location data gleaned from Americans’ phones to combat the novel coronavirus, including tracking whether people are keeping one another at safe distances to stem the outbreak. Public-health experts are interested in the possibility that private-sector companies could compile the data in anonymous, aggregated form, which they could then use to map the spread of the infection, according to three people familiar with the effort, who spoke on the condition of anonymity because the project is in its early stages.
Introduction To Big O Notation In Data Structure
Introduction
One of the most essential mathematical notations in computer science for determining an algorithm’s effectiveness is the Big O notation. The length of time, memory, other resources, as well as a change in input size required to run an algorithm can all be used to evaluate how effective it is. Data structure’s Big O Notation provides information about an algorithm’s performance under various conditions. In other words, it provides the worst-case complexity or upper-bound runtime of an algorithm.
Big O Notation in Data StructureA change in input size can affect how well an algorithm performs. Asymptotic notations, such as Big O notation, are useful in this situation. When the input goes toward a particular or limiting value, asymptotic notations can be used to represent how long an algorithm will run.
Algebraic terms are used to indicate algorithmic complexity using the Big O Notation within data structures. It determines the time and memory required to run an algorithm for a given input value and represents the upper bound of an algorithm’s runtime.
A mathematical notation called Big O is named after the phrase “order of the function,” which refers to the growth of functions. It is a member of the Asymptotic Notations family and is also known as Landau’s Symbol.
Mathematical ExplanationConsider the functions f(n) & g(n), where f and g have unbounded definitions on the collection of positive real numbers. Every big value of n has a strict positive value for g(n).
The following can be written:
Where n goes to infinity (n ), f(n) = O(g(n)).
The expression above can be expressed succinctly as:
f(n) = O(g(n)).
Analysis of AlgorithmThe following describes the general step-by-step process for Big-O runtime analysis
Determine the input and what n stands for.
Describe the algorithm’s highest limit of operations in terms of n.
Remove all but the terms with the highest order.
Eliminate all the consistent elements.
The following are some of the Big-O notation analysis’s beneficial characteristics
If f(n) = f1(n) + f2(n) + — + FM(n) and fi(n) fi+1(n) i=1, 2, –, m, then the Summation Function is: Hence, O(f(n)) = O(max(f1(n), f2(n), -, fm(n))
If f(n) = log an and g(n) = log bn, then the Logarithmic Function is O(f(n)) = O(g(n)) .
If f(n) = g(n), then
f(n) = a0 + a1.n + a2.n2 + — + chúng tôi if polynomial function, then O(f(n)) = O(nm) (nm).
We must compute and analyze the very worst runtime complexities of an algorithm to evaluate and assess its performance. The quickest runtime for an algorithm is O(1), also known as Constant Running Time, and it takes the same amount of time regardless of the quantity of the input. Despite being the optimal runtime for an algorithm, Constant Running Time is rarely achieved because the duration relies on the size of n inputs.
Examples of typical algorithms with high runtime complexity
Linear Search Runtime Complexity: O (n)
Binary Search Runtime Complexity – O (log n)
Bubble sorting, insertion sorting, selection sorting, and bucket sorting have runtime complexity of O(nc).
Exponential algorithms like the Tower of Hanoi have runtime complexity of O(cn).
Heap Sort and Merge Sort Runtime Complexity in O (n log n).
Analyzing Space ComplexityDetermining an algorithm’s space complexity is also crucial. This is because the space complexity of an algorithm shows how much memory it requires. We contrast the algorithm’s worst-case space complexities. Functions are categorized using the Big O notation according to how quickly they expand; many functions with the same rate of growth could be written using the same notation.
Since a function’s order is also referred to as its development rate, the symbol O is used. A function’s development rate is typically only constrained by the upper bound in a large O notation representation of the function.
The following actions must be taken first before Big O notation may analyze the Space complexity
Program implementation for a specific algorithm.
It is necessary to know the amount of input n to determine how much storage every item will hold.
Some Typical Algorithms’ Space Complexity
Space Complexity is O for linear search, binary search, bubble sort, selection sort, heap sort, and insertion sort (1).
Space complexity for the radix sort is O(n+k).
Space complexity for quick sorting is O (n).
Space complexity for a merge sort is O (log n).
Let us Explore Some Examples:void
linearTimeComplex
(
int
a
[],
int
s
)
{
for
(
int
i
=
0
;
i
<
s
;
i
++)
{
}
}
This function executes in O(n) time, sometimes known as “linear time,” where n is just the array’s size in items. We must print 10 times if the array contains 10 elements. We must print 1000 times if there are 1000 items and the complexity we get is O(n).
void
quadraTimeComplex
(
int
a
[],
int
s
)
{
for
(
int
i
=
0
;
i
<
s
;
i
++)
{
for
(
int
j
=
0
;
j
<
s
;
j
++)
{
}
}
}
We are layering two loops here. When there are n items in our array, the outer loop iterates n times, the inner loop iterates n times for every iteration of an outer loop, and the result is n2 total prints. We must print 100 times if the array contains 10 elements. We must print 1000000 times if there are 1000 items. So, this function takes O(n2) time to complete, and we get complexity as O(n^2).
void
constTimeComplex
(
int
a
[])
{
printf
(
"First array element = %d"
,
a
[
0
]);
}
In relation to its input, this function executes in O(1) time, sometimes known as “constant time.” There need only be one step for this method, regardless of whether the input array contains 1 item or 1,000 things.
ConclusionBig O Notation is particularly helpful in understanding algorithms if we work with big data. The tool helps programmers to determine the scalability of an algorithm or count the steps necessary to produce outputs based on the data the programme utilizes. If users are attempting to run our code to increase its efficiency, the Big O Notation in Data Structures can be particularly helpful.
Why Does Chatgpt Need Phone Number?
When you register for a new ChatGPT account, you’ll notice that the Openai asks for your phone number. You cannot complete the registration process without verifying your number. However, many users are concerned about their security and privacy while using ChatGPT.
ChatGPT has been banned in some countries for collecting and storing users’ information without their consent. If you know the same, you might think twice before submitting your phone number to ChatGPT.
Despite all, the question is, Why does ChatGPT need a phone number? Isn’t it possible to use ChatGPT without entering your phone number?
Well, ChatGPT asks for your phone number mainly for various reasons. Read this guide to learn why ChatGPT asks for your phone number during registration.
Why does ChatGPT need phone number?
ChatGPT doesn’t accept VoIP (Voice over Internet Protocol) numbers, which means you’ll need an active mobile number to verify your account.
ChatGPT supports two-factor authentication. Firstly, it verifies its user’s email address and, secondly, its phone number. The phone number verification step ensures you are an actual human and not creating a fake account.
Adding an additional security layer also ensures that your account doesn’t get hacked easily.
ChatGPT also asks for a valid phone number to ensure its users don’t create multiple accounts because a user may have multiple email addresses but not numbers. So, by asking for a phone number, ChatGPT ensures that every user creates a limited number of accounts.
Again, ChatGPT is banned in some countries. So, if you try accessing ChatGPT from a restricted country, the phone number verification step will prevent you from proceeding ahead. The phone number will help ChatGPT to verify your country of residence. It also provides country-specific benefits to some users.
Another reason for providing your phone number to ChatGPT is to use it for account recovery or claiming your account in the future.
For instance, if you run into some issue while logging into ChatGPT, you can give your phone number and identity to the customer support team, who will help you recover your password.
The company also states that the user’s phone number helps prevent fraudulent activities, scams, and account hacking.
Is it safe to give OpenAI my phone number?
Your phone number may be linked to several places, including your bank accounts. By leaking your phone number to third-party vendors and other business owners, Open AI can put your personal information at risk of being hacked.
So, it’s evident to think before entering your phone number on ChatGPT’s website.
Again, with the recent news regarding ChatGPT’s ban in Italy, your concerns may have been raised more. But you shouldn’t worry!
Open AI promises to keep your mobile number safe and not disclose it to anyone else. The company only asks for your phone number to verify your identity and keep your account secure. So, it is safe to give your phone number to ChatGPT.
Does ChatGPT store your phone data?
ChatGPT stores its users’ information, such as personal details, social media details, usage data, cookies, and log information. It uses this information to enhance the user experience and improve the platform.
ChatGPT also collects and saves the chat conversations until the user deletes them. After deleting the chat history, ChatGPT stores its user information for a few days before deleting it from its records permanently.
Open AI also mentions in its privacy policy that the data it collects will be deleted from its records when you stop using its services. However, it will take several days to delete the data based on how important and sensitive the user’s information is.
Can you sign up for ChatGPT without giving your phone number?
Unfortunately, ChatGPT doesn’t allow users to use the platform without providing a phone number. So, you must enter your phone number and verify it to complete the registration process. Besides, ChatGPT doesn’t support VoIP numbers.
However, some virtual number generator websites like Dingtone claim they use real phone numbers to prevent online sites from detecting them. You can use such websites to generate a phone number and try using it to create a ChatGPT account.
You can also use a private number to sign up for a ChatGPT account. This number won’t reveal your personal information but will help complete the registration process.
Again, in some countries, ChatGPT supports account verification via WhatsApp. You can share your WhatsApp details to use ChatGPT without giving your phone number. This option is most feasible when you don’t have an active cell phone.
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