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Introduction to Principal Component Analysis (PCA) in Python

Python is no longer an unfamiliar word for professionals from the IT or Web Designing world. It’s one of the most widely used programming languages because of its versatility and ease of usage. It has a focus on object-oriented, as well as functional and aspect-oriented programming. Python extensions also add a whole new dimension to the functionality it supports. The main reasons for its popularity are its easy-to-read syntax and value for simplicity. The Python language can be used as a glue to connect components of existing programmes and provide a sense of modularity.Introducing Principal Component Analysis with Python   1. Principal Component  Analysis definition   Principal Component Analysis is a method that is used to reduce the dimensionality of large amounts of data. It transforms many variables into a smaller set without sacrificing the information contained in the original set, thus reducing the dimensionality of the data.  PCA Python is often used in machine learning as it is easier for machine learning software to analyse and process smaller sets of data and variables. But this comes at a cost. Since a larger set of variables contends, it sacrifices accuracy for simplicity. It preserves as much information as possible while reducing the number of variables involved. The steps for Principal Component Analysis Python include Standardisation, that is, standardising the range of the initial variables so that they contribute equally to the analysis. It is to prevent variables with larger ranges from dominating over those with smaller ranges.  The next step involves complex matrix computation. It involves checking if there is any relationship between variables and shows if they contain redundant information or not. To identify this, the covariance matrix is computed. The next step is determining the principal components of the data. Principal Components are the new variables that are formed from the mixtures of the initial variables. The principal components are formed such that they're Uncorrelated, unlike the initial variables. They follow a descending order where the program tries to put as much information as possible in the first component, the remaining in the second, etc. It helps to discard components with low information and effectively reduces the number of variables. This comes at the cost of the principal components losing the meaning of the initial data. Further steps include computing the eigenvalues and discarding the figures with fewer eigenvalues, meaning that they have less significance. The remaining is a matrix of vectors that can be called the Feature Vector. It effectively reduces the dimensions since we take an eigenvalue. The last step involves reorienting the data obtained in the original axes to recast it along the axes formed by the principal components. 2. Objectives of PCA  The objectives of Principal Component Analysis are the following:  Find and Reduce the dimensionality of a data set As shown above, Principal Component Analysis is a helpful procedure to reduce the dimensionality of a data set by lowering the number of variables to keep track of.  Identify New Variables Sometimes this process can help one identify new underlying pieces of information and find new variables for the data sets which were previously missed.  Remove needless Variables The process reduces the number of needless variables by eliminating those with very little significance or those that strongly correlate with other variables.Image Source 3. Uses of PCA  The uses of Principal Component Analysis are wide and encompass many disciplines, for instance, statistics and geography with applications in image compression techniques etc. It is a huge component of compression technology for data and may be in video form, picture form, data sets and much more.  It also helps to improve the performance of algorithms as more features will increase their workload, but with Principal Component Analysis, the workload is reduced to a great degree. It helps to find correlating values since finding them manually in thousands of sets is almost impossible.  Overfitting is a phenomenon that occurs when there are too many variables in a set of data. Principal Component Analysis reduces overfitting, as the number of variables is now reduced. It is very difficult to carry out the visualisation of data when the number of dimensions being dealt with is too high. PCA alleviates this issue by reducing the number of dimensions, so visualisation is much more efficient, easier on the eyes and concise. We can potentially even use a 2D plot to represent the data after Principal Component Analysis.  4. Applications of PCA  As discussed above, PCA has a wide range of utilities in image compression, facial recognition algorithms, usage in geography, finance sectors, machine learning, meteorological departments and more. It is also used in the medical sector to interpret and process Medical Data while testing medicines or analysis of spike-triggered covariance. The scope of applications of PCA implementation is really broad in the present day and age.  For example, in neuroscience, spike-triggered covariance analysis helps to identify the properties of a stimulus that causes a neutron to fire up. It also helps to identify individual neutrons using the action potential they emit. Since it is a dimension reduction technique, it helps to find a correlation in the activity of large ensembles of neutrons. This comes in special use during drug trials that deal with neuronal actions.  5. Principal Axis Method  In the principal axis method, the assumption is that the common variance in communalities is less than one. The implementation of the method is carried out by replacing the main diagonal of the correlation matrix with the initial communality estimates. The initial matrix consisted of ones as per the PCA methodology. The principal components are now applied to this new and improved version of the correlation matrix.   6. PCA for Data Visualization Tools like Plotly allow us to visualise data with a lot of dimensions using the method of dimensional reduction and then applying it to a projection algorithm. In this specific example, a tool like Scikit-Learn can be used to load a data set and then the dimensionality reduction method can be applied to it. Scikit learn is a machine learning library. It has an arsenal of software and training machine learning algorithms along with evaluation and testing models. It works easily with NumPy and allows us to use the Principal Component Analysis Python and pandas library.  The PCA technique ranks the various data points based on relevance, combines correlated variables and helps to visualise them. Visualising only the Principal components in the representation helps make it more effective. For example, in a dataset containing 12 features, 3 represent more than 99% of the variance and thus can be represented in an effective manner.  The number of features can drastically affect its performance. Hence, reducing the amount of these features helps a lot to boost machine learning algorithms without a measurable decrease in the accuracy of results. 7. PCA as dimensionality reduction  The process of reducing the number of input variables in models, for instance, various forms of predictive models, is called dimensionality reduction. The fewer input variables one has, the simpler the predictive model is. Simple often means better and can encapsulate the same things as a more complex model would. Complex models tend to have a lot of irrelevant representations. Dimensionality reduction leads to sleek and concise predictive models.  Principal Component Analysis is the most common technique used for this purpose. Its origin is in the field of linear algebra and is a crucial method in data projection. It can automatically perform dimensionality reduction and give out principal factors, which can be translated as a new input and make much more concise predictions instead of the previous high dimensionality input.In this process, the features are reconstructed; in essence, the original features don't exist. They are, however, constructed from the same overall data but are not directly compared to it, but they can still be used to train machine learning models just as effectively.  8. PCA for visualisation: Hand-written digits  Handwritten digit recognition is a machine learning system's ability to identify digits written by hand, as on post, formal examinations and more. It's important in the field of exams where OMR sheets are often used. The system can recognise OMRs, but it also needs to recognise the student's information, besides the answers. In Python, a handwritten digit recognition system can be developed using moist Datasets. When handled with conventional PCA strategies of machine learning, these datasets can yield effective results in a practical scenario. It is really difficult to establish a reliable algorithm that can effectively identify handwritten digits in environments like the postal service, banks, handwritten data entry etc. PCA ensures an effective and reliable approach for this recognition. 9. Choosing the number of components  One of the most important parts of Principal Component analysis is estimating the number of components needed to describe the data. It can be found by having a look at the cumulative explained variance ratio and taking it as a function of the number of components.  One of the rules is Kaiser's Stopping file, where one should choose all components with an eigenvalue of more than one. This means that variables that have a measurable effect are the only ones that get chosen.  We can also plot a graph of the component number along with eigenvalues. The trick is to stop including values when the slope becomes close to a straight line in shape. 10. PCA as Noise Filtering  Principal Component Analysis has found a utility in the field of physics. It is used to filter noise from experimental electron energy loss (EELS) spectrum images. It, in general, is a method to remove noise from the data as the number of dimensions is reduced. The nuance is also reduced, and one only sees the variables which have the maximum effect on the situation. The principal component analysis method is used after the conventional demonising agents fail to remove some remnant noise in the data. Dynamic embedding technology is used to perform the principal component analysis. Then the eigenvalues of the various variables are compared, and the ones with low eigenvalues are removed as noise. The larger eigenvalues are used to reconstruct the speech data.  The very concept of principal component analysis lends itself to reducing noise in data, removing irrelevant variables and then reconstructing data which is simpler for the machine learning algorithms without missing the essence of the information input.   11. PCA to Speed-up Machine Learning Algorithms  The performance of a machine learning algorithm, as discussed above, is inversely proportional to the number of features input in it. Principal component analysis, by its very nature, allows one to drastically reduce the number of features of variables input, allows one to remove excess noise and reduces the dimensionality of the data set. This, in turn, means that there is a lot less strain on a machine learning algorithm, and it can produce near identical results with heightened efficiency.  12. Apply Logistic Regression to the Transformed Data  Logistic regression can be used after a principal component analysis. The PCA is a dimensionality reduction, while the logical regression is the actual brains that make the predictions. It is derived from the logistic function, which has its roots in biology.   13. Measuring Model Performance After preparing the data for a machine learning model using PCA, the effectiveness or performance of the model doesn’t change drastically. This can be tested by several metrics such as testing true positives, negatives, and false positives and false negatives. The effectiveness is computed by plotting them on a specialised confusion matrix for the machine learning model.  14. Timing of Fitting Logistic Regression after PCA  Principle component regression Python is the technique that can give predictions of the machine learning program after data prepared by the PCA process is added to the software as input. It more easily proceeds, and a reliable prediction is returned as the end product of logical regression and PCA.  15. Implementation of PCA with Python scikit learn can be used with Python to implement a working PCA algorithm, enabling Principal Component Analysis in Python 720 as explained above as well. It is a working form of linear dimensionality reduction that uses singular value decomposition of a data set to put it into a lower dimension space. The input data is taken, and the variables with low eigenvalues can be discarded using Scikit learn to only include ones that matter- the ones with a high eigenvalue. Steps involved in the Principal Component Analysis Standardization of dataset. Calculation of covariance matrix. Complete the eigenvalues and eigenvectors for the covariance matrix. Sort eigenvalues and their corresponding eigenvectors. Determine, k eigenvalues and form a matrix of eigenvectors. Transform the original matrix. Conclusion  In conclusion, PCA is a method that has high possibilities in the field of science, art, physics, chemistry, as well as the fields of graphic image processing, social sciences and much more, as it is effectively a means to compress data without compromising on the value it gives. Only the variables that do not significantly affect the value are removed, and the correlated variables are consolidated.

Introduction to Principal Component Analysis (PCA) in Python

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  • by Abhresh S
  • 10th May, 2021
  • Last updated on 26th May, 2021
  • 10 mins read
Introduction to Principal Component Analysis (PCA) in Python

Python is no longer an unfamiliar word for professionals from the IT or Web Designing world. It’s one of the most widely used programming languages because of its versatility and ease of usage. It has a focus on object-oriented, as well as functional and aspect-oriented programming. Python extensions also add a whole new dimension to the functionality it supports. The main reasons for its popularity are its easy-to-read syntax and value for simplicity. The Python language can be used as a glue to connect components of existing programmes and provide a sense of modularity.

Introducing Principal Component Analysis with Python  

 1. Principal Component  Analysis definition   

Principal Component Analysis is a method that is used to reduce the dimensionality of large amounts of data. It transforms many variables into a smaller set without sacrificing the information contained in the original set, thus reducing the dimensionality of the data.  

PCA Python is often used in machine learning as it is easier for machine learning software to analyse and process smaller sets of data and variables. But this comes at a cost. Since a larger set of variables contends, it sacrifices accuracy for simplicity. It preserves as much information as possible while reducing the number of variables involved. 

The steps for Principal Component Analysis Python include Standardisation, that is, standardising the range of the initial variables so that they contribute equally to the analysis. It is to prevent variables with larger ranges from dominating over those with smaller ranges.  

The next step involves complex matrix computation. It involves checking if there is any relationship between variables and shows if they contain redundant information or not. To identify this, the covariance matrix is computed. 

The next step is determining the principal components of the data. Principal Components are the new variables that are formed from the mixtures of the initial variables. The principal components are formed such that they're Uncorrelated, unlike the initial variables. They follow a descending order where the program tries to put as much information as possible in the first component, the remaining in the second, etc. It helps to discard components with low information and effectively reduces the number of variables. This comes at the cost of the principal components losing the meaning of the initial data. 

Further steps include computing the eigenvalues and discarding the figures with fewer eigenvalues, meaning that they have less significance. The remaining is a matrix of vectors that can be called the Feature Vector. It effectively reduces the dimensions since we take an eigenvalue. The last step involves reorienting the data obtained in the original axes to recast it along the axes formed by the principal components.

 2. Objectives of PCA  

The objectives of Principal Component Analysis are the following:  

Find and Reduce the dimensionality of a data set As shown above, Principal Component Analysis is a helpful procedure to reduce the dimensionality of a data set by lowering the number of variables to keep track of.  

  • Identify New Variables 

Sometimes this process can help one identify new underlying pieces of information and find new variables for the data sets which were previously missed.  

  • Remove needless Variables 

The process reduces the number of needless variables by eliminating those with very little significance or those that strongly correlate with other variables.Introduction to Principal Component Analysis (PCA) in Python

Image Source

 3. Uses of PCA  

The uses of Principal Component Analysis are wide and encompass many disciplines, for instance, statistics and geography with applications in image compression techniques etc. It is a huge component of compression technology for data and may be in video form, picture form, data sets and much more.  

It also helps to improve the performance of algorithms as more features will increase their workload, but with Principal Component Analysis, the workload is reduced to a great degree. It helps to find correlating values since finding them manually in thousands of sets is almost impossible 

Overfitting is a phenomenon that occurs when there are too many variables in a set of data. Principal Component Analysis reduces overfitting, as the number of variables is now reduced. 

It is very difficult to carry out the visualisation of data when the number of dimensions being dealt with is too high. PCA alleviates this issue by reducing the number of dimensions, so visualisation is much more efficient, easier on the eyes and concise. We can potentially even use a 2D plot to represent the data after Principal Component Analysis. 

 4. Applications of PCA  

As discussed above, PCA has a wide range of utilities in image compression, facial recognition algorithms, usage in geography, finance sectors, machine learning, meteorological departments and more. It is also used in the medical sector to interpret and process Medical Data while testing medicines or analysis of spike-triggered covariance. The scope of applications of PCA implementation is really broad in the present day and age.  

For example, in neuroscience, spike-triggered covariance analysis helps to identify the properties of stimulus that causes a neutron to fire up. It also helps to identify individual neutrons using the action potential they emit. Since it is a dimension reduction technique, it helps to find a correlation in the activity of large ensembles of neutrons. This comes in special use during drug trials that deal with neuronal actions. 

 5. Principal Axis Method 

In the principal axis method, the assumption is that the common variance in communalities is less than one. The implementation of the method is carried out by replacing the main diagonal of the correlation matrix with the initial communality estimates. The initial matrix consisted of ones as per the PCA methodology. The principal components are now applied to this new and improved version of the correlation matrix.  

 6. PCA for Data Visualization 

Tools like Plotly allow us to visualise data with a lot of dimensions using the method of dimensional reduction and then applying it to a projection algorithm. In this specific example, a tool like Scikit-Learn can be used to load a data set and then the dimensionality reduction method can be applied to it. Scikit learn is a machine learning library. It has an arsenal of software and training machine learning algorithms along with evaluation and testing models. It works easily with NumPy and allows us to use the Principal Component Analysis Python and pandas library.  

The PCA technique ranks the various data points based on relevance, combines correlated variables and helps to visualise them. Visualising only the Principal components in the representation helps make it more effective. For example, in a dataset containing 12 features, 3 represent more than 99% of the variance and thus can be represented in an effective manner.  

The number of features can drastically affect its performance. Hence, reducing the amount of these features helps a lot to boost machine learning algorithms without a measurable decrease in the accuracy of results.

 7. PCA as dimensionality reduction  

The process of reducing the number of input variables in models, for instance, various forms of predictive models, is called dimensionality reduction. The fewer input variables one has, the simpler the predictive model is. Simple often means better and can encapsulate the same things as a more complex model would. Complex models tend to have a lot of irrelevant representations. Dimensionality reduction leads to sleek and concise predictive models.  

Principal Component Analysis is the most common technique used for this purpose. Its origin is in the field of linear algebra and is a crucial method in data projection. It can automatically perform dimensionality reduction and give out principal factors, which can be translated as a new input and make much more concise predictions instead of the previous high dimensionality input.

In this process, the features are reconstructed; in essence, the original features don't exist. They are, however, constructed from the same overall data but are not directly compared to it, but they can still be used to train machine learning models just as effectively. 

 8. PCA for visualisation: Hand-written digits  

Handwritten digit recognition is a machine learning system's ability to identify digits written by hand, as on post, formal examinations and more. It's important in the field of exams where OMR sheets are often used. The system can recognise OMRs, but it also needs to recognise the student's information, besides the answers. In Python, a handwritten digit recognition system can be developed using moist Datasets. When handled with conventional PCA strategies of machine learning, these datasets can yield effective results in a practical scenario. It is really difficult to establish a reliable algorithm that can effectively identify handwritten digits in environments like the postal service, banks, handwritten data entry etc. PCA ensures an effective and reliable approach for this recognition.

 9. Choosing the number of components  

One of the most important parts of Principal Component analysis is estimating the number of components needed to describe the data. It can be found by having a look at the cumulative explained variance ratio and taking it as a function of the number of components.  

One of the rules is Kaiser's Stopping file, where one should choose all components with an eigenvalue of more than one. This means that variables that have a measurable effect are the only ones that get chosen.  

We can also plot a graph of the component number along with eigenvalues. The trick is to stop including values when the slope becomes close to a straight line in shape.

 10. PCA as Noise Filtering  

Principal Component Analysis has found a utility in the field of physics. It is used to filter noise from experimental electron energy loss (EELS) spectrum images. It, in general, is a method to remove noise from the data as the number of dimensions is reduced. The nuance is also reduced, and one only sees the variables which have the maximum effect on the situation. The principal component analysis method is used after the conventional demonising agents fail to remove some remnant noise in the data. Dynamic embedding technology is used to perform the principal component analysis. Then the eigenvalues of the various variables are compared, and the ones with low eigenvalues are removed as noise. The larger eigenvalues are used to reconstruct the speech data.  

The very concept of principal component analysis lends itself to reducing noise in data, removing irrelevant variables and then reconstructing data which is simpler for the machine learning algorithms without missing the essence of the information input.  

 11. PCA to Speed-up Machine Learning Algorithms  

The performance of a machine learning algorithm, as discussed above, is inversely proportional to the number of features input in it. Principal component analysis, by its very nature, allows one to drastically reduce the number of features of variables input, allows one to remove excess noise and reduces the dimensionality of the data set. This, in turn, means that there is a lot less strain on a machine learning algorithm, and it can produce near identical results with heightened efficiency. 

 12. Apply Logistic Regression to the Transformed Data  

Logistic regression can be used after a principal component analysis. The PCA is a dimensionality reduction, while the logical regression is the actual brains that make the predictions. It is derived from the logistic function, which has its roots in biology.  

 13. Measuring Model Performance 

After preparing the data for a machine learning model using PCA, the effectiveness or performance of the model doesn’t change drastically. This can be tested by several metrics such as testing true positives, negatives, and false positives and false negatives. The effectiveness is computed by plotting them on a specialised confusion matrix for the machine learning model. 

 14. Timing of Fitting Logistic Regression after PCA  

Principle component regression Python is the technique that can give predictions of the machine learning program after data prepared by the PCA process is added to the software as input. It more easily proceeds, and a reliable prediction is returned as the end product of logical regression and PCA. 

 15. Implementation of PCA with Python 

scikit learn can be used with Python to implement a working PCA algorithm, enabling Principal Component Analysis in Python 720 as explained above as well. It is a working form of linear dimensionality reduction that uses singular value decomposition of a data set to put it into a lower dimension space. The input data is taken, and the variables with low eigenvalues can be discarded using Scikit learn to only include ones that matter- the ones with a high eigenvalue. 

Steps involved in the Principal Component Analysis 

  1. Standardization of dataset. 
  2. Calculation of covariance matrix. 
  3. Complete the eigenvalues and eigenvectors for the covariance matrix. 
  4. Sort eigenvalues and their corresponding eigenvectors. 
  5. Determine, k eigenvalues and form a matrix of eigenvectors. 
  6. Transform the original matrix. 

Conclusion  

In conclusion, PCA is a method that has high possibilities in the field of science, art, physics, chemistry, as well as the fields of graphic image processing, social sciences and much more, as it is effectively a means to compress data without compromising on the value it gives. Only the variables that do not significantly affect the value are removed, and the correlated variables are consolidated.

Abhresh

Abhresh S

Author

An Online Technical Trainer by profession! And Content writer by hobby! Interested in sharing quality knowledge to make the Industry grow better towards better success and better tomorrow! With a Guru Mantra of - "Keep Learning & Keep Practicing".

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Python released in 1991 and within 5 to 6 years, this programming language become the most popular and widely used programming language in various disciplines. Today, companies use Python for GUI and CLI-based software development, web development (server-side), data science, machine learning, AI, robotics, drone systems, developing cyber-security tools, mathematics, system scripting, etc. PCAP is a professional Python certification credential that measures your competency in using the Python language to create code and your fundamental understanding of object-oriented programming.It comprises of topics likeBasic concepts of PythonOperators & data typesControl and EvaluationsModules and PackagesData AggregatesException HandlingStringsFunctions and ModulesObject-Oriented ProgrammingList Comprehensions, Lambdas, Closures, and I/O OperationsClasses, Objects, and ExceptionsDemand and Benefits: Having a Python certification verifies that the programmer or the aspirant has all the necessary and essential skills needed to become an expert Python developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The average entry-level salary of a Python developer starts at around $100k per annum. With a few years of experience, the average salary hikes to $ 105k annually.Top companies and organizations hiring certified Python programmers are Bank of America, Atlassian, Google, Adobe, Apple, Cisco Systems, Intel, Lyft, IBM, etc.Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for beginners in Python programming. It has hands-on learning with 24 hours of instructor-led online lectures. Apart from that, the course has 100 hours of MCQs and three live projects.Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, or computer science aspirant - who wants to pursue a career as a Python developer or  Python programmer can opt for this certification training. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 295Exam fee for certification: $ 295Retake fee for certification: If a candidate fails the exam, he/she has to wait for 15 days before being allowed to retake the exam for free. There is no limit to the number of times a candidate may retake an exam.4. MongoDB Certified Developer Associate ExamMongoDB is a NoSQL, document-based high-volume heterogeneous database system. Instead of having tables with rows and columns, MongoDB uses a collection of documents. It is a database development system that provides scalability and flexibility as per query requirements. Its document models are easy to implement for developers and can meet complex demands at scale.MongoDB created this MongoDB Certified Developer Associate Exam for individuals who require to verify their knowledge on fundamentals of designing and building applications using MongoDB. They recommend this certification for those who want to become software engineers and have a solid understanding of core MongoDB along with professional experience.It comprises of topics likeMongoDB BasicsCRUDIndexing and PerformanceThe MongoDB Aggregation FrameworkBasic Cluster AdministrationAggregation & ReplicationShardingMongoDB Performance  MongoDB for Python DevelopersMongoDB for Java Developers or MongoDB for JavaScript DevelopersData ModelingDemand and Benefits: Having a MongoDB Certified Developer Associate Exam certification verifies that the programmer or the aspirant has all the necessary and essential skills to become a NoSQL database expert. The MongoDB certification is inexpensive and in demand. The average salary for a software developer with MongoDB skills starts from $ 8200 per annum.Top companies and organizations hiring certified MongoDB developers are Accenture, Collabera, Leoforce LLC., Adobe, Trigent Software, Lyft, etc.Where to take Training for Certification: KnowledgeHut has a comprehensive course structure for those who want to learn MongoDB & Mongodb Administrator. It has 24+ hours of instructor-led online lectures and 80+ hours of hands-on with cloud labs. This self-paced course also includes capstone projects to give participants a feel of real world working.  Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to embark on a career as a MongoDB developer or start his/her career as a NoSQL database expert or do better in their current role as a MongoDB developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 150Exam fee for certification: $ 150Retake fee for certification: MongoDB University is no longer allowing a free retake with the exam fee. The candidate has to pay an additional $10 to reschedule or retake the exam.5. R Programming CertificationIt is a part of the data science specialization from Johns Hopkins University under Coursera. This course teaches R programming for efficient data analysis. It covers different R programming concepts like building blocks of R, datatypes, reading data into R from external files, accessing packages, writing functions, debugging techniques, profiling R code, and performing analysis.It comprises of topics like:Basic building blocks in RData types in RControl StructuresScoping Rules - OptimizationCoding StandardsDates and TimesFunctionsLoopingDebugging toolsSimulating data in RR ProfilerDemand and Benefits: Having an R Programming certification verifies that the programmer or the aspirant has all the necessary and essential skills require to get a job role as data analyst. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified R programmer with this certification is ₹ 508,224 per annum.Top companies and industries hiring certified R programmers are Technovatrix, CGI Group Inc., Amazon, Sparx IT Solutions, Accenture, Uber, etc.Where to take Training for Certification: KnowledgeHut has a fascinating training course for those who wants to become a R programmer. It has 22+ hours of instructor-led live training and three self-paced live projects.Who should take the Training (roles) for Certification: Any data analyst, graduate, post graduate student, experienced data analyst or computer science aspirant - who wants to settle as a R programmer or data analyst can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: FreeFee for certification: $ 60 (Coursera Plus Monthly)Retake fee for certification: Free6. Oracle MySQL Database Administration Training and Certification (CMDBA)It is another course offered by Oracle for SQL developers. Oracle University designed this course for database administrators who want to validate their skills with developing performance, blending business processes, and accomplishing data processing work. Structured Query Language (SQL) is one of the top database management query languages that allows us to access and manipulate databases. If you want to verify your database skills during a job interview or impress your peers at your workplace then this certification is worth getting. This certification path includes Professional, Specialist, and Developer levels. The candidate should pass the MySQL Database Administrator Certified Professional Exam Part 1 & Part 2 to earn the certification.It comprises of topics likeInstalling MySQLMySQL ArchitectureConfiguring MySQLUser ManagementMySQL SecurityMaintaining a Stable SystemOptimizing Query PerformanceBackup StrategiesConfiguring a Replication TopologyDemand and Benefits: Having an CMDBA certification verifies that the programmer or the aspirant has all the necessary and essential skills required to get a job role as SQL developer. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified MySQL DBA or backend developer with this certification is $ 66,470 per annum.Top companies and industries hiring Certified MySQL database administrators are Fiserv, IBM, HCL, Adobe, Microsoft, Apple, Accenture, Collabera, and more.Where to take Training for Certification: KnowledgeHut has a cutting-edge curriculum for those who want to become  MySQL database administrators. It has 16+ hours of instructor-led online lectures and 80+ hours of hands-on lab. Apart from that, this self-paced course has Capstone projects.Who should take the Training (roles) for Certification: Any developer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to pursue a career as a DBA or backend developer or start his/her career in database management or backend software development can opt for this certification course. There is no other prerequisite to appear for this exam or course.Course fees for Certification: $ 255Exam fee for certification: $ 255Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days after the initial attempt.7. CCA Spark and Hadoop DeveloperWith the exponential growth in data, IT firms and organizations have to manage this tremendous amount of data generated. So, many companies are actively looking for Big data and Spark developers who can optimize performance. Big Data is the term used to describe enormous volumes of data. Apache Spark supports data management as it is an open-source centralized analytics engine that handles large-scale data processing.It requires prerequisite knowledge of Scala and Python. This certification also verifies and showcases your skills through Spark and Hadoop projects. Passing this certification course gives you a logo and a license to authenticate your CCA status.It comprises of topics likeLoad data from HDFS for use in Spark applicationsWrite the results back into HDFS using SparkRead and write files in a variety of file formatsPerform standard extract, transform, load (ETL) processes on data using the Spark APIUse metastore tables as an input source or an output sink for Spark applicationsUnderstand the fundamentals of querying datasets in SparkFilter data using SparkWrite queries that calculate aggregate statisticsJoin disparate datasets using SparkProduce ranked or sorted dataSupply command-line options to change your application configuration, such as increasing available memoryDemand and Benefits: Passing the CCA Spark and Hadoop Developer Exam (CCA175) by Cloudera verifies that you have all the essential skills required to get a job as a Hadoop developer and handle Big data projects. The average salary of a certified CCA Spark and Hadoop Developer with this certification is $ 74,200 per annum.Top companies and industries hiring Certified Spark and Hadoop Developers are Primus Global, IBM, Collabera, CorroHealth, Genpact, Xerox, Accenture, and more.Where to take Training for Certification: KnowledgeHut has extensive courses for those who want to become Big Data experts and want to work as Hadoop developers. It has different courses on Big Data Analytics, Apache Storm, Hadoop Administration, Apache Spark & Scala, Big Data with Hadoop, and more.Who should take the Training (roles) for Certification: Any Big Data developer, graduate & post graduate students, Hadoop developer or computer science aspirant - who wants to make a career in Big data development or start his/her career as a Big Data or Hadoop project developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification: $ 295Application fee for certification: $ 295Exam fee for certification: $ 295Retake fee for certification: Within 30 to 60 minutes of exam completion, Cloudera will send a scorecard mail with a pass or fail status. If the candidate fails the exam, then they have to wait for 30 days for another try.  Cloudera gives additional discounts on retakes.ConclusionWhether you are starting your career as a coder or are an experienced programmer looking to grow in the industry, having a certification and proper knowledge of any popular programming language is one of the most proven ways to elevate your programming career.  We trust that this article will help you to understand your area of interest. Choose the programming language you wish to make a career in, wisely. This would also depend on your pre-existing knowledge. If you aren't sure which resource will be more informative for doing your certification as per your area of interest, KnowledgeHut (https://www.knowledgehut.com/) has all the support and expert trainers who can guide you, from start to finish—that is in clearing the exam and helping you gain sound knowledge of your preferred subject.Receiving a programming certification is an added bonus which will make you stand out from the rest. Proper training from an institute such as KnowledgeHut will help you gain skills that are relevant and in demand in the industry.
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Most Popular Programming Certifications for 2021

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Top IT Certifications for Java Developers in 2021

Programming languages are at the heart of computer science and software development. They help developers write efficient code for developing digital solutions through applications and websites. Programming helps in automating, maintaining, assembling, and measuring the processed data.  Java is one such popular programming language. It is a robust, high-level, general-purpose, pure object-oriented programming language developed by Sun Microsystems (now part of Oracle). James Gosling is the creator of Java which was earlier named Oak. Java ranks high in the top programming languages list and is one of the most extensively used software development platforms. It is well suited to developing software solutions and other innovative projects and simulations.  Since Oracle acquired Sun Microsystems in January 2010, they have been responsible for the further development of the Java platform. All the mentioned top Java certifications verify a specific expertise level and knowledge of the Java platform highlighting particular domains. Without further due, let us now dig into the top 5 Java certifications and their details. About Oracle’s Java CertificationsOrganizations and industries consider certifications as proof of knowledge, especially when the certifications are from a recognized body or firm. Aspirants and professionals looking for possibilities in the Java development domain can avail of a plethora of benefits through the certifications mentioned in this article. There are six levels of Oracle Java Certification based on job roles, skills, and responsibilities: Oracle Certified Junior Associate (OCJA) Oracle Certified Associate (OCA) Oracle Certified Professional (OCP) Oracle Certified Specialist (OCS) Oracle Certified Expert (OCE) Oracle Certified Master (OCM) Among them, the top five Java certifications that are in demand for the year 2021 are – 1. Oracle Certified Associate Java Programmer OCAJP (Applicable for Java SE 8 & Java SE 11)It is the preliminary and most basic certification provided by Oracle for Java. It helps you gain fundamental understanding of Java programming and build a foundation in Java and other general programming concepts. There are two subcategories in this certification – OCAJP Java Standard Edition 8 (OCAJP 8) and  OCAJP Java Standard Edition 11 (OCAJP 11) OCAJP8 comprises of topics like  Creating and Using Arrays Handling Exceptions Java Basics Using Loop Constructs Using Operators and Decision Constructs Working with Inheritance Working with Java Data Types Working with Methods and Encapsulation Working with Selected classes from the Java API OCAJP11 comprises of topics like Applying Encapsulation Creating and Using Methods Creating Simple Java Programs Describing and Using Objects and Classes Handling Exceptions Java Technology and the Java Development Environment Programming Abstractly Through Interfaces Reusing Implementations Through Inheritance Understanding Modules Using Operators and Decision Constructs Working with Java Arrays Working with Java Primitive Data Types and String APIs Demand and Benefits: Having an OCAJP certification verifies that the programmer or the aspirant has all the necessary and essential skills to become an expert Java developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The entry-level salary of a junior Java developer with this certification is $ 3670 per annum; when the candidate gathers two to three years of experience, the average salary hikes to $ 5430 annually.   (Source: Glassdoor) Top companies and industries hiring Oracle Certified Associate Java Programmers are Smart Monitor Pvt. Ltd., Fiserv, Micron Semiconductor Asia Pvt. Ltd., and more. Where to take Training for Certification: KnowledgeHut has a fascinating course, designed for beginners in Java programming. It offers hands-on learning with 40 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to be a Java developer or start his/her career as a Java programmer can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. 2) Oracle Certified Professional Java Programmer OCPJPIt is a professional-level certification program provided by Oracle for Java developers. It verifies the candidates' knowledge and professional expertise. Using this certification, aspirants and other hard-core Java programmers can distinguish themselves from those Java professionals who are not certified. It comes in the second level of Oracle's Java Certification list. There are two subcategories of this certification – OCPJP Java Standard Edition 8 (OCPJP 8) and  OCPJP Java Standard Edition 11 (OCPJP 11) This certification is preferable if someone has professional experience with Java or has already worked for some years in Java technology.  OCPJP8 comprises of topics like: Advanced Class Design Building Database Applications with JDBC Concurrency Exceptions and Assertions Generics and Collections Java Class Design Java File I/O (NIO.2) Java I/O Fundamentals Java Stream API Lambda Built-in Functional Interfaces Localization Use Java SE 8 Date/Time API OCPJP11 comprises of topics like: Annotations Built-in Functional Interfaces Concurrency Database Applications with JDBC Exception Handling and Assertions Functional Interface and Lambda Expressions Generics and Collections I/O (Fundamentals and NIO.2) Java Fundamentals Java Interfaces Java Stream API Lambda Operations on Streams Localization Migration to a Modular Application Parallel Systems Secure Coding in Java SE Application Services in a Modular ApplicationDemand and Benefits: Once you are a certified Professional Java Programmer (OCPJP), you can switch to better salary slabs and organizations that hire senior Java developers. This certification also helps in getting internal promotions as Java developers in different organizations and firms. The average salary of a certified professional Java developer is $ 5300 - $ 8610 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are Oracle, Capgemini, Morgan Stanley, Chetu, Mphasis, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for Java developers and professionals for learning intermediate Java topics. It has hands-on learning with 32 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course. Who should take the Training (roles) for Certification: Any Java programmer who wants to apply for a senior Java developer's role or start his/her career as a Java programmer can opt for this professional certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.3. Oracle Certified Expert - Web Component Developer OCEWCDIt is an intermediate-level course offered by Oracle for Java web developers. The Oracle Certified Expert Web Component Developer is for web developers who want to write web applications using Java. Through this course, they can prove their expertise in developing web apps using JSP and Servlet technologies. It verifies your expertise in Servlet 3.0 and helps in creating dynamic Web content and Web services.  It comprises of topics like Understanding Java EE Architecture Managing Persistence using JPA entities and Bean Validation Implementing business logic using EJBs Using Java Message Service API Implement SOAP Services using JAX-WS and JAXB APIs Creating Java Web Applications using Servlets and JSPs Implementing REST Services using JAX-RS API Creating Java Applications using WebSockets Developing Web Applications using JSFs Securing Java EE 7 Applications Using CDI Beans Demand and Benefits: You can opt for this course once you are a certified Professional Java Programmer (OCPJP) or certified associated Java programmer. This certification course will help you get a job in organizations having rigorous work in Servlet, Java Server Page, JSF, and web microservices. The average salary of a certified professional Java developer is $ 8,850 - $ 11,930 per annum. Top companies and industries hiring Oracle Certified Web Component Developers are Amdocs, IBM, Oracle, Capgemini, SAP, Shine, Byjus, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for Java web developers (. It has hands-on learning with instructor-led online lectures and live projects. Apart from this, you can get online training from Oracle University as wellWho should take the Training (roles) for Certification: Any programmer or computer science aspirant who wants to settle as a Java web developer or start his/her career as a Java web content and web service developer can opt for this certification course. As a prerequisite, you have to pass the OCPJP to opt for this certification.  Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. 4. Oracle Certified Professional Java Application Developer (OCPJAD)It is an advanced-level course offered by Oracle for Java application developers. The Oracle Certified Professional Java Application Developer (OCPJAD) is for software developers who want to write different applications and automation tools using Java. Through this course, developers can prove their expertise and abilities to develop and deploy applications through Java Enterprise Edition 7. OCPJAD is ideal for desktop application developers, frontend + backend app developers, software engineers, and application architects. It comprises of topics like Creating Batch API Developing CDI Beans Concepts of Concurrency Creating Java Applications with Web-Sockets Creating Java Web Applications with JSPs Developing Java Web Applications with Servlets Developing Web Applications with JSFs Implementing Business Logic with EJBs Performing REST Services with JAX-RS API Implementing SOAP Services with JAX-WS and JAXB APIs Java EE 7 system architecture Java EE 7 Security Techniques Java Message Service API Managing Persistence with JPA Entities and Bean-ValidationDemand and Benefits: Once you pass the Certified Professional Java Application Developer (OCPJAD), you can seek employment in organizations that work on critical application development and command higher salaries. This professional certification will give you exposure to develop APIs, implementing business logic using EJBs, create message services, and apply security systems. The average salary of a certified professional application developer is $ 9,800 - $ 13,910 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are Oracle, Capgemini, NetSuite Inc., SAP, Cognizant, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity with hands-on learning exposure and live projects. Apart from this, you can get online training from Oracle University as well. Who should take the Training (roles) for Certification: Any Java developer or full-stack application developer who wants to become a certified Java application developer or move to the specialized sector of API development using REST, security architect or software engineer can opt for this certification course. As a prerequisite, you should have passed the OCAJP certification.  Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.5. Oracle Certified Master Java Enterprise Architect (OCMJEA)Large-scale development and service firms have different critical applications and systems to develop, manage, and maintain. Such systems require full-stack developers and specialized professionals with proven skills. Such organizations and MNCs hire only highly experienced professionals and specialists who can supervise the extensive operation, architect the defects, and define & develop systems as per requirements. The Oracle Certified Master Java Enterprise Architect (OCMJEA) is one of the most prestigious Java certifications a Java developer can achieve.  It comprises of topics like Architect Enterprise Applications through Java EE Developing Applications for the Java EE 6 Developing Applications for the Java EE 7 Developing Applications with Java EE 6 on WebLogic Server 12c Java Design Patterns Java EE 6: Develop Business Components with JMS & EJBs Java EE 6: Develop Database Applications with JPA Java EE 6: Develop Web Services with JAX-WS & JAX-RS Java EE 7: New Features Java SE 7: Develop Rich Client Applications Java SE 8: Programming Java SE 8 Fundamentals Object-Oriented Analysis and Design Using UML, etc. Demand and Benefits: Once you pass the Certified Master Java Enterprise Architect course, you get the essential skills and understanding of how to execute application development on an enterprise level. Such an experienced professional gains full-stack Java development skills. They get hired with the responsibility of undertaking Java projects from the very start to their final delivery. Many Certified Master Java Enterprise Architects work as managers or senior managerial roles in industries and firms. The average salary of a certified professional application developer is $ 14,000 - $ 19,210 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are IBM, Oracle, Microsoft, HCL, Capgemini, NetSuite Inc., SAP, Cognizant, Atlassian, etc. Where to take Training for Certification: KnowledgeHut has a fascinating Java course  with hands-on learning exposure and a live project. Apart from that, a professional can train himself through ILT (Instructor-Led-in-Class), Learning Subscription, TOD (Training on Demand), LVC (Live Virtual Class), or classes delivered by Oracle Authorized Education Center . Other Oracle Authorized Partner Oracle Academy, Oracle University Training Center, or Oracle Workforce Development Program can also benefit and train you in this course.  Who should take the Training (roles) for Certification: Any Java developer or full-stack application developer who wants to move to a senior role in the enterprise-level or want to become a manager or team lead can opt for this certification course. As a prerequisite, you need to have passed the OCPJP certification.  Course fees for Certification:  $248 Application fee for certification: $ 248 Exam fee for certification: $ 248 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. Java is an evergreen programming language and is here to stay, at least for the next couple of decades. A vast community of professionals and entry-level aspirants enjoy the benefit of this pure object-oriented, class-based, multi-paradigm, high-level programming language. Java Certification requires proper training.KnowledgeHut has the required infrastructure and quality education faculty, both online and offline, to train aspirants for these Oracle Certifications. It caters to well-structured, industry-oriented Java certification training, explicitly designed to serve the candidates according to the latest industry needs. Getting proper training from KnowledgeHut will help aspirants master core knowledge of Java plus equip themselves with the industry standards to manage large projects. 
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Top IT Certifications for Java Developers in 2021

Programming languages are at the heart of comput... Read More

Best Python Certifications of 2021

Programming is always at the core of computer science and Information Technology. Every year millions of programmers graduate with a degree and look for opportunities in the job market. The demand for programmers is growing exponentially, and this demand is not going anytime soon. Python was released by Python Software Foundation in 1991, and in just a few years, has become the most popular and widely used programming language in various disciplines.Python is an interpreted, general-purpose, and high-level programming language developed by Guido Van Rossum. Today, companies use Python for GUI and CLI-based software development, web development (server-side), data science, machine learning, AI, robotics, drone systems, developing cyber-security tools, mathematics, system scripting, etc.According to TIOBE index, Python ranks second among all other programming languages. KnowledgeHut has some fascinating advanced-level courses on Python, such as Machine Learning using Python and Artificial Intelligence using Python.Once you gain expertise in writing Python programs, candidates can start learning advanced-level Python libraries and modules such as Pandas, SciPy, NumPy, Matplotlib, etc. There are different options one can explore after learning Python. These are data analysis, machine learning, cybersecurity, automation, web scraping, etc.Top Python Certifications of 2021Certified Entry-Level Python Programmer (PCEP) Certified Associate in Python Programmer (PCAP) Introduction to Programming Using Python by Microsoft Certified Professional in Python Programming 1 & 2 (PCPP 1 & 2) Certified Expert in Python Programming (PEPP) During the course of your Python certification training and exam preparation, you will develop different real-world projects and get familiar with case studies. Also, there will be hands-on lab experiences in Python programming. In this article, you will get to know the top five Python certifications of 2021 that can give you the launchpad you need to embark on a successful career.   1. Certified Entry-Level Python Programmer (PCEP): The PCEP is an entry-level Python certification. To enroll in this course, you need to have a basic understanding of how procedural programming works. Also, some knowledge of flowcharts and algorithm creation will benefit you. Through this certification, an aspirant can gain the core and fundamental understanding of Python. This certification from the Python Institute will make you proficient in Python programming and help you become a Python certified professional. Aspirants and professionals can choose Python as a career option/path and climb the Python Institute’s certification ladder from associate to professional.PCEP comprises of topics like Basic formatting and outputting methods Handling Boolean values Compilation vs. interpretation Constants, Variables and Variable naming conventions Defining user-defined functions Fundamentals of computer programming Inputting and converting Data Logical vs. bitwise operations in Python Looping and control statements Lists New data aggregates: Tuples and Dictionaries The assignment operator Primary kinds of data and numerical operators Rules governing the building of expressions Working with multi-dimensional arrays Different slicing operations Demand and Benefits: Having a PCEP certification verifies that the programmer or the aspirant has knowledge of all the necessary and fundamental Python concepts. The course also covers all the syntax and semantics of different Python constructs & data types offered by the language. This course brings crisp knowledge on general coding techniques using standard language infrastructure and basic programming skills using Python. The average entry-level salary of a Python programmer with this certification will be $ 5660 per annum. Top companies and industries hiring PCEP are Philips, Cataleya Pvt. Ltd., Deloitte, Zynga, Mphasis, VMware, etc.Where to take Training for Certification: Python Institute has all the study resources you need to prepare for this examination. Apart from that, you can join the Python course offered by KnowledgeHut  that has 24 hours of instructor-led training covering the core programming concepts like operators, control flow, functions, syntax & indentations. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant, who wants to learn Python or start an internship or entry-level job as Python programmer can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 295 Application fee for certification: $ 295 Exam fee for certification: $ 295 Retake fee for certification: If a candidate fails the exam, he/she can wait 15 days before being allowed to retake the exam for free. There is no limit on the number of times a candidate may retake the exam.2. Certified Associate in Python Programmer (PCAP):PCAP is another important second-level or associate-level certification exam for Python. This course and certification will give you the confidence to measure your skill and complete the Python-based coding tasks. It also facilitates competing for competitive coding sessions. This course also comprises the essential notions and concepts related to object-oriented programming. With this associated-level certification, you can stand unique in the competitive job market. PCAP comprises of topics like Basic formatting and outputting methods Python basics Using Boolean values Compilation vs. interpretation Variables and variable naming conventions Defining and using functions Fundamentals of computer programming Fundamentals of OOP  How to use OOPs in the Python programming language Generators and closures Inputting and converting of data Logical vs. bitwise operations Looping and control statements File processing for Python developers Name scope issues New data aggregates: tuples and dictionaries Primary kinds of data and numerical operators Python modules Inheritance in Python Rules for creating expressions Working with multi-dimensional arrays Strings, lists, and other Python data structures The assignment operator The concept of exceptions and implementation Demand and Benefits: Having a PCAP certification verifies that the programmer or the aspirant has all the necessary and essential concepts of intermediate-level Python programming. The course also covers all the fundamental concepts of different Python constructs & fundamentals of OOP. This course brings crisp knowledge on general coding techniques using standard language infrastructure and basic programming skills using Python. The approximate salary of a Python programmer with this certification will be $7000 to $11,262 per annum. Top companies and industries hiring PCAP are CareCentrix, Accenture, Deutsche Bank, Collabera, NetApp, Capgemini, Tech Mahindra, Myntra, etc. Where to take Training for Certification: Python Institute (https://pythoninstitute.org/free-python-courses/) has all the study resources you need to prepare for this examination. You can also get a comprehensive training by enrolling for the Python course offered by KnowledgeHut (https://www.knowledgehut.com/programming/python-programming-certification-training) that has 24 hours of instructor-led training covering the core programming concepts like operators, control flow, functions, syntax & indentations. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant, who wants to build a career in Python or pursue an associate-level job as a Python programmer or developer, can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 295 Application fee for certification: $ 295 Exam fee for certification: $ 295 Retake fee for certification: If a candidate fails the exam, he/she can wait 15 days before being allowed to retake the exam for free. There is no limit to the number of times a candidate may retake an exam. 3. Introduction to Programming Using Python by MicrosoftIt is another popular entry-level Python certification by Microsoft (https://docs.microsoft.com/en-us/learn/certifications/exams/98-381). This certification covers all the syntax, data types, and basic understanding of Python. It also teaches how to logically solve any problem using Python constructs. Candidates wanting to enroll for this course are expected to have had some instruction or hands-on experience of approximately 100 hours with the Python programming language, including debugging skills, logic development, understanding conditional & decision-making statements, and maintaining well-formed well documented Python code. Microsoft’s Introduction to Programming Using Python comprises of topics like Basics of Python Using Boolean values Fundamentals of computer programming Interpretations Variables and variable naming conventions Defining and using functions Indexing and slicing operations Type conversions Basic formatting and outputting Data Types and Operators Control Flow with Decisions and Loops Construct Data structures Jump Statements Perform Input and Output Operations Document and Structure Code Comments and white-spaces Perform Operations Using Modules and Tools Demand and Benefits: Having a Microsoft certification verifies that the Python programmer or the aspirant has all the necessary and fundamental Python concepts. The course also covers all the syntax and semantics of different Python constructs & data types offered by the language. Anyone with this certification will have a better understanding of core Python, and the candidate can stand out in the competitive exams from the rest. The average entry-level salary of a Python programmer with this certification will be $ 5660 per annum. Top companies and industries hiring Python professionals with this credential are Cataleya Pvt. Ltd., Zynga, VMware, Mphasis, Deloitte, Capgemini, etc. Where to take Training for Certification: Microsoft has a paid five-day instructor-led course to prepare for this examination. Apart from that, you can join the Python course offered by KnowledgeHut that has 24 hours of instructor-led training covering the core programming concepts like operators, control flow, functions, syntax & indentations. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant, who wants to learn Python or start an internship or entry-level job as Python programmer, can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 127 Application fee for certification: $ 127 Exam fee for certification: $ 127 Retake fee for certification: Exam retake is free. If the candidate fails to achieve a passing score on the first attempt, he/she must wait 24 hours before retaking the exam. 4. Certified Professional in Python Programming 1 & 2 (PCPP 1 & 2):Once you sound knowledge of the core concepts of Python or have 3 to 5 years of experience in Python programming, you may prepare for professional Python certification. Certified Professional in Python Programming 1 certifications will reflect your experience and programming skills in the following areas: Text File Handling GUI-based Programming Encapsulation Inheritance Advanced Object-Oriented Programming PEP conventions Metaprogramming Communicating with a program's environment Using Libraries and Modules Importing math, science, and engineering modules Having this globally recognized credential will make you stand out in a competitive job market. Many recruiting agencies and firms are looking for professional Python programmers who can develop and deploy applications. Certified Professional in Python Programming 2 (PCPP2) is another advanced-level professional certification course offering proficiency in Python-MySQL database handling. Certified Professional in Python Programming 2 certification will reflect your experience and programming skills in the following areas: Basic directory structure CRUD operations Design patterns Observer and Proxy Singleton and State Design Template Method Model-View-Controller using Python Multiprocessing, threading, subprocess, and multiprocessor synchronization Relational database management using Python MySQL and SQL commands Sharing, storing and installing packages Network programming in Python Application testing techniques and principles Demand and Benefits: Having a PCPP certification verifies that the Python developer has all the necessary and essential skills of a professional Python programmer. The course covers all the advanced object-oriented programming concepts, GUI programming, etc. This course brings crisp knowledge for experienced professionals to make them stand out in the software development industry. The approximate salary of a Python programmer with this certification will be $ 12,053 to $ 14,700 per annum. Top companies and industries hiring PCPP certified professionals are Dell, Accenture, SG Analytics, HCL, Oracle, Capgemini, Tech Mahindra, Flipkart, etc. Where to take Training for Certification: Python Institute has all the study resources you need to prepare for this examination. Apart from that, you can join the Python course offered by KnowledgeHut that has 32 hours of instructor-led training covering the advanced programming concepts like database handling, OOPs, logical layout, data visualization, etc. Who should take the Training (roles) for Certification: Any professional, programmer, or experienced Python developer - who wants to settle as a senior Python developer or pursue an experienced-level job as a Python programmer or developer can opt for this certification course. The candidate should have the Certified Associate in Python Programmer (PCAP) certification or few years of work experience in Python. Course fees for Certification: $ 195 Application fee for certification: $ 195 Exam fee for certification: $ 195  Retake fee for certification: If a candidate fails the exam, he/she can wait 15 days before being allowed to retake the exam for free. There is no limit to the number of times a candidate may retake an exam. 5. Certified Expert in Python Programming (CEPP):This Python certification tag is for experts who complete all the OpenEDG Python Institute's Programming certification program (PCAP-31-xx, PCPP-32-1-xx, and PCPP-32-2-xx exams). It is the most advanced credential a Python developer can achieve from the Python Institute. Having this globally recognized credential will verify your expertise in Python programming. It highlights expertise in the universal concepts of Python programming. Also, this certification showcases the skills in resolving typical implementation challenges on different verticals of Python. Demand and Benefits: Having a CEPP certification verifies that the Python developer has industry level expertise in Python. This certification designates that the candidate has covered all the topics from basics to advance object-oriented programming concepts, GUI programming, etc. Using this certification, one can apply for a senior software development role, Python developer’s role, team lead, agile project management lead, and other senior job roles. Many professionals switch their careers to Big Data, Data Analytics, Machine learning, and deep learning after completing this certification. The approximate salary of a Python programmer with this certification will be $ 17,350 to $ 39,945 per annum. Top companies and industries hiring CEPPs are Amazon, Tesla, HSBC, Google, HCL, Oracle, Capgemini, Qualcomm, 6sense, Vitrana, and other top service-based companies. Where to take Training for Certification: Python Institute has all the study resources you need to prepare for PCAP-31-xx, PCPP-32-1-xx, and PCPP-32-2-xx examination. Once a candidate has passed all the certifications, he/she becomes recognized as an Open EDG Python Institute Certified Expert in Python Programming (CEPP). Who should take the Training (roles) for Certification: Any professional, Python expert, or senior Python developer, who wants to settle as a team lead or pursue an experienced-level job profile can opt for these certifications to reach at this level.  Course fees for Certification: $ 295 + $ 295 + $ 195 Application fee for certification: $ 295 + $ 295 + $ 195 Exam fee for certification: $ 295 + $ 295 + $ 195  Retake fee for certification: There is no retake fee Conclusion We trust this article gave you a better insight into different Python certifications! Whether you are starting out as a coder, or are an experienced Python programmer looking at making a splash in the industry, having a Python certification and proper knowledge of Python will elevate your programming career. Python is one of the top programming languages that can help you land different jobs in web development, app development, data science, cybersecurity, networking, web scraping, robotics, IoT, etc. If you aren't sure which online resource will be more informative for your Python certification, KnowledgeHut (https://www.knowledgehut.com/) has all the study materials and expert trainers who will help you reach the pinnacle of Python expertise. Receiving a Python certification, apart from academics and degrees, will make you stand out from the rest. So, start preparing for one today! 
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Best Python Certifications of 2021

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