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Machine learning has the ultimate goal of creating a model that is generalizable. It is important to select the most accurate model by comparing and choosing it correctly. You will need a different holdout than the one you used to train your hyperparameters. You will also need to use statistical tests that are appropriate to evaluate the results.

It is crucial to understand the goals of the users or participants in a data science project. However, this does not guarantee success. Data science teams must adhere to best practices when executing a project in order to deliver on a clearly defined brief. These ten points can be used to help you understand what it means.

1. Understanding the Problem

Knowing the problem you are trying to solve is the most important part of solving it. You must understand the problem you are trying to predict, all constraints, and the end goal of this project.

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2. Know Your Data

Knowing what your data means will help you understand which models are most effective and which features to use. The data problem will determine which model is most successful. Also, the computational time will impact the project’s cost.

You can improve or mimic human decision-making by using and creating meaningful features. It is crucial to understand the meaning of each field, especially when it comes to regulated industries where data may be anonymized and not clear. If you’re unsure what something means, consult a domain expert.

3. Split your data

What will your model do with unseen data? If your model can’t adapt to new data, it doesn’t matter how good it does with the data it is given.

You can validate its performance on unknown data by not letting the model see any of it while training. This is essential in order to choose the right model architecture and tuning parameters for the best performance.

Splitting your data into multiple parts is necessary for supervised learning. The training data is the data the model uses to learn. It typically consists of 75-80% of the original data.

This data was chosen randomly. The remaining data is called the testing data. This data is used to evaluate your model. You may need another set of data, called the validation set.

This is used to compare different supervised learning models that were tuned using the test data, depending on what type of model you are creating.

You will need to separate the non-training data into the validation and testing data sets. It is possible to compare different iterations of the same model with the test data, and the final versions using the validation data.

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4. Don’t Leak Test Data

It is important to not feed any test data into your model. This could be as simple as training on the entire data set, or as subtle as performing transformations (such as scaling) before splitting.

If you normalize your data before splitting, the model will gain information about the test set, since the global minimum and maximum might be in the held-out data.

5. Use the Right Evaluation Metrics

Every problem is unique so the evaluation method must be based on that context. Accuracy is the most dangerous and naive classification method. Take the example of cancer detection.

We should always say “not cancer” if we want to build a reliable model. This will ensure that we are correct 99 percent of the time.

Also read: Top 10 Business Intelligence Tools of 2023

6. Keep it simple

It is important to select the best solution for your problem and not the most complex. Management, customers, and even you might want to use the “latest-and-greatest.” You need to use the simplest model that meets your needs, a principle called Occam’s Razor.

This will not only make it easier to see and reduce training time but can also improve performance. You shouldn’t try to kill Godzilla or shoot a fly with your bazooka.

7. Do not overfit or underfit your model

Overfitting, also called variance, can lead to poor performance when the model doesn’t see certain data. The model simply remembers the training data.

Bias, also known as underfitting, is when the model has too few details to be able to accurately represent the problem. These two are often referred to as “bias-variance trading-off”, and each problem requires a different balance.

Let’s use a simple image classification tool as an example. It is responsible for identifying whether a dog is present in an image.

8. Try Different Model Architectures

It is often beneficial to look at different models for a particular problem. One model architecture may not work well for another.

You can mix simple and complex algorithms. If you are creating a classification model, for example, try as simple as random forests and as complex as neural networks.

Interestingly, extreme gradient boosting is often superior to a neural network classifier. Simple problems are often easier to solve with simple models.

9. Tune Your Hyperparameters

These are the values that are used in the model’s calculation. One example of a hyperparameter in a decision tree would be depth.

This is how many questions the tree will ask before it decides on an answer. The default parameters for a model’s hyperparameters are those that give the highest performance on average.

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10. Comparing Models Correctly

Machine learning has the ultimate goal of creating a model that is generalizable. It is important to select the most accurate model by comparing and choosing it correctly.

You will need a different holdout than the one you used to train your hyperparameters. You will also need to use statistical tests that are appropriate to evaluate the results.

You're reading Data Science: The 10 Commandments For Performing A Data Science Project

Mastering Exploratory Data Analysis(Eda) For Data Science Enthusiasts

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

Overview

Step by Step approach to Perform EDA

Resources Like Blogs, MOOCS for getting familiar with EDA

Getting familiar with various Data Visualization techniques, charts, plots

Demonstration of some steps with Python Code Snippet

What is that one thing that differentiates one data science professional, from the other?

Not Machine Learning, Not Deep Learning, Not SQL, It’s Exploratory Data Analysis (EDA). How good one is with the identification of hidden patterns/trends of the data and how valuable the extracted insights are, is what differentiates Data Professionals.

1. What Is Exploratory Data Analysis

EDA assists Data science professionals in various ways:-

3 Getting a better understanding of the problem statement

[ Note: the dataset in this blog is being opted as iris dataset]

2. Checking Introductory Details About Data

The first and foremost step of any data analysis, after loading the data file, should be about checking few introductory details like, no. Of columns, no. of rows, types of features( categorical or Numerical), data types of column entries.

Python Code Snippet

Python Code:



data.head()              For displaying first five rows

data.tail()   For Displaying last Five Rows

3. Statistical Insight

This step should be performed for getting details about various statistical data like Mean, Standard Deviation, Median, Max Value, Min Value

Python Code Snippet

data.describe() 

  4. Data cleaning

This is the most important step in EDA involving removing duplicate rows/columns, filling the void entries with values like mean/median of the data, dropping various values, removing null entries

Checking Null entries

data.IsNull().sum   gives the number of missing values for each variable

Removing Null Entries

data.dropna(axis=0,inplace=True)     If null entries are there

Filling values in place of Null Entries(If Numerical feature)

Values can either be mean, median or any integer

Python Code Snippet

Checking Duplicates

data.duplicated().sum()  returning total number of duplicates entries

Removing Duplicates

data.drop_duplicates(inplace=True)

5. Data Visualization

Data visualization is the method of converting raw data into a visual form, such as a map or graph, to make data easier for us to understand and extract useful insights.

The main goal of data visualization is to put large datasets into a visual representation. It is one of the important steps and simple steps when it comes to data science

You Can refer to the blog below for getting more details about Data Visualization

Various Types of Visualization analysis is: a. Uni Variate analysis:

This shows every observation/distribution in data on a single data variable. It can be shown with the help of various plots like Scatter Plot, Line plot,  Histogram(summary)plot, box plots, violin plot, etc.

b. Bi-Variate analysis: c. Multi-Variate analysis:

Scatterplots, Histograms, box plots, violin plots can be used for Multivariate Analysis

Various Plots

Below are some of the plots that can be deployed for Univariate, Bivariate, Multivariate analysis

a. Scatter Plot Python Code Snippet

sns.scatterplot(data[‘sepal_length’],data[‘sepal_width’],hue =data[‘species’],s=50)

For multivariate analysis Python Code Snippet

sns.pairplot(data,hue=”species”,height=4)

b. Box Plot Python Code Snippet

plt.show()

c. Violin Plot

More informative, than box plot, and shows full distribution of data

Python Code Snippet

plt.show()

d. Histograms

It can be used for visualizing the Probability density function(PDF)

Python Code Snippet

.add_legend();

Email: [email protected]

You can refer to the blog being, mentioned below for getting familiar with Exploratory Data Analysis

Exploratory Data Analysis: Iris Dataset

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

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A/B Testing For Data Science Using Python – A Must

Overview

A/B testing is a popular way to test your products and is gaining steam in the data science field

Here, we’ll understand what A/B testing is and how you can leverage A/B testing in data science using Python

Introduction

Statistical analysis is our best tool for predicting outcomes we don’t know, using the information we know.

Picture this scenario – You have made certain changes to your website recently. Unfortunately, you have no way of knowing with full accuracy how the next 100,000 people who visit your website will behave. That is the information we cannot know today, and if we were to wait until those 100,000 people visited our site, it would be too late to optimize their experience.

This seems to be a classic Catch-22 situation!

This is where a data scientist can take control. A data scientist collects and studies the data available to help optimize the website for a better consumer experience. And for this, it is imperative to know how to use various statistical tools, especially the concept of A/B Testing.

A/B Testing is a widely used concept in most industries nowadays, and data scientists are at the forefront of implementing it. In this article, I will explain A/B testing in-depth and how a data scientist can leverage it to suggest changes in a product.

Table of contents:

What is A/B testing?

How does A/B testing work?

Statistical significance of the Test

Mistakes we must avoid while conducting the A/B test

When to use A/B test

What is A/B testing?

A/B testing is a basic randomized control experiment. It is a way to compare the two versions of a variable to find out which performs better in a controlled environment.

For instance, let’s say you own a company and want to increase the sales of your product. Here, either you can use random experiments, or you can apply scientific and statistical methods. A/B testing is one of the most prominent and widely used statistical tools.

In the above scenario, you may divide the products into two parts – A and B. Here A will remain unchanged while you make significant changes in B’s packaging. Now, on the basis of the response from customer groups who used A and B respectively, you try to decide which is performing better.

Source

It is a hypothetical testing methodology for making decisions that estimate population parameters based on sample statistics. The population refers to all the customers buying your product, while the sample refers to the number of customers that participated in the test.

How does A/B Testing Work?

The big question!

In this section, let’s understand through an example the logic and methodology behind the concept of A/B testing.

Let’s say there is an e-commerce company XYZ. It wants to make some changes in its newsletter format to increase the traffic on its website. It takes the original newsletter and marks it A and makes some changes in the language of A and calls it B. Both newsletters are otherwise the same in color, headlines, and format.

Objective

Our objective here is to check which newsletter brings higher traffic on the website i.e the conversion rate. We will use A/B testing and collect data to analyze which newsletter performs better.

1.  Make a Hypothesis

Before making a hypothesis, let’s first understand what is a hypothesis.

A hypothesis is a tentative insight into the natural world; a concept that is not yet verified but if true would explain certain facts or phenomena.

It is an educated guess about something in the world around you. It should be testable, either by experiment or observation. In our example, the hypothesis can be “By making changes in the language of the newsletter, we can get more traffic on the website”.

In hypothesis testing, we have to make two hypotheses i.e Null hypothesis and the alternative hypothesis. Let’s have a look at both.

Null hypothesis or H0:

The null hypothesis is the one that states that sample observations result purely from chance. From an A/B test perspective, the null hypothesis states that there is no difference between the control and variant groups. It states the default position to be tested or the situation as it is now, i.e. the status quo. Here our H0 is ” there is no difference in the conversion rate in customers receiving newsletter A and B”.

Alternative Hypothesis or H0:

The alternative hypothesis challenges the null hypothesis and is basically a hypothesis that the researcher believes to be true. The alternative hypothesis is what you might hope that your A/B test will prove to be true.

In our example, the Ha is- “the conversion rate of newsletter B is higher than those who receive newsletter A“.

Now, we have to collect enough evidence through our tests to reject the null hypothesis.

2. Create Control Group and Test Group

Once we are ready with our null and alternative hypothesis, the next step is to decide the group of customers that will participate in the test. Here we have two groups – The Control group, and the Test (variant) group.

The Control Group is the one that will receive newsletter A and the Test Group is the one that will receive newsletter B.

For this experiment, we randomly select 1000 customers – 500 each for our Control group and Test group.

Randomly selecting the sample from the population is called random sampling. It is a technique where each sample in a population has an equal chance of being chosen. Random sampling is important in hypothesis testing because it eliminates sampling bias, and it’s important to eliminate bias because you want the results of your A/B test to be representative of the entire population rather than the sample itself.

Another important aspect we must take care of is the Sample size. It is required that we determine the minimum sample size for our A/B test before conducting it so that we can eliminate under coverage bias. It is the bias from sampling too few observations.

3. Conduct the A/B Test and Collect the Data

One way to perform the test is to calculate daily conversion rates for both the treatment and the control groups. Since the conversion rate in a group on a certain day represents a single data point, the sample size is actually the number of days. Thus, we will be testing the difference between the mean of daily conversion rates in each group across the testing period.

When we run our experiment for one month, we noticed that the mean conversion rate for the Control group is 16% whereas that for the test Group is 19%.

Statistical significance of the Test

Now, the main question is – Can we conclude from here that the Test group is working better than the control group?

The answer to this is a simple No! For rejecting our null hypothesis we have to prove the Statistical significance of our test.

There are two types of errors that may occur in our hypothesis testing:

Type I error: We reject the null hypothesis when it is true. That is we accept the variant B when it is not performing better than A

Type II error: We failed to reject the null hypothesis when it is false. It means we conclude variant B is not good when it performs better than A

To avoid these errors we must calculate the statistical significance of our test.

An experiment is considered to be statistically significant when we have enough evidence to prove that the result we see in the sample also exists in the population.

That means the difference between your control version and the test version is not due to some error or random chance. To prove the statistical significance of our experiment we can use a two-sample T-test.

Source

To understand this, we must be familiar with a few terms:

Significance level (alpha):

The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. Generally, we use the significance value of 0.05

P-Value: It is the probability that the difference between the two values is just because of random chance. P-value is evidence against the null hypothesis. The smaller the p-value stronger the chances to reject the H0. For the significance level of 0.05, if the p-value is lesser than it hence we can reject the null hypothesis

Confidence interval: The confidence interval is an observed range in which a given percentage of test outcomes fall. We manually select our desired confidence level at the beginning of our test. Generally, we take a 95% confidence interval

Next, we can calculate our t statistics using the below formula:

Let’s Implement the Significance Test in Python

Let’s see a python implementation of the significance test. Here, we have a dummy data having an experiment result of an A/B testing for 30 days. Now we will run a two-sample t-test on the data using Python to ensure the statistical significance of chúng tôi can download the sample data here.

Python Code:



sns.distplot(data.Conversion_B)

At last, we will perform the t-test:

t_stat, p_val= ss.ttest_ind(data.Conversion_B,data.Conversion_A) t_stat , p_val (3.78736793091929, 0.000363796012828762)

For our example, the observed value i.e the mean of the test group is 0.19. The hypothesized value (Mean of the control group) is 0.16. On the calculation of the t-score, we get the t-score as .3787. and the p-value is 0.00036.

SO what does all this mean for our A/B Testing?

Here, our p-value is less than the significance level i.e 0.05. Hence, we can reject the null hypothesis. This means that in our A/B testing, newsletter B is performing better than newsletter A. So our recommendation would be to replace our current newsletter with B to bring more traffic on our website.

What Mistakes Should we Avoid While Conducting A/B Testing?

There are a few key mistakes I’ve seen data science professionals making. Let me clarify them for you here:

Invalid hypothesis: The whole experiment depends on one thing i.e the hypothesis. What should be changed? Why should it be changed, what the expected outcome is, and so on? If you start with the wrong hypothesis, the probability of the test succeeding, decreases

Testing too Many Elements Together: Industry experts caution against running too many tests at the same time. Testing too many elements together makes it difficult to pinpoint which element influenced the success or failure. Thus, prioritization of tests is indispensable for successful A/B testing

Ignoring Statistical Significance: It doesn’t matter what you feel about the test. Irrespective of everything, whether the test succeeds or fails, allow it to run through its entire course so that it reaches its statistical significance

Not considering the external factor: Tests should be run in comparable periods to produce meaningful results. For example, it is unfair to compare website traffic on the days when it gets the highest traffic to the days when it witnesses the lowest traffic because of external factors such as sale or holidays

When Should We Use A/B Testing?

A/B testing works best when testing incremental changes, such as UX changes, new features, ranking, and page load times. Here you may compare pre and post-modification results to decide whether the changes are working as desired or not.

A/B testing doesn’t work well when testing major changes, like new products, new branding, or completely new user experiences. In these cases, there may be effects that drive higher than normal engagement or emotional responses that may cause users to behave in a different manner.

End Notes

To summarize, A/B testing is at least a 100-year-old statistical methodology but in its current form, it comes in the 1990s. Now it has become more eminent with the online environment and availability for big data. It is easier for companies to conduct the test and utilize the results for better user experience and performance.

There are many tools available for conducting A/B testing but being a data scientist you must understand the factors working behind it. Also, you must be aware of the statistics in order to validate the test and prove it’s statistical significance.

To know more about hypothesis testing, I will suggest you read the following article:

Related

Top 10 Universities Teaching Data Science For Free In 2023

Data science has created a plethora of job opportunities for aspiring data scientists and many more professions in recent years. Students and working professionals are highly interested to have a strong understanding of different aspects and elements of data science. They constantly look for online courses with 100% placement in reputed companies from different institutions offering courses on data science. Courses on data science are providing a sufficient and deep understanding of all key concepts and hands-on experience with real-life projects to candidates. Let’s explore some of the top universities providing data science courses.

MIT, OpenCourseWare

MIT is one of the leading institutes for both teaching and research in the field of modern computing. In 2001, the university launched its OpenCourseWare platform. The aim of which is to make lecture notes, problem sets, exams, and video lectures, for the vast majority of its courses, available for free online.

Columbia, Applied Machine Learning

Andreas C. Muller, one of the core developers for the popular Python machine learning library Scikit-learn, is also a Research Scientist and lecturer at Columbia University. Each year he publishes all material for his ‘Applied Machine Learning Course online. All the slides, lecture notes, and homework assignments for the course are available in this Github repo.

Stanford, Seminars Free Online Courses, Harvard

Harvard University publishes a selection of completely free online courses on its website. The courses are mostly hosted by edX so you also have the option of pursuing certification for each course for a small payment.

Purdue University: Krannert School of Management

Offered through the Krannert School of Management, Purdue University’s Master of Science in Business Analytics and Information Management is a full-time program that starts every year in June and runs for three semesters. The graduate program offers three specializations in supply chain analytics, investment analytics, and corporate finance analytics.

DePaul University

DePaul University offers an MS in data science that promises to equip students with the right skills for a career in data science. The program includes a graduate capstone requirement, but you can choose between completing a real-world data analytics project, taking a predictive analytics capstone course, participating in an analytics internship, or completing a master’s thesis.

University of Rochester

The University of Rochester offers an MS in Data Science through the Goergen Institute for Data Science. The program can be completed in two or three semesters of full-time study, but the two-semester path includes a rigorous course load, so it’s recommended for students who already have a strong background in computer science and mathematics. For those without a strong background in computer science, you can take an optional summer course that will help get you up to speed before the program starts.

New York University

New York University offers an MS in Data Science (MSDS) and offers several concentrations to select from, including data science, big data, mathematics and data, natural language processing, and physics. You’ll need to earn 36 credits to graduate, which takes full-time students an average of two years to complete.

Carnegie Mellon University

Carnegie Mellon University offers a Master’s in Computation Data Science (MCDS) through the Tepper School of Business. During your first semester, you will be required to take four core courses: cloud computing, machine learning, interactive data science, and a data science seminar. By the end of the first semester, you will need to select from three concentrations, including systems, analytics, or human-centered data science. Your concentration will help inform the courses you take during the rest of the program.

North Carolina State University – Raleigh, North Carolina

North Carolina State University offers an MS in Analytics (MSA) program that’s designed as a 10-month cohort-based learning experience that focuses on teamwork and one-on-one coaching. The graduating MSA class of 2023 had a 95 percent employment rate by graduation, with an average base salary of US$98,200 per year, according to the university.

Data Science Interview Series: Part

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

Introduction

Data science interviews consist of questions from statistics and probability, Linear Algebra, Vector, Calculus, Machine Learning/Deep learning mathematics, Python, OOPs concepts, and Numpy/Tensor operations. Apart from these, an interviewer asks you about your projects and their objective. In short, interviewers focus on basic concepts and projects.

This article is part 1 of the data science interview series and will cover some basic data science interview questions. We will discuss the interview questions with their answers:

What is OLS? Why, and Where do we use it?

OLS (or Ordinary Least Square) is a linear regression technique that helps estimate the unknown parameters that can influence the output. This method relies on minimizing the loss function. The loss function is the sum of squares of residuals between the actual and predicted values. The residual is the difference between the target values and forecasted values. The error or residual is:

Minimize ∑(yi – ŷi)^2

Where ŷi is the predicted value, and yi is the actual value.

We use OLS when we have more than one input. This approach treats the data as a matrix and estimates the optimal coefficients using linear algebra operations.

What is Regularization? Where do we use it?

Regularization is a technique that reduces the overfitting of the trained model. This technique gets used where the model is overfitting the data.

Overfitting occurs when the model performs well with the training set but not with the test set. The model gives minimal error with the training set, but the error is high with the test set.

Hence, the regularization technique penalizes the loss function to acquire the perfect fit model.

What is the Difference between L1 AND L2 Regularization?

L1 Regularization is also known as Lasso(Least Absolute Shrinkage and Selection Operator) Regression. This method penalizes the loss function by adding the absolute value of coefficient magnitude as a penalty term.

Lasso works well when we have a lot of features. This technique works well for model selection since it reduces the features by shrinking the coefficients to zero for less significant variables.

Thus it removes some features that have less importance and selects some significant features.

L2 Regularization( or Ridge Regression) penalizes the model as the complexity of the model increases. The regularization parameter (lambda) penalizes all the parameters except intercept so that the model generalizes the data and does not overfit.

Ridge regression adds the squared magnitude of the coefficient as a penalty term to the loss function. When the lambda value is zero, it becomes analogous to OLS. While lambda is very large, the penalty will be too much and lead to under-fitting.

Moreover, Ridge regression pushes the coefficients towards smaller values while maintaining non-zero weights and a non-sparse solution. Since the square term in the loss function blows up the outliers residues that make the L2 sensitive to outliers, the penalty term endeavors to rectify it by penalizing the weights.

Ridge regression performs better when all the input features influence the output with weights roughly equal in size. Besides, Ridge regression can also learn complex data patterns.

What is R Square?

R Square is a statistical measure that shows the closeness of the data points to the fitted regression line. It calculates the percentage of the predicted variable variation calculated by a linear model.

The value of R-Square lies between 0% and 100%, where 0 means the model can not explain the variation of the predicted values around its mean. Besides, 100% indicates that the model can explain the whole variability of the output data around its mean.

In short, the higher the R-Square value, the better the model fits the data.

Adjusted R-Squared

The R-square measure has some drawbacks that we will address here too.

The problem is if we add junk independent variables or significant independent variables, or impactful independent variables to our model, the R-Squared value will always increase. It will never decrease with a newly independent variable addition, whether it could be an impactful, non-impactful, or insignificant variable. Hence we need another way to measure equivalent RSquare, which penalizes our model with any junk independent variable.

So, we calculate the Adjusted R-Square with a better adjustment in the formula of generic R-square.

What is Mean Square Error?

Mean square error tells us the closeness of the regression line to a set of data points. It calculates the distances from data points to the regression line and squares those distances. These distances are the errors of the model for predicted and actual values.

The line equation is given as y = MX+C

M is the slope, and C is the intercept coefficient. The objective is to find the values for M and C to best fit the data and minimize the error.

Why Support Vector Regression? Difference between SVR and a simple regression model?

The objective of the simple regression model is to minimize the error rate while SVR tries to fit the error into a certain threshold.

Main Concepts:

Boundary

Kernel

Support Vector

Hyper-plane

The best fit line is the line that has a maximum number of points on it. The SVR attempts to calculate a decision boundary at the distance of ‘e’ from the base hyper-plane such that data points are nearest to that hyper-plane and support vectors are within that boundary line.

Conclusion

The ordinary least squares technique estimates the unknown coefficients and relies on minimizing the residue.

L1 and L2 Regularization penalizes the loss function with absolute value and square of the value of the coefficient, respectively.

The R-square value indicates the variation of response around its mean.

R-square has some drawbacks, and to overcome these drawbacks, we use adjusted R-Square.

Mean square error calculates the distance between points on the regression line to the data points.

SVR fits the error within a certain threshold instead of minimizing it.

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

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Top 10 Data Science Jobs To Apply For Before May End

Embarking on a Data Science career opens a world of exciting possibilities to extract valuable insights from complex datasets.

Professionals with a strong technical and analytical skill set can find intriguing career prospects in the rapidly expanding field of data science. Finding the ideal data science career on the job market is a terrific idea as May draws to a close. The top 10 data science jobs that you should think about applying for before the month is out covered in this post.

1.Data Engineer– The infrastructure needed for data science initiatives is built and maintained by data engineers, who are essential to the process. They work with big data technologies like Hadoop and Spark, develop data pipelines, and guarantee data quality. For this position, it is crucial to have a solid grasp of database systems and programming language skills in Python and SQL. Because of their high demand, data engineers often earn US$120,000 annually.

2.Analyst for Business Intelligence– Data collection, analysis, and presentation in a form that business users can understand are the duties of business intelligence analysts. They assist organizations in making better decisions by utilizing their expertise in data mining, data visualization, and reporting. The average annual income for business intelligence analysts is US$95,000, and there is a considerable need for these professionals.

3.Data Scientist– The creation and implementation of data-driven solutions to business problems fall under the purview of data scientists. They build predictive models using their expertise in statistics, machine learning, and programming. The average annual income for data scientists, who are in high demand, is US$120,000.

4.Statistician– It is the job of statisticians to gather, examine, and interpret data. They apply their knowledge of probability, statistics, and data analysis to solve issues in a range of industries, including business, healthcare, and government. Because of their high demand, statisticians typically earn US$100,000 annually.

5.Data Analysts– Data collection, cleaning, and analysis are the duties of data analysts. They give suggestions that can help firms improve their operations by using their talents to see trends and patterns. The average annual compensation for data analysts is US$90,000, and this occupation is in high demand.

6.Quantitative Analyst– To make financial judgments, quantitative analysts oversee employing mathematical and statistical models. They create models that can forecast future changes in the market using their expertise in statistics, finance, and programming. The typical wage for a quantitative analyst is US$150,000 per year, and there is a considerable demand for their services.

7.Data Visualization Expert– The creation of visual representations of data is the responsibility of data visualization specialists. They provide charts, graphs, and other images that can aid in the understanding of data by utilizing their expertise in data analysis, data storytelling, and data visualization. Specialists in data visualization are in high demand, and their annual salaries are often US$100,000.

8.Data Security Specialist– Engineers in data security oversee preventing unauthorized access, use, disclosure, disturbance, alteration, and destruction of data. To create and implement security measures that can safeguard data assets, they make use of their expertise in information security, cryptography, and network security. The average annual compensation for data security engineers is US$130,000, and there is a great need for these professionals.

9.Data Architect– Data architects are responsible for designing and implementing data systems. They use their skills in database design, data modeling, and data warehousing to create systems that can store, manage, and analyze large amounts of data. Data architects are in high demand, and the average salary for this position is US$140,000 per year.

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