Trending March 2024 # Why Synthetic Data And Deepfakes Are The Future Of Data Analytics? # Suggested April 2024 # Top 4 Popular

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Synthetic data can help test exceptions in software design or software response when scaling.

It’s impossible to understand what’s going on in the enterprise technology space without first understanding data and how data is driving innovation.

What is synthetic data?

Synthetic data is data that you can create at any scale, whenever and wherever you need it. Crucially, synthetic data mirrors the balance and composition of real data, making it ideal for fueling machine learning models. What makes synthetic data special is that data scientists, developers, and engineers are in complete control. There’s no need to put your faith in unreliable, incomplete data, or struggle to find enough data for machine learning at the scale you need. Just create it for yourself.  

What is Deepfake?

Deepfake technology is used in synthetic media to create falsified content, replace or synthesizing faces, and speech, and manipulate emotions. It is used to digitally imitate an action by a person that he or she did not commit.  

Advantages of deepfakes:

Bringing Back the Loved Ones! Deepfakes have a lot of potential users in the movie industry. You can bring back a decedent actor or actress. It can be debated from an ethical perspective, but it is possible and super easy if we do not think about ethics! And also, probably way cheaper than other options.  

Chance of Getting Education from its Masters

Just imagine a world where you can get physics classes from Albert Einstein anytime, anywhere! Deepfake makes impossible things possible. Learning topics from its masters is a way motivational tool. You can increase the efficiency, but it still has a very long way to go.  

Can Synthetic Data bring the best in Artificial Intelligence (AI) and Data Analytics?

In this technology-driven world, the need for training data is constantly increasing. Synthetic data can help meet these demands. For an AI and data analytics system, there is no ‘real’ or ‘synthetic’; there’s only data that we feed it to understand. Synthetic data creation platforms for AI training can generate the thousands of high-quality images needed in a couple of days instead of months. And because the data is computer-generated through this method, there are no privacy concerns. At the same time, biases that exist in real-world visual data can be easily tackled and eliminated. Furthermore, these computer-generated datasets come automatically labeled and can deliberately include rare but crucial corner cases, even better than real-world data. According to Gartner, 60 percent of the data used for AI and data analytics projects will be synthetic by 2024. By 2030, synthetic data and deepfakes will have completely overtaken real data in AI models.  

Use Cases for Synthetic Data

There are a number of business use cases where one or more of these techniques apply, including:

Software testing: Synthetic data can help test exceptions in software design or software response when scaling.

User-behavior: Private, non-shareable user data can be simulated and used to create vector-based recommendation systems and see how they respond to scaling.

Marketing: By using multi-agent systems, it is possible to simulate individual user behavior and have a better estimate of how marketing campaigns will perform in their customer reach.

Art: By using GAN neural networks, AI is capable of generating art that is highly appreciated by the collector community.

Simulate production data: Synthetic data can be used in a production environment for testing purposes, from the resilience of data pipelines to strict policy compliance. The data can be modeled depending on the needs of each individual.

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What Is Big Data? Why Big Data Analytics Is Important?

What is Big Data? Why Big Data Analytics Is Important? Data is Indispensable. What is Big Data?

Is it a product?

Is it a set of tools?

Is it a data set that is used by big businesses only?

How big businesses deal with big data repositories?

What is the size of this data?

What is big data analytics?

What is the difference between big data and Hadoop?

These and several other questions come to mind when we look for the answer to what is big data? Ok, the last question might not be what you ask, but others are a possibility.

Hence, here we will define what is it, what is its purpose or value and why we use this large volume of data.

Big Data refers to a massive volume of both structured and unstructured data that overpowers businesses on a day to day basis. But it’s not the size of data that matters, what matters is how it is used and processed. It can be analyzed using big data analytics to make better strategic decisions for businesses to move.

According to Gartner:

Importance of Big Data

The best way to understand a thing is to know its history.

Data has been around for years; but the concept gained momentum in the early 2000s and since then businesses started to collect information, run big data analytics to uncover details for future use.  Thereby, giving organizations the ability to work quickly and stay agile.

This was the time when Doug Laney defined this data as the three Vs (volume, velocity, and variety):

Volume: is the amount of data moved from Gigabytes to terabytes and beyond.

Velocity: The speed of data processing is velocity.

Variety: data comes in different types from structured to unstructured. Structured data is usually numeric while unstructured – text, documents, email, video, audio, financial transactions, etc.

Where these three Vs made understanding big data easy, they even made clear that handling this large volume of data using the traditional framework won’t be easy.  This was the time when Hadoop came into existence and certain questions like:

What is Hadoop?

Is Hadoop another name of big data?

Is Hadoop different than big data?

All these came into existence.

So, let’s begin answering them.

Big Data and Hadoop

Let’s take restaurant analogy as an example to understand the relationship between big data and Hadoop

Tom recently opened a restaurant with a chef where he receives 2 orders per day he can easily handle these orders, just like RDBMS. But with time Tom thought of expanding the business and hence to engage more customers he started taking online orders. Because of this change the rate at which he was receiving orders increased and now instead of 2 he started receiving 10 orders per hour. This same thing happened with data. With the introduction of various sources like smartphones, social media, etc data growth became huge but due to a sudden change handling large orders/data isn’t easy. Hence a need for a different kind of strategy to cope up with this problem arise.

Likewise, to tackle the data problem huge datasets, multiple processing units were installed but this wasn’t effective either as the centralized storage unit became the bottleneck. This means if the centralized unit goes down the whole system gets compromised. Hence, there was a need to look for a better solution for both data and restaurant.

Tom came with an efficient solution, he divided the chefs into two hierarchies, i.e. junior and head chef and assigned each junior chef with a food shelf. Say for example the dish is pasta sauce. Now, according to Tom’s plan, one junior chef will prepare pasta and the other junior chef will prepare the sauce. Moving ahead they will hand over both pasta and sauce to the head chef, where the head chef will prepare the pasta sauce after combining both the ingredients, the final order will be delivered. This solution worked perfectly for Tom’s restaurant and for Big Data this is done by Hadoop.

Hadoop is an open-source software framework that is used to store and process data in a distributed manner on large clusters of commodity hardware. Hadoop stores the data in a distributed fashion with replications, to provide fault tolerance and give a final result without facing bottleneck problem. Now, you must have got an idea of how Hadoop solves the problem of Big Data i.e.

Storing huge amount of data.

Storing data in various formats: unstructured, semi-structured and structured.

The processing speed of data.

So does this mean both Big Data and Hadoop are same?

We cannot say that, as there are differences between both.

What is the difference between Big Data and Hadoop?

Big data is nothing more than a concept that represents a large amount of data whereas Apache Hadoop is used to handle this large amount of data.

It is complex with many meanings whereas Apache Hadoop is a program that achieves a set of goals and objectives.

This large volume of data is a collection of various records, with multiple formats while Apache Hadoop handles different formats of data.

Hadoop is a processing machine and big data is the raw material.

Now that we know what this data is, how Hadoop and big data work. It’s time to know how companies are benefiting from this data.

How Companies are Benefiting from Big Data?

A few examples to explain how this large data helps companies gain an extra edge:

Coca Cola and Big Data

Coca-Cola is a company that needs no introduction. For centuries now, this company has been a leader in consumer-packaged goods. All its products are distributed globally. One thing that makes Coca Cola win is data. But how?

Coca Cola and Big data:

Using the collected data and analyzing it via big data analytics Coca Cola is able to decide on the following factors:

Selection of right ingredient mix to produce juice products

Supply of products in restaurants, retail, etc

Social media campaign to understand buyer behavior, loyalty program

Creating digital service centers for procurement and HR process

Netflix and Big Data

To stay ahead of other video streaming services Netflix constantly analyses trends and makes sure people get what they look for on Netflix. They look for data in:

Most viewed programs

Trends, shows customers consume and wait for

Devices used by customers to watch its programs

What viewers like binge-watching, watching in parts, back to back or a complete series.

For many video streaming and entertainment companies, big data analytics is the key to retain subscribers, secure revenues, and understand the type of content viewers like based on geographical locations. This voluminous data not only gives Netflix this ability but even helps other video streaming services to understand what viewers want and how Netflix and others can deliver it.

Alongside there are companies that store following data that helps big data analytics to give accurate results like:

Tweets saved on Twitter’s servers

Information stored from tracking car rides by Google

Local and national election results

Treatments took and the name of the hospital

Types of the credit card used, and purchases made at different places

What, when people watch on Netflix, Amazon Prime, IPTV, etc and for how long

Hmm, so this is how companies know about our behavior and they design services for us.

What is Big Data Analytics?

The process of studying and examining large data sets to understand patterns and get insights is called big data analytics. It involves an algorithmic and mathematical process to derive meaningful correlation. The focus of data analytics is to derive conclusions that are based on what researchers know.

Importance of big data analytics

Ideally, big data handle predictions/forecasts of the vast data collected from various sources. This helps businesses make better decisions. Some of the fields where data is used are machine learning, artificial intelligence, robotics, healthcare, virtual reality, and various other sections. Hence, we need to keep data clutter-free and organized.

This provides organizations with a chance to change and grow. And this is why big data analytics is becoming popular and is of utmost importance. Based on its nature we can divide it into 4 different parts:

In addition to this, large data also play an important role in these following fields:

Identification of new opportunities

Data harnessing in organizations

Earning higher profits & efficient operations

Effective marketing

Better customer service

Now, that we know in what all fields data plays an important role. It’s time to understand how big data and its 4 different parts work.

Big Data Analytics and Data Sciences

Data Sciences, on the other hand, is an umbrella term that includes scientific methods to process data. Data Sciences combine multiple areas like mathematics, data cleansing, etc to prepare and align big data.

Due to the complexities involved data sciences is quite challenging but with the unprecedented growth of information generated globally concept of voluminous data is also evolving.  Hence the field of data sciences that involve big data is inseparable. Data encompasses, structured, unstructured information whereas data sciences is a more focused approach that involves specific scientific areas.

Businesses and Big Data Analytics

Due to the rise in demand use of tools to analyze data is increasing as they help organizations find new opportunities and gain new insights to run their business efficiently.

Real-time Benefits of Big Data Analytics

Data over the years has seen enormous growth due to which data usage has increased in industries ranging from:

Banking

Healthcare

Energy

Technology

Consumer

Manufacturing

All in all, Data analytics has become an essential part of companies today.

Job Opportunities and big data analytics

Data is almost everywhere hence there is an urgent need to collect and preserve whatever data is being generated. This is why big data analytics is in the frontiers of IT and had become crucial in improving businesses and making decisions. Professionals skilled in analyzing data have got an ocean of opportunities. As they are the ones who can bridge the gap between traditional and new business analytics techniques that help businesses grow.

Benefits of Big Data Analytics

Cost Reduction

Better Decision Making

New product and services

Fraud detection

Better sales insights

Understanding market conditions

Data Accuracy

Improved Pricing

How big data analytics work and its key technologies

Here are the biggest players:

Machine Learning: Machine learning, trains a machine to learn and analyze bigger, more complex data to deliver faster and accurate results. Using a machine learning subset of AI organizations can identify profitable opportunities – avoiding unknown risks.

Data management: With data constantly flowing in and out of the organization we need to know if it is of high quality and can be reliably analyzed. Once the data is reliable a master data management program is used to get the organization on the same page and analyze data.

Data mining: Data mining technology helps analyze hidden patterns of data so that it can be used in further analysis to get an answer for complex business questions. Using data mining algorithm businesses can make better decisions and can even pinpoint problem areas to increase revenue by cutting costs. Data mining is also known as data discovery and knowledge discovery.

In-memory analytics: This business intelligence (BI) methodology is used to solve complex business problems. By analyzing data from RAM computer’s system memory query response time can be shortened and faster business decisions can be made. This technology even eliminates the overhead of storing data aggregate tables or indexing data, resulting in faster response time. Not only this in-memory analytics even helps the organization to run iterative and interactive big data analytics.

Predictive analytics: Predictive analytics is the method of extracting information from existing data to determine and predict future outcomes and trends. techniques like data mining, modeling, machine learning, AI are used to analyze current data to make future predictions. Predictive analytics allows organizations to become proactive, foresee future, anticipate the outcome, etc. Moreover, it goes further and suggests actions to benefit from the prediction and also provide a decision to benefit its predictions and implications.

Text mining: Text mining also referred to as text data mining is the process of deriving high-quality information from unstructured text data. With text mining technology, you uncover insights you hadn’t noticed before. Text mining uses machine learning and is more practical for data scientists and other users to develop big data platforms and help analyze data to discover new topics.

Big data analytics challenges and ways they can be solved

A huge amount of data is produced every minute hence it is becoming a challenging job to store, manage, utilize and analyze it.  Even large businesses struggle with data management and storage to make a huge amount of data usage. This problem cannot be solved by simply storing data that is the reason organizations need to identify challenges and work towards resolving them:

Improper understanding and acceptance of big data

Meaningful insights via big data analytics

Data storage and quality

Security and privacy of data

Collection of meaningful data in real-time: Skill shortage

Data synching

Visual representation of data

Confusion in data management

Structuring large data

Information extraction from data

Organizational Benefits of Big Data

Big Data is not useful to organize data, but it even brings a multitude of benefits for the enterprises. The top five are:

Understand market trends: Using large data and  big data analytics, enterprises can easily, forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behavior. These insights in return help understand purchasing patterns, buying patterns, preference and more. Such beforehand information helps in ding planning and managing things.

Understand customer needs:  Big Data analytics helps companies understand and plan better customer satisfaction. Thereby impacting the growth of a business. 24*7 support, complaint resolution, consistent feedback collection, etc.

Improving the company’s reputation: Big data helps deal with false rumors, provides better service customer needs and maintains company image. Using big data analytics tools, you can analyze both negative and positive emotions that help understand customer needs and expectations.

Promotes cost-saving measures: The initial costs of deploying Big Data is high, yet the returns and gainful insights more than you pay. Big Data can be used to store data more effectively.

Makes data available: Modern tools in Big Data can in actual-time presence required portions of data anytime in a structured and easily readable format.

Sectors where Big Data is used:

Retail & E-Commerce

Finance Services

Telecommunications

Conclusion

With this, we can conclude that there is no specific definition of what is big data but still we all will agree that a large voluminous amount of data is big data. Also, with time the importance of big data analytics is increasing as it helps enhance knowledge and come to a profitable conclusion.

If you are keen to benefit from big data, then using Hadoop will surely help. As it is a method that knows how to manage big data and make it comprehensible.

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About the author

Preeti Seth

Applications Of Data Analytics In Hospitality

Most hospital industry players find it hard to attract new customers and convince them to come back again. It is important to develop ways to stand out from your competitors when working in a competitive market like the hospitality sector.

Client analytic solutions have proven to be beneficial recently since they detect problem areas and develop the best solution. Data analytics application in the hospitality industry has proven to increase efficiency, profitability, and productivity.

Data analytics assists companies to receive real-time insights that inform them where improvement is required, among others. Most companies in the hospitality sector have incorporated a data analytics platform to stay ahead of their rivals.

Below we discuss the applications of data analytics in hospitality.

1. Unified Client Experience

Most customers use several gadgets when booking, browsing, and knowing more about hotels. This makes it essential to have a mobile-friendly app or website and make sure the customer can shift from one platform to the other easily.

The customer’s data should be readily accessible despite the booking method or the gadget used during the reservation. Companies that create a multi-platform, seamless customer experience not only enhance their booking experience but also encourage their customers to return.

2. Consolidates Date from Various Channels

Customers enjoy various ways to book rooms and other services, from discount websites to travel agents and direct bookings. It is essential to ensure your enterprise has relevant information concerning the customer’s reservation to provide the best service. This data can also be important for analytics.

3. Targeted Discounts and Marketing

Targeted marketing is an important tool. Remember, not all guests are looking for the exact thing, and you might share information they are not concerned about by sending them the same promotions.

However, customer analytic solutions assist companies in sending every individual promotion they are interested in, which causes an improved conversion rate. Companies also use these analytics to target their website’s visitors, not just those on the email list.

4. Predictive Analysis

Predictive analysis is an important tool in most industries. This tool is the most suitable course of action for a company’s future projects, instead of simply determining how much a certain project has been successful.

These tools enable businesses to test various options before determining which one has a high chance of succeeding. Consider investing in robust analytics since it saves you significant money and time.

Also read:

Top 10 Successful SaaS Companies Of All Times

5. Develop Consistent Experiences

The best way to improve client satisfaction and loyalty is to ensure their data is more accessible to all brand properties. For example, if a hotel has determined former customers’ most common preferences and needs, they should make this information accessible to the entire chain.

This enables all hotels to maximize this information, which enables them to provide their customers with a seamless and consistent experience.

6. Enhances Revenue Management

Data analytics is important in the hospitality industry since it assists hoteliers in coming up with a way of handling revenue using the information acquired from different sources, like those found online.

Final Thoughts

More and more industries continue adopting data analytics due to its substantial benefits. The above article has discussed data analytics applications in the hospitality sector, and you can reach out for more information.

Hypothesis Testing For Data Science And Analytics

This article was published as a part of the Data Science Blogathon.

Introduction to Hypothesis Testing

Every day we find ourselves testing new ideas, finding the fastest route to the office, the quickest way to finish our work, or simply finding a better way to do something we love. The critical question, then, is whether our idea is significantly better than what we tried previously.

These ideas that we come up with on such a regular basis – that’s essentially what a hypothesis is. And testing these ideas to figure out which one works and which one is best left behind, is called hypothesis testing.

The article is structured in a manner that you will get examples in each section. You’ll get to learn all about hypothesis testing, p-value, Z test, t-test and much more.

Fundamentals of Hypothesis Testing

Let’s take an example to understand the concept of Hypothesis Testing. A person is on trial for a criminal offence and the judge needs to provide a verdict on his case. Now, there are four possible combinations in such a case:

First Case: The person is innocent and the judge identifies the person as innocent

Second Case: The person is innocent and the judge identifies the person as guilty

Third Case: The person is guilty and the judge identifies the person as innocent

Fourth Case: The person is guilty and the judge identifies the person as guilty

As you can clearly see, there can be two types of error in the judgment – Type 1 error, when the verdict is against the person while he was innocent and Type 2 error, when the verdict is in favour of the Person while he was guilty.

The basic concepts of Hypothesis Testing are actually quite analogous to this situation.

Steps to Perform for Hypothesis Testing

There are four steps to performing Hypothesis Testing:

Set the Hypothesis

Compute the test statistics

Make a decision

1. Set up Hypothesis (NULL and Alternate): Let us take the courtroom discussion further. The defendant is assumed to be innocent (i.e. innocent until proven guilty) and the burden is on a prosecutor to conduct a trial to show evidence that the defendant is not innocent. This is the Null Hypothesis.

Keep in mind that, the only reason we are testing the null hypothesis is that we think it is wrong. We state what we think is wrong about the null hypothesis in an Alternative Hypothesis.

In the courtroom example, the alternate hypothesis can be – the defendant is not guilty. The symbol for the alternative hypothesis is ‘H1’.

2. Set the level of Significance – To set the criteria for a decision, we state the level of significance for a test. It could 5%, 1% or 0.5%. Based on the level of significance, we make a decision to accept the Null or Alternate hypothesis.

Don’t worry if you didn’t understand this concept, we will be discussing it in the next section.

3. Computing Test Statistic – Test statistic helps to determine the likelihood. A higher probability has a higher likelihood and enough evidence to accept the Null hypothesis.

We’ll be looking into this step in later lessons.

4. Make a decision based on p-value – But What does this p-value indicate?

We can understand this p-value as the measurement of the Defense Attorney’s argument. If the p-value is less than ⍺ , we reject the Null Hypothesis or if the p-value is greater than ⍺, we fail to reject the Null Hypothesis.

Critical Value (p-value)

We will understand the logic of Hypothesis Testing with the graphical representation for Normal Distribution.

Typically, we set the Significance level at 10%, 5%, or 1%. If our test score lies in the Acceptance Zone we fail to reject the Null Hypothesis. If our test score lies in the critical zone, we reject the Null Hypothesis and accept the Alternate Hypothesis.

Critical Value is the cut off value between Acceptance Zone and Rejection Zone. We compare our test score to the critical value and if the test score is greater than the critical value, that means our test score lies in the Rejection Zone and we reject the Null Hypothesis. On the opposite side, if the test score is less than the Critical Value, that means the test score lies in the Acceptance Zone and we fail to reject the null Hypothesis.

But why do we need a p-value when we can reject/accept hypotheses based on test scores and critical values?

p-value has the benefit that we only need one value to make a decision about the hypothesis. We don’t need to compute two different values like critical values and test scores. Another benefit of using a p-value is that we can test at any desired level of significance by comparing this directly with the significance level.

This way we don’t need to compute test scores and critical values for each significance level. We can get the p-value and directly compare it with the significance level.

Directional Hypothesis

Great, You made it here! Hypothesis Testing is further divided into two parts –

Direction Hypothesis

Non-Direction Hypothesis

In the Directional Hypothesis, the null hypothesis is rejected if the test score is too large (for right-tailed and too small for left tailed). Thus, the rejection region for such a test consists of one part, which is right from the centre.

Non-Directional Hypothesis

In a Non-Directional Hypothesis test, the Null Hypothesis is rejected if the test score is either too small or too large. Thus, the rejection region for such a test consists of two parts: one on the left and one on the right.

What is Z test?

z tests are a statistical way of testing a hypothesis when either:

We know the population variance, or

We do not know the population variance but our sample size is large n ≥ 30

If we have a sample size of less than 30 and do not know the population variance, then we must use a t-test.

One-Sample Z test

We perform the One-Sample Z test when we want to compare a sample mean with the population mean.

Example:

Let’s say we need to determine if girls on average score higher than 600 in the exam. We have the information that the standard deviation for girls’ scores is 100. So, we collect the data of 20 girls by using random samples and record their marks. Finally, we also set our ⍺ value (significance level) to be 0.05.

In this example:

The mean Score for Girls is 641

The size of the sample is 20

The population mean is 600

The standard Deviation for the Population is 100

Since the P-value is less than 0.05, we can reject the null hypothesis and conclude based on our result that Girls on average scored higher than 600.

Two- Sample Z Test

We perform a Two-Sample Z test when we want to compare the mean of two samples.

Example:

Here, let’s say we want to know if Girls on average score 10 marks more than the boys. We have the information that the standard deviation for girls’ Scores is 100 and for boys’ scores is 90. Then we collect the data of 20 girls and 20 boys by using random samples and record their marks. Finally, we also set our ⍺ value (significance level) to be 0.05.

In this example:

The mean Score for Girls (Sample Mean) is 641

The mean Score for Boys (Sample Mean) is 613.3

The standard Deviation for the Population of Girls is 100

The standard deviation for the Population of Boys is 90

The Sample Size is 20 for both Girls and Boys

The difference between the Mean Population is 10

Thus, we can conclude based on the P-value that we fail to reject the Null Hypothesis. We don’t have enough evidence to conclude that girls on an average score of 10 marks more than the boys. Pretty simple, right?

What is a T-Test?

In simple words, t-tests are a statistical way of testing a hypothesis when:

We do not know the population variance

Our sample size is small, n < 30

One-Sample T-Test

We perform a One-Sample t-test when we want to compare a sample mean with the population mean. The difference from the Z Test is that we do not have the information on Population Variance here. We use the sample standard deviation instead of the population standard deviation in this case.

Eaxmple:

Let’s say we want to determine if on average girls score more than 600 in the exam. We do not have the information related to variance (or standard deviation) for girls’ scores. To a perform t-test, we randomly collect the data of 10 girls with their marks and choose our ⍺ value (significance level) to be 0.05 for Hypothesis Testing.

In this example:

The mean Score for Girls is 606.8

The size of the sample is 10

The population mean is 600

The standard deviation for the sample is 13.14

Our P-value is greater than 0.05 thus we fail to reject the null hypothesis and don’t have enough evidence to support the hypothesis that on average, girls score more than 600 in the exam.

Two-Sample T-Test

We perform a Two-Sample t-test when we want to compare the mean of two samples.

Example:

Here, let’s say we want to determine if on average, boys score 15 marks more than girls in the exam. We do not have the information related to variance (or standard deviation) for girls’ scores or boys’ scores. To perform a t-test. we randomly collect the data of 10 girls and boys with their marks. We choose our ⍺ value (significance level) to be 0.05 as the criteria for Hypothesis Testing.

In this example:

The mean Score for Boys is 630.1

The mean Score for Girls is 606.8

Difference between Population Mean 15

The standard Deviation for Boys’ scores is 13.42

The standard Deviation for Girls’ scores is 13.14

Thus, P-value is less than 0.05 so we can reject the null hypothesis and conclude that on average boys score 15 marks more than girls in the exam.

Deciding between Z Test and T-Test

So when we should perform the Z test and when we should perform the t-Test? It’s a key question we need to answer if we want to master statistics.

If the sample size is large enough, then the Z test and t-Test will conclude with the same results. For a large sample size Sample Variance will be a better estimate of Population variance so even if population variance is unknown, we can use the Z test using sample variance.

Similarly, for a  Large Sample, we have a high degree of freedom. And since t-distribution approaches the normal distribution, the difference between the z score and t score is negligible.

Conclusion

In this article, we learn about a few important techniques to solve the real problem such as:-

what is hypothesis testing?

steps to perform for hypothesis testing

p-value

directional hypothesis

Non- directional hypothesis

what is Z-test?

One-sample Z-test with example

Two-sample Z-test with example

what is a t-test?

One-sample t-test with example

Two-sample t-test with example

If you want to read my previous blogs, you can read Previous Data Science Blog posts from here.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

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Crucial Data Analytics Lessons That Came With The Pandemic

Data analytics came as a boon to businesses when they were sitting hand-on-head during the beginning of the pandemic. Data analytics helped organizations sieve through tons of data to get insightful information that helped them understand the changed consumer wants. But the on-going COVID-19 pandemic taught some data lesions that are practical and provocative, ranging from the importance of trust, collaboration, and addressing the limitations and misinformation.

The Teachings Of The Pandemic

Data Points Represent People The logic is simple, data is generated by people. So, the lesson here is to think about what good practitioners can do through data and the unintended consequences of the published data at a policy level decision.   Data that is used to inform broad public decisions like health and safety measures should be treated with caution than normal public datasets Wrong representation of data can minimize the intensity of the information and influence their decisions around important regulations. A common example for this is what is happening around the vaccine numbers. By sharing misinformation about the vaccine numbers, people responsible are creating a problem by encouraging people to take up vaccines while having supply issues.   Data can show the true picture of the intensity of a tragedy COVID-19 showed the true power of data visualization, not on screen but via symbolic representations like candles lit for every life lost or flags meant for social distancing. While data on screen was there, nothing came close to the visual representation and that is the takeaway. Though a data analyst has the numbers, the understanding of those numbers will only come via proper representation.   Bias and inequalities in data shouldn’t be tucked away In the US, COVID-19 data is represented at national, state, and district levels, but it took a lot of months for states to release data race-wise. States were insisted to do so because indigineous, Black, and Hispianic communities constitute the essential workers group who were under the risk. The data then showed inequalities in the impact faced by privileged people who had the liberty to work from home and those who had to be on the line daily. Only when analysts don’t hide these inequalities in data, people work on understanding the cause and come with a remedy.   Don’t Rely On Just One Data Source Lots of reports saw light during the initial days of COVID-19 regarding positive cases, hospitalizations, and deaths. And towards the end of 2023, different reports came out talking about the mortality and recovery rate which, when compared, showed a complete picture of the impact. This implies that one shouldn’t trust data from one instance. Keeping in mind data’s dynamic behaviour, results should only be judged after a thorough collection. Data transparency matters. While the world is grappling with challenges about the case counts and bias, a lot of mistrust is being created. To make the right data more accessible, the above mentioned issues should be fixed.  

Data analytics came as a boon to businesses when they were sitting hand-on-head during the beginning of the pandemic. Data analytics helped organizations sieve through tons of data to get insightful information that helped them understand the changed consumer wants. But the on-going COVID-19 pandemic taught some data lesions that are practical and provocative, ranging from the importance of trust, collaboration, and addressing the limitations and chúng tôi logic is simple, data is generated by people. So, the lesson here is to think about what good practitioners can do through data and the unintended consequences of the published data at a policy level decision.Wrong representation of data can minimize the intensity of the information and influence their decisions around important regulations. A common example for this is what is happening around the vaccine numbers. By sharing misinformation about the vaccine numbers, people responsible are creating a problem by encouraging people to take up vaccines while having supply issues.COVID-19 showed the true power of data visualization, not on screen but via symbolic representations like candles lit for every life lost or flags meant for social distancing. While data on screen was there, nothing came close to the visual representation and that is the takeaway. Though a data analyst has the numbers, the understanding of those numbers will only come via proper chúng tôi the US, COVID-19 data is represented at national, state, and district levels, but it took a lot of months for states to release data race-wise. States were insisted to do so because indigineous, Black, and Hispianic communities constitute the essential workers group who were under the risk. The data then showed inequalities in the impact faced by privileged people who had the liberty to work from home and those who had to be on the line daily. Only when analysts don’t hide these inequalities in data, people work on understanding the cause and come with a chúng tôi of reports saw light during the initial days of COVID-19 regarding positive cases, hospitalizations, and deaths. And towards the end of 2023, different reports came out talking about the mortality and recovery rate which, when compared, showed a complete picture of the impact. This implies that one shouldn’t trust data from one instance. Keeping in mind data’s dynamic behaviour, results should only be judged after a thorough collection. Data transparency matters. While the world is grappling with challenges about the case counts and bias, a lot of mistrust is being created. To make the right data more accessible, the above mentioned issues should be fixed.

Top 10 Key Ai And Data Analytics Trends For 2023

Transacting has changed dramatically due to the global pandemic. E-commerce, cloud computing and enhanced cybersecurity measures are all part of the global trend assessment for data analysis.

Businesses have always had to consider how to manage risk and keep costs low. Any company that wants to be competitive must have access to machine learning technology that can effectively analyze data.

Why trends are important for model creators?

The industry’s top data analysis trends for 2023 should give our creators an idea of where it is headed.

Creators can make their work more valuable by staying on top of data science trends and adapting their models to current standards. These data analysis trends can inspire you to create new models or update existing ones.

AI is the creator economy: Think Airbnb for AI artifacts

Similar to the trend in computer gaming where user-generated content (UGC), was monetized as a part of gaming platforms, so we expect similar monetization in data science. These models include simple ones like classification, regression, and clustering.

They are then repurposed and uploaded onto dedicated platforms. These models are then available to business users worldwide who wish to automate their everyday business processes and data.

These will quickly be followed by deep-model artifacts such as convents and GAN’s and autoencoders which are tuned to solve business problems. These models are intended to be used by commercial analysts and not teams of data scientists.

It is not unusual for data scientists to sell their expertise and experience through consulting gigs or by uploading models into code repositories.

These skills will be monetized through two-sided marketplaces in 2023, which allow a single model to access a global marketplace.

For AI, think Airbnb.

The future of environmental AI is now in your mind

While most research is focused on pushing the limits of complexity, it is clear that complex models and training can have a significant impact on the environment.

Data centers are predicted to account for 15% of global CO2 emissions in 2040. A 2023 paper entitled “Energy considerations For Deep Learning” found that the training of a natural language translator model produced CO2 levels equal to four-family cars. It is clear that the more training you receive, the more CO2 you release.

Organizations are looking for ways to reduce their carbon footprint, as they have a better understanding of the environmental impact.

While AI can be used to improve the efficiency of data centers, it is expected that there will be more interest in simple models for specific problems.

In reality, why would we need a 10-layer convolutional neural net when a simple Bayesian model can perform equally well and requires significantly less data, training, or compute power?

As environmental AI creators strive to build simple, cost-effective models that are usable and efficient, “Model Efficiency” will be a common term.

Hyper-parameterized models become the superyachts of big tech

The number of parameters in the largest models has increased from 94M parameters in 2023 to an astonishing 1.6 Trillion in 2023 in just three years. This is because Google, Facebook, and Microsoft push the limits of complexity.

These trillions of parameters can be language-based today, which allows data scientists to create models that understand language in detail.

This allows models to write articles, reports, and translations at a human level. They are able to write code, create recipes, and understand irony and sarcasm in context.

Vision models that are capable of recognizing images with minimal data will be able to deliver similar human-level performance in 2023 and beyond. You can show a toddler chocolate bar once and they will recognize it every time they see it.

These models are being used by creators to address specific needs. Dungeon. AI is a games developer who has created a series of fantasy games that are based on the 1970’s Dungeons and Dragons craze.

These realistic worlds were created using the GPT-3 175 billion parameter model. As models are used to understand legal text, write copy campaigns or categorize images and video into certain groups, we expect to see more of these activities from creators.

Top 10 Key AI and Data Analytics Trends 1. A digitally enhanced workforce of co-workers

Businesses around the globe are increasingly adopting cognitive technologies and machine-learning models. The days of ineffective admin and assigning tedious tasks to employees are rapidly disappearing.

Businesses are now opting to use an augmented workforce model, which sees humans and robotics working together. This technological breakthrough makes it easier for work to be scaled and prioritized, allowing humans to concentrate on the customer first.

While creating an augmented workforce is definitely something creators should keep track of, it is difficult to deploy the right AI and deal with the teething issues that come along with automation.

Moreover, workers are reluctant to join the automation bandwagon when they see statistics that predict that robots will replace one-third of all jobs by 2025.

While these concerns may be valid to a certain extent, there is a well-founded belief machine learning and automation will only improve the lives of employees by allowing them to take crucial decisions faster and more confidently.

An augmented workforce, despite its potential downsides, allows individuals to spend more time on customer care and quality assurance while simultaneously solving complex business issues as they arise.

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2. Increased Cybersecurity

Since most businesses were forced to invest in increased online presence due to the pandemics, cybersecurity is one of the top data analysis trends going into 2023.

One cyber-attack can cause a company to go out of business. But how can companies avoid being entangled in a costly and time-consuming process that could lead to a complete failure? This burning question can be answered by excellent modeling and a dedication to understanding risk.

AI’s ability analyzes data quickly and accurately makes it possible to increase risk modeling and threat perception.

Machine learning models are able to process data quickly and provide insights that help keep threats under control. IBM’s analysis of AI in cybersecurity shows that this technology can gather insights about everything, from malicious files to unfavorable addresses.

This allows businesses to respond to security threats up to 60 percent faster. Businesses should not overlook investing in cybersecurity modeling, as the average cost savings from containing a breach amounts to $1.12 million.

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3. Low-code and no-code AI

Because there are so few data scientists on the global scene, it is important that non-experts can create useful applications using predefined components. This makes low-code or no-code AI one the most democratic trends in the industry.

This approach to AI is essentially very simple and requires no programming. It allows anyone to “tailor applications according to their needs using simple building blocks.”

Recent trends show that the job market for data scientists and engineers is extremely favorable.

LinkedIn’s new job report claims that around 150,000,000 global tech jobs will be created within the next five years. This is not news, considering that AI is a key factor in businesses’ ability to stay relevant.

The current environment is not able to meet the demand for AI-related services. Furthermore, more than 60% of AI’s best talent is being nabbed in the finance and technology sectors. This leaves few opportunities for employees to be available in other industries.

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4. The Rise of the Cloud

Cloud computing has been a key trend in data analysis since the pandemic. Businesses around the globe have quickly adopted the cloud to share and manage digital services, as they now have more data than ever before.

Machine learning platforms increase data bandwidth requirements, but the rise in the cloud makes it possible for companies to do work faster and with greater visibility.

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5. Small Data and Scalable AI

The ability to build scalable AI from large datasets has never been more crucial as the world becomes more connected.

While big data is essential for building effective AI models, small data can add value to customer analysis. While big data is still valuable, it’s nearly impossible to identify meaningful trends in large datasets.

Small data, as you might guess from its name contains a limited number of data types. They contain enough information to measure patterns, but not too much to overwhelm companies.

Marketers can use small data to gain insights from specific cases and then translate these findings into higher sales by personalization.

6. Improved Data Provenance

Boris Glavic defines data provenance as “information about data’s origin and creation process.”  Data provenance is one trend in data science that helps to keep data reliable.

Poor data management and forecasting errors can have a devastating impact on businesses. However, improvements in machine learning models have made this a less common problem.

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7. Migration to Python and Tools

Python, a high-level programming language with a simple syntax and language, is revolutionizing the tech industry by providing a more user-friendly way to code.

While R will not disappear from data science any time soon, Python can be used by global businesses because it places a high value on logical code and understandability. Python, unlike R, is primarily used for statistical computing.

However, it can be easily deployed for machine learning because it analyzes and collects data at a deeper level than R.

The use of Python in scalable production environments can give data analysts an edge in the industry. This trend in data science should not be overlooked by budding creators.

8. Deep Learning and Automation

Deep learning is closely related to machine learning, but its algorithms are inspired from the neural pathways of the human brain. This technology is beneficial for businesses as it allows them to make accurate predictions and create useful models that are easy to understand.

Deep learning may not be appropriate for all industries, but the neural networks in this subfield allow for automation and high levels of analysis without any human intervention.

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9. Real-time data

Real-time data is also one of the most important data analysis trends. It eliminates the cost associated with traditional, on-premises reporting.

10. Moving beyond DataOps to XOps

Manual processing is no longer an option with so many data at our disposal in modern times.

DataOps can be efficient in gathering and assessing data. However, XOps will become a major trend in data analytics for next year. Gartner supports this assertion by stating that XOps is an efficient way to combine different data processes to create a cutting-edge approach in data science.

DataOps may be a term you are familiar with, but if this is a new term to you, we will explain it.

Salt Project’s data management experts say that XOps is a “catch all, umbrella term” to describe the generalized operations and responsibilities of all IT disciplines.

This encompasses DataOps and MLOps as well as ModelOps and AIOps. It provides a multi-pronged approach to boost efficiency and automation and reduce development cycles in many industries.

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What are the key trends in data analysis for the future?

Data science trends for 2023 look amazing and show that businesses are more valuable than ever with accurate and easily digestible data.

Data analysis trends will not be static, however, because the volume of data available to businesses keeps growing, so data analysis trends will never stop evolving. It is therefore difficult to find effective data processing methods that work across all industries.

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