# Trending February 2024 # Hypothesis Testing For Data Science And Analytics # Suggested March 2024 # Top 3 Popular

You are reading the article Hypothesis Testing For Data Science And Analytics updated in February 2024 on the website Bellydancehcm.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 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.

Related

You're reading Hypothesis Testing For Data Science And Analytics

## Your Guide To Master Hypothesis Testing In Statistics

Introduction – the difference in mindset

I started my career as a MIS professional and then made my way into Business Intelligence (BI) followed by Business Analytics, Statistical modeling and more recently machine learning. Each of these transition has required me to do a change in mind set on how to look at the data.

But, one instance sticks out in all these transitions. This was when I was working as a BI  professional creating management dashboards and reports. Due to some internal structural changes in the Organization I was working with, our team had to start reporting to a team of Business Analysts (BA). At that time, I had very little appreciation of what is Business analytics and how is it different from BI.

In today’s article, I will explain hypothesis testing and reading statistical significance to differentiate signal from the noise in data – exactly what my new manager wanted me to do!

P.S. This might seem like a lengthy article, but would be one of the most useful one, if you follow through.

A case study:

Let us say that average marks in mathematics of class 8th students of ABC School is 85. On the other hand, if we randomly select 30 students and calculate their average score, their average comes to be 95. What can be concluded from this experiment? It’s simple. Here are the conclusions:

These 30 students are different from ABC School’s class 8th students, hence their average score is better i.e behavior of these randomly selected 30 students sample is different from the population (all ABC School’s class 8th students) or these are two different population.

There is no difference at all. The result is due to random chance only i.e. we found the average value of 85. It could have been higher / lower than 85 since there are students having average score less or more than 85.

How should we decide which explanation is correct? There are various methods to help you to decide this. Here are some options:

Increase sample size

Test for another samples

Calculate random chance probability

The first two methods require more time & budget. Hence, aren’t desirable when time or budget are constraints.

So, in such cases, a convenient method is to calculate the random chance probability for that sample i.e. what is the probability that sample would have average score of 95?. It will help you to draw a conclusion from the given two hypothesis given above.

Now the question is, “How should we calculate the random chance probability?“.

To answer it, we should first review the basic understanding of statistics.

Basics of Statistics

As I discussed, these methods always work with normal distribution (shown above) only, not with other distributions. In case, the population distribution is not normal, we’d resort to Central Limit Theorem.

2. Central Limit Theorem: This is an important theorem in statistics. Without going into definitions, I’ll explain it using an example . Let’s look at the case below. Here, we have a data of 1000 students of 10th standard with their total marks. Following are the derived key metrics of this population:

Is this some kind of distribution you can recall? Probably not. These marks have been randomly distributed to all the students.

Now, let’s take a sample of 40 students from this population. So, how many samples can we take from this population? We can take 25 samples(1000/40 = 25). Can you say that every sample will have the same average marks as population has (48.4)? Ideally, it is desirable but practically every sample is unlikely to have the same average.

Does this distribution looks like the one we studied above? Yes, this table is also normally distributed. For better understanding, you can download this file from here and while doing this exercise you’ll come across the findings stated below:

1. Mean of sample means (1000 sample means) is very close to population mean

2. Standard deviation of the sample distribution can be found out from the population standard deviation divided by square root of sample size N and it is also known as standard error of means.

3. The distribution of sample means is normal regardless of the distribution of the actual population. This is known as Central Limit theorem. This can be very powerful. In our initial example of ABC School students, we compared the sample mean and population mean. Precisely, we looked at the distribution of sample mean and found out the distance between population mean and the sample mean. In such cases, you can always use a normal distribution without worrying about the population distribution.

Now, let’s say we have calculated the random chance probability. It comes out to be 40%, then should I go with first conclusion or other one ? Here the “Significance Level” will help us to decide.

What is Significance Level?

We have taken an assumption that probability of sample mean 95 is 40%, which is high i.e. more likely that we can say that there is a greater chance that this has occurred due to randomness and not due to behavior difference.

Now, how do we decide what is high probability and what is low probability?

To be honest, it is quite subjective in nature. There could be some business scenarios where 90% is considered to be high probability and in other scenarios could be 99%. In general, across all domains, cut off of 5% is accepted. This 5% is called Significance Level also known as alpha level (symbolized as α). It means that if random chance probability is less than 5% then we can conclude that there is difference in behavior of two different population. (1- Significance level) is also known as Confidence Level i.e. we can say that I am 95% confident that it is not driven by randomness.

Till now, we looked at the tools to test a hypothesis, whether sample mean is different from population or it is due to random chance. Now, let’s look at the steps to perform a hypothesis test and post that we will go through it using an example.

What are the steps to perform Hypothesis Testing?

Set up Hypothesis (NULL and Alternate):  In ABC School example, we actually tested a hypothesis. The hypothesis, we are testing was the difference between sample and population mean was due to a random chance. It is called as “NULL Hypothesis” i.e. there is no difference between sample and population. The symbol for the null hypothesis is ‘H0’. Keep in mind that, the only reason we are testing the null hypothesis is because we think it is wrong. We state what we think is wrong about the null hypothesis in an Alternative Hypothesis. For the ABC School example, alternate hypothesis is, there is a significant difference in behavior of sample and population. The symbol for the alternative hypothesis is ‘H1’. In a courtroom, since the defendant is assumed to be innocent (this is the null hypothesis so to speak), the burden is on a prosecutor to conduct a trial to show evidence that the defendant is not innocent. In a similar way, we assume the null hypothesis is true, placing the burden on the researcher to conduct a study to show evidence that the null hypothesis is unlikely to be true.

Set the Criteria for  decision: 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. There could be 0.03 probability which accepts Null hypothesis on 1% level of significance but rejects Null hypothesis on 5% of significance. It is based on business requirements.

Compute the random chance of probability: Random chance probability/ Test statistic helps to determine the likelihood. Higher probability has higher likelihood and enough evidence to accept the Null hypothesis.

4. The decision to reject the null hypothesis could be incorrect, it is known as Type I error.

Example

Blood glucose levels for obese patients have a mean of 100 with a standard deviation of 15. A researcher thinks that a diet high in raw cornstarch will have a positive effect on blood glucose levels. A sample of 36 patients who have tried the raw cornstarch diet have a mean glucose level of 108. Test the hypothesis that the raw cornstarch had an effect or not.

Solution:- Follow the above discussed steps to test this hypothesis:

Step-1: State the hypotheses. The population mean is 100.

Step-2: Set up the significance level. It is not given in the problem so let’s assume it as 5% (0.05).

Step-3: Compute the random chance probability using z score and z-table.

For this set of data: z= (108-100) / (15/√36)=3.20

You can look at the probability by looking at z- table and p-value associated with 3.20 is 0.9993 i.e. probability of having value less than 108 is 0.9993 and more than or equals to 108 is (1-0.9993)=0.0007.

Step-4: It is less than 0.05 so we will reject the Null hypothesis i.e. there is raw cornstarch effect.

Note: Setting significance level can also be done using z-value known as critical value. Find out the z- value of 5% probability and it is 1.65 (positive or negative, in any direction). Now we can compare calculated z-value with critical value to make a decision.

Directional/ Non Directional Hypothesis Testing

In previous example, our Null hypothesis was, there is no difference i.e. mean is 100 and alternate hypothesis was sample mean is greater than 100. But, we could also set an alternate hypothesis as sample mean is not equals to 100. This becomes important when we do reject the Null hypothesis, should we go with which alternate hypothesis:

Sample mean is greater than 100

Sample mean is not equals to 100 i.e. there is a difference

Here, the question is “Which alternate hypothesis is more suitable?”. There are certain points which will help you to decide which alternate hypothesis is suitable.

You are not interested in testing sample mean lower than 100, you only want to test the greater value

You have strong believe that Impact of raw cornstarch is greater

In above two cases, we will go with One tail test. In one tail test, our alternate hypothesis is greater or less than the observed mean so it is also known as Directional Hypothesis test. On the other hand, if you don’t know whether the impact of test is greater or lower then we go with Two tail test also known as Non Directional Hypothesis test.

Let’s say one of research organization is coming up with new method of teaching. They want to test the impact of this method. But, they are not aware that it has positive or negative impact. In such cases, we should go with two tailed test.

In one tail test, we reject the Null hypothesis if the sample mean is either positive or negative extreme any one of them. But, in case of two tail test we can reject the Null hypothesis in any direction (positive or negative).

Look at the image above. Two-tailed test allots half of your alpha to testing the statistical significance in one direction and half of your alpha in the other direction. This means that .025 is in each tail of the distribution of your test statistic. Why are we saying 0.025 on both side because normal distribution is symmetric. Now we come to a conclusion that Rejection criteria for Null hypothesis in two tailed test is 0.025 and it is lower than 0.05 i.e. two tail test has more strict criteria to reject the Null Hypothesis.

Example

Templer and Tomeo (2002) reported that the population mean score on the quantitative portion of the Graduate Record Examination (GRE) General Test for students taking the exam between 1994 and 1997 was 558 ± 139 (μ ± σ). Suppose we select a sample of 100 participants (n = 100). We record a sample mean equal to 585 (M = 585). Compute the p-value t0 check whether or not we will retain the null hypothesis (μ = 558) at 0.05 level of significance (α = .05).

Solution:

Step-1: State the hypotheses. The population mean is 558.

H1: μ ≠ 558 (two tail test)

Step-2: Set up the significance level. As stated in the question, it as 5% (0.05). In a non-directional two-tailed test, we divide the alpha value in half so that an equal proportion of area is placed in the upper and lower tail. So, the significance level on either side is calculated as: α/2 = 0.025. and z score associated with this (1-0.025=0.975) is 1.96. As this is a two-tailed test, z-score(observed) which is less than -1.96 or greater than 1.96 is a evidence to reject the Null hypothesis.

Step-3: Compute the random chance probability or  z score

For this set of data: z= (585-558) / (139/√100)=1.94

You can look at the probability by looking at z- table and p-value associated with 1.94 is 0.9738 i.e. probability of having value less than 585 is 0.9738 and more than or equals to 585 is (1-0.9738)=0.03

Step-4: Here, to make a decision, we compare the obtained z value to the critical values (+/- 1.96). We reject the null hypothesis if the obtained value exceeds a critical values. Here obtained value (Zobt= 1.94) is less than the critical value. It does not fall in the rejection region. The decision is to retain the null hypothesis.

End Notes

In this article, we have looked at the complete process of undertaking hypothesis testing during predictive modeling. Initially, we looked at the concept of hypothesis followed by the types of hypothesis and way to validate hypothesis to make an informed decision. We also have also looked at important concepts of hypothesis testing like Z-value, Z-table, P-value, Central Limit theorem.

As mentioned in the introduction, this was one of the most difficult change in mindset for me when I read this first time. But it was also one of the most helpful and significant change. I can easily say that this change started me to think like a predictive modeler.

In next article, we will look at the what-if scenarios with hypothesis testing like:

If sample size is less than 30 (Not satisfy CLT)

Compare two sample rather than sample and population

If we don’t know the population standard deviation

p-values and Z-scores in the Big Data age

Related

## 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.

Also read: The Five Best Free Cattle Record Keeping Apps & Software For Farmers/Ranchers/Cattle Owners

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.

Also read: 10 Best Chrome Extensions For 2023

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.

Also read: 10 Best Android Development Tools that Every Developer should know

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.

Also read: No Plan? Sitting Ideal…No Problem! 50+ Cool Websites To Visit

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.

Also read: Best Online Courses to get highest paid in 2023

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.

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.

Also read: How to Start An E-commerce Business From Scratch in 2023

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.

## Top 10 Big Data Analytics Trends And Predictions For 2023

These trends in big data will prepare you for the future.

Big data and analytics (BDA) is a crucial resource for public and private enterprises nowadays, as well as for healthcare institutions in battling the COVID-19 pandemic. Thanks in large part to the evolution of cloud software, organizations can now track and analyze volumes of business data in real-time and make the necessary adjustments to their business processes accordingly.

AI will continue to improve, but humans will remain crucial

Earlier this year, Gartner® stated “Smarter, more responsible, scalable AI will enable better learning algorithms, interpretable systems and shorter time to value. Organizations will begin to require a lot more from AI systems, and they’ll need to figure out how to scale the technologies — something that up to this point has been challenging.” While AI is likely to continue to develop, we aren’t yet near the point where it can do what humans can. Organizations will still need data analytics tools that empower their people to spot anomalies and threats in an efficient manner.

According to Dresner’s business intelligence market study 2023, organizations in the technology, business services, consumer services, and manufacturing industries are reporting the highest increases in planned adoption of business intelligence tools in 2023.

Predictive analytics is on the rise

Organizations are using predictive analytics to forecast potential future trends. According to a report published by Facts & Factors, the global predictive analytics market is growing at a CAGR of around 24.5% and is expected to reach \$22.1 billion by the end of 2026.

Cloud-native analytics solutions will be necessary Self-service analytics will become even more critical to business intelligence

The demand for more fact-based daily decision-making is driving companies to seek self-service data analytics solutions. Jim Ericson, research director at Dresner Advisory Services, recently observed, “Organizations that are more successful with BI are universally more likely to use self-service BI capabilities including collaboration and governance features included in BI tools.” In 2023, more companies will adopt truly self-service tools that allow non-technical business users to securely access and glean insights from data.

The global business intelligence market will be valued at \$30.9 billion by 2023

According to research by Beroe, Inc., a leading provider of procurement intelligence, the global business intelligence market is estimated to reach \$30.9 billion by 2023. and key drivers include big data analytics, demand for data-as-a-service, demand for personalized, self-servicing BI capabilities.

60% of organizations report company culture as being their biggest obstacle to success with business intelligence

Dresner’s business intelligence market study 2023 revealed that the most significant obstacle to success with business intelligence is “a culture that doesn’t fully understand or value fact-based decision-making.” 60% of respondents reported this factor as most damaging.

Retail/wholesale, financial services, and technology organizations are increasing their BI budgets by over 50% in 2023

Retail/wholesale, financial services, and technology organizations are the top industries increasing their investment in business intelligence. Each of these industries is planning to increase budgets for business intelligence by over 50%, according to Dresner’s business intelligence market study 2023.

63% of companies say that improved efficiency is the top benefit of data analytics, while 57% say more effective decision-making

Finances online report that organizations identify improved efficiency and more effective decision-making as the top two benefits of using data analytics.

The global big data analytics in the retail market generated \$4.85 billion in 2023 and is estimated to increase to \$25.56 billion by 2028, with a CAGR of 23.1% from 2023 to 2028

## Can Java Be Used For Machine Learning And Data Science?

The world is drooling over

Top Expertise to Develop For Machine Learning & Data Science

If you want to excel in any field, you first need to develop the skills. Here’s a list of all the skills required if you’re going to learn ML & data science. Math: It is all about permutations and combination complemented with your calculation ability to be able to link yourself with machines. Data Architecture: To be able to reach the core of any technology, you must have a broad idea of the data formats. Software Structures: There is no ML without software, and a data engineer should be clear with concepts related to software and their working. Programming & Languages: If you do not know anything about this, there is no ML for you. Programming languages are the essential requirement for one to be able to build a career in ML. Differencing and Data Mining: If you have no clue about data, you are a zero. To be able to learn ML, data mining, and the ability to infer the information is crucial.

Java: Machine Learning & Data Science’s Future

Java is a technology that proves beneficial in varied arrays of development and ML. One of the critical things in ML & Data Science is algorithms. With Java’s available resources, one can efficiently work in various algorithms and even develop them. It is a scalable language with many frameworks and libraries. In the current scenario, Java is amongst the most prominent languages in AI and ML. Some of the reasons why Java is an excellent alternative for a future in Data Science, Machine Learning, and finally, Artificial Intelligence are:

Pace of Execution

If you are arguing about the speed of coding and execution, Java takes the lead in it, which means faster ML & DS technologies. Its features of statically typing and compilation are what makes it super in execution. With a lesser run time than any other language, knowing Java means you are good to go in the ML industry.

Coding

Indentation in Java is not a must which makes it easier than Python or R. Also, coding in Java may require more lines, but it is easier than in other languages. If you are well-versed with coding, Java will be beneficial in ML and DS.

Learning Curve

Java has a lot of areas where one must work hard. The learning curve for Java and allied language is quicker and more comfortable than other languages in totality. Suppose you know a language better and efficiently. In that case, it means that you can enter the domain at a more accelerated pace than through any other language whose learning curve is typical of Java.

Salary Packages

Java has been in use for 30+ years. The future salaries of people who know Java are perceived to be higher than through any other language. We are not saying that you might not have a handsome amount in your hand if one knows Python. Instead, we are just focusing that with Java’s legacy in place, the salaries you get in your growth years are expected to be more for people who know Java.

Community

Java will complete three decades of existence and is still one of the most prevalent and popularized languages. It means that numerous people in the enterprise know the language and will provide you with support in requirements. Several people in DS and ML are working through Java. It is an additional benefit that you can avail of if you learn ML and DS with Java.

Varied Libraries

With Java, you have access to various libraries in Java for learning ML. To name a few, there are ADAMS, Mahaut, JavaML, WEKA, Deeplearning4j, etc.

## Flask Python Tutorial For Data Science Professionals

As a Data Science Enthusiast, Machine Learning Engineer, or data science practitioner, it’s not just about creating a machine learning model for a specific problem. Presenting your solution to the audience or clients is equally important, as your goal is to impact society. Deploying your solution to the cloud requires the assistance of a web framework, and Flask Python is one such micro web framework that simplifies the process.

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

What is Web-Framework, and Micro Web-Framework?

A web application framework is a package of libraries and modules that simplifies the development process by handling protocol details and application maintenance. Python Django is an example of a traditional web framework, an enterprise framework.

On the other hand, a micro-framework offers developers more flexibility and freedom. Unlike traditional frameworks, micro-frameworks do not require extensive setup and are commonly used for small web application development. This approach saves time and reduces maintenance costs.

Flask, a web framework written in Python, enables developers to quickly and rapidly develop web applications, configuring the backend and front end seamlessly. It grants developers complete control over data access and is built on Werkzeug’s WSGI toolkit and Jinja templating engine. Flask offers several key features, including:

Simplified REST API development: Flask streamlines the creation of REST APIs by providing convenient libraries, tools, and modules for handling user requests, routing, sessions, form validation, and more.

Versatility for various projects: Flask utilizes various applications, such as blog websites, commercial websites, and other web-based projects.

Minimal boilerplate code: Flask eliminates the need for excessive boilerplate code, allowing developers to focus on the core functionality of their applications.

Lightweight and essential components: Flask is a micro-framework that offers only essential components, allowing developers to implement additional functionalities through separate modules or extensions.

Extensibility through Flask extensions: Flask boasts a vast ecosystem of extensions that can be seamlessly integrated to enhance its functionalities, providing developers with flexibility and the ability to expand its capabilities.

Maintain the following folder structure:

Create a folder named “templates”: This folder will store your HTML files. Place all the HTML templates used in your Flask application inside this folder.

Create a folder named “static”: This folder will contain CSS, JavaScript, and any additional images you utilize in your application.

Note: The pickle file in the image may not be present in your current setup. It will generate when you implement the Flask application. For now, focus on creating the three components mentioned above: the templates folder, the static folder, and the main Flask file.

Following this standard structure, you can easily organize and maintain your Flask application, ensuring smooth execution and better collaboration with other developers.

Checkout: Develop and Deploy Image Classifier using Flask: Part 1 & Part 2

Key Aspects of Flask: WSGI and Jinja2

Everywhere it is said and written that Flask is WSGI compliant or Flask uses Jinja templating. But what is the actual meaning of these terms and what significance does this play in the flask development lifecycle? Let’s explore one by one each of two terminology.

What is Web Server Gateway Interface(WSGI)?

WSGI is a standard that describes the specifications concerning the communication between a client application and a web server. The benefit of using WSGI is that it helps in the scalability of applications with an increase in traffic, maintains efficiency in terms of speed, and maintains the flexibility of components.

What is Jinja2?

Template means frontend application designed using HTML, CSS, and whose content is displayed to a user in an interactive way. Flask helps to render the web page for the server with some specified custom input. In simple words, Flask helps connect your backend workflow with the frontend part and act as client-side scripting means It helps you to access the data that the user provides on frontend designed application and process the inputs by passing values to backend application and again rendering the output to HTML content is the task of Jinja templating.

Jinja2 has vast functionalities like a template inheritance which means when you create multiple templates (pages) for the application then some code or design is the same on each page so you do not need to write it again. It can be inherited from another template.

Now you have a good understanding of the theory of flask. let us enhance our understanding by making our hands dirty while trying something to implement using a flask.

It is good to create a new virtual environment if you start working on any new project. In your python working directory from anaconda prompt or command prompt create a new environment using the below code.

Now the first thing is to install a flask. use the pip command to install Flask.

Write the below code in created python app file and run it from the command line in a working directory using the below code.

from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): return 'Hello World’ if __name__ == '__main__': app.run()

There are parameters that can be defined in the app run function. the run function basically runs the application on a local development server.

app.run(host, port, debug, options)

host – It defines that on what hostname to listen to. we are running at localhost(default is 127.0.0.1)

Port – on which port to call the application. The default port is 5000.

options – The options are forwarded to the werkzeug server.

The route is a decorator in a python flask. It basically tells the application which function to be run or on which URL the user should be rendered. in a route function, the escape sequence describes the URL. The function after defining the route is created and you can pass parameters as a normal python function.

Flask support dynamic routing as well. you can modify the URL or while rendering you can put various conditions with custom data to send.

HTTP Methods

HTTP methods are the core communication block between various parties on the worldwide web. It helps to get, send, cached data from different websites or files. let us explore the different HTTP methods that Flask support and which method is used for what purpose.

1. GET

It is the most basic form of sending data to websites by concatenating the content to URL. The GET method is most commonly used to fetch the data from files or load a new HTML page. It can be used where you send the non-confidential data which if disclosed is not an issue.

2. POST

POST method is the most used method after the GET request. It is used to send the data to a server using encryption. The data is not appended to URL, it is sent and displayed in a body of HTML using jinja python templating. The POST method is mostly used when we are working with forms to send receive user data and after processing sending output back to display in HTML body.

POST method is the most-trusted method and is used to send confidential data like login credentials.

The head method is similar to the Get method but it can be cached on the system. The passed data is unencrypted and it must have a response. Suppose if some URL requests for a large file download, now by using HEAD method URL can request to get file size.

4. PUT

The PUT method is similar to the POST method. The only difference lies in when we call POST request multiple times then that many time request is made, and in PUT method it opposes the multiple requests and replaces the new request with an old response.

5. DELETE

delete is a simple HTTP method that is used to delete some particular resource to a server.

Now we have a practical and basic understanding of the flask framework works. Now you must be wondering how can deploy our machine learning model using flask so that the Public can use it and provide new data. So, it is a simple task where the inputs(best features you have chosen) your machine learning model requires is taken from a user in form of HTML form or flask form, and using the flask GET method you access data at the backend. After providing the user data to the model and model gives you an output. Using POST request you render the output to an HTML page and give it to a user. The process is simple and works really very fast. let us implement this on a dataset.

Problem Statement

We are using a Healthcare expense dataset from Kaggle. You can find details and the dataset here. The data basically aims to predict the individual healthcare expenses given age, family details, BMI, gender. The particular dataset is chosen because it contains different input variables so you will learn how to access different inputs from the front end using a flask. Our main aim here is not for implementing any generalized machine learning model. our main aim is to understand the development of flask applications for any machine learning model.

Prepare Machine Learning Model

Before implementing the Flask application it is important to have a machine learning model in any form like pickle, h5, etc. so lets us load the data, preprocess the data, and train a linear regression algorithm on it. After modeling, we will save the model using the pickle module. The code snippet of complete model preparation is given below.

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.linear_model import LinearRegression import pickle data = pd.read_csv("/kaggle/input/insurance/insurance.csv") le = LabelEncoder() le.fit(data['sex']) data['Sex'] = le.transform(data['sex']) le.fit(data['smoker']) data['Smoker'] = le.transform(data['smoker']) le.fit(data['region']) data['Region'] = le.transform(data['region']) #independent and dependent columns x = data[["age", "bmi", "children", "Sex", "Smoker", "Region"]] y = data['charges'] #split in train and test x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0) #model training linreg = LinearRegression() linreg.fit(x_train, y_train) #model testing predictions = linreg.predict(x_test) linreg.score(x_test,y_test) #save the model file = open("expense_model.pkl", 'wb') pickle.dump(linreg, file)

Also Read: How to Deploy Machine Learning Models using Flask (with Code!)

Now it’s time to get ready and build a flask application. we will move in a step-by-step procedure by designing an HTML page, server-side flask application.

Step 1: Create a HTML Page

In templates, folders create an HTML file with any name. we have kept it as index.html. In this, we have to design a form that will visible to a user through which the user will provide us a data. I have created a simple web interface to observe everything clearly. you can give design it more and can also add a CSS file from a static folder. The code snippet is below and its explanation is given below code.

{{ prediction_text }}

Healthcare Expense Predictor

Age:

BMI:

Children:

Sex:

Smoker:

Region:

{{ prediction_text }}

In HTML form we have used a dynamic URL building of the Jinja template. It means that when a form is submitted then where should a user be redirected. in our form whenever the to submit button will be triggered then it will call to prediction URL and our flask application will capable to access the data.

Dynamic URL Building

It is a method used to dynamically build URLs at run-time. url_for method is used to achieve this. the first parameter is accepted as a folder name or function name and the second is the filename. If you use it in flask then it is used to redirect to some URL after success or failure of some event where URL changes as per input. In simple words means the URL keeps changing based on inputs. Consider the below script as a demo in a flask of how to build a dynamic URL.

@app.route('/user/') def hello_user(name): if name =='admin': return redirect(url_for('admin')) else: return redirect(url_for('guest', guest = name))

In the above code, it accepts the name from the frontend through some form, and on hitting a post request hello user function accepts the name and check that it is admin then redirect to the admin panel else on the guest panel. The same dynamic URL building you can use if you are working on some big project or you want to redirect the user to different HTML pages based on his inputs.

Control Statements in Flask jinja Template

After creating the HTML form, we use the H2 tag with jinja syntax to display our output. In Flask, when you need to display data from the server on the front end, you enclose the variable in double curly braces within the desired tag. You can also use loops and conditions for printing data. Loops and control statements are written in single curly braces followed by a modulo sign. Below is a sample snippet demonstrating how to display an array of numbers and determine if they are even or odd. This code is for reference purposes only and must not be added to your actual code files. It helps you understand how to use control statements in jinja.

{% for i in arr%} {% if i%2 == 0%} &lt;h4&gt; {{i}} &lt;/h4&gt; &lt;p&gt;Even&lt;/p&gt; {% else %} &lt;h4&gt; {{i}} &lt;/h4&gt; &lt;p&gt;Odd&lt;/p&gt; {% endif %} {% endfor %}

Step 2: Create a Flask Python Application

Now let us edit our python app file. here we will write a complete logic of how our web app routing will happen, and what action to take each time. The complete explanation of each term is described below the code.

from flask import Flask, render_template, request import pickle app = Flask(__name__) model = pickle.load(open('expense_model.pkl','rb')) #read mode @app.route("/") def home(): return render_template('index.html') @app.route("/predict", methods=['GET','POST']) def predict(): if request.method == 'POST': #access the data from form ## Age age = int(request.form["age"]) bmi = int(request.form["bmi"]) children = int(request.form["children"]) Sex = int(request.form["Sex"]) Smoker = int(request.form["Smoker"]) Region = int(request.form["Region"]) #get prediction input_cols = [[age, bmi, children, Sex, Smoker, Region]] prediction = model.predict(input_cols) output = round(prediction[0], 2) return render_template("index.html", prediction_text='Your predicted annual Healthcare Expense is \$ {}'.format(output)) if __name__ == "__main__": app.run(debug=True) Stage 1

First, we have to import the Flask class and define our app. After that, we have loaded the model that we have saved in our working directory. The first routing is at our home page which is given by a single escape sign. It means that if the user heat at the “/” URL then redirects to our HTML page which is our home application.

Stage 2

After this, the main predict function is there. It means if the user makes a POST request means to hit a submit button then load the site to “/predict”, and the flask will access the data inserted in HTML form. when we have the data we pass it to our loaded model in 2-dimension to get the desired output. As we get an output to redirect the user to the same page with prediction and using jinja templating we have printed output on the HTML page.

Final Stage

The inputs from the flask form are accessed using the name attribute that we have provided to each label while creating an HTML page. the request make a request to that input label and match it with the name and collect the value selected or entered by the user. so when you create an HTML form for a machine learning model do provide each input with an appropriate name so you can access the value easily. And the input variable is categorical then using the value attribute we have encoded it to numeric. so when a request fetches the data it gets the value as specified but when it brings the data, it is always in string format so we typecast it to an integer.

Step 3: Run the Flask application locally

That sits, and by following these simple two to three easy steps you can deploy your machine learning model and make it available for the public to use. Now let’s check this from our command prompt whether everything is running fine or not. open your command prompt and go into the working directory and just run the app file.

On running the app file you will get a localhost URL, copy the URL and open it in the browser and you will see your web app in your browser.

Now Provide some random data in a form and check whether you are getting predictions or not.

Hence, we have successfully made our first flask application, and we are getting an output. Now you can use any cloud platform to deploy your application and make it available for the audience to use.

Q1. What is Flask Python used for?

A. Flask Python is a web framework used for building web applications in Python. It provides tools and libraries for handling web development tasks efficiently.

Q2. Is Flask Python or Django?

A. Flask and Django are both Python web frameworks but differ in their design philosophies and feature sets. Flask is minimalistic and flexible, while Django is a full-featured framework with batteries included.

Q3. What is Python Django vs Flask?

A. Python Django is a high-level web framework that follows the Model-View-Controller (MVC) architectural pattern. On the other hand, Flask is a microframework that follows a simpler and more lightweight approach.

Q4. Is Python Flask an API?

A. Yes, Python Flask can be used to build APIs. It provides the tools and libraries to handle HTTP requests and responses, making it suitable for building RESTful APIs or web services.