## Python Map Vs List Comprehension

Python has many inbuilt methods, data structures, and modules to get the desired output. When we write a huge code consisting of many lines for a detailed problem statement, it is difficult to debug the code. Python provides the features we need to write code with a fewer number of lines.  Python Map and Python list comprehension are features that work differently but have some similarities. Their perfomance varies with the parameters we are using. Let's discuss what these maps and list comprehension features are, how they work, and their performances. Map function:-  The map function takes an Expression and an Iterable. The output will be an Iterable object where the expression will work on each element of the given Iterable. The output of each expression will be an element of the resultant Iterable.  Syntax  map( expression, iterable) Parameters  It accepts two parameters as arguments the "expression maps "and  "iterable". expression- It is the formula you need to calculate the result. Iterable- It is the iterable whose each element will be calculated on from the expression. Return type  It returns an object of the iterable where the given formula calculates each element (as of 1st argument) applied on the elements of given iterable (2nd argument) of the map function. The output can be converted to an iterable. Example Example 1: Map ( ) using the function as the first argument. #This code will add "2" to each element of the list  def add_two(n):  #parameter is "n"   return n + 2             #add "2" to "n"  #application of formula "add_two( )" on every element of "num"  num = [1, 2, 3, 4] #2nd argument  added_two = map(add_two, num)      #returns "added_two" as object  print(list(added_two))       #object "added_two" is converted to list. #Output: added_two = [3, 4, 5, 6] Example 2: Using Lambda expression as first argument  # Double up all numbers through map and lambda   num = [1, 2, 3, 4] #input list, 2nd argument  added_two = map(lambda i: i + 2, num)      #output list object  print(list(result))        #object is converted to list Iterable  #Output: added_two = [3, 4, 5, 6] map vs. for loop python speed  Map and For Loop can produce the same output, but they work differently. Inside "for loop", we iterate a variable fed to an expression within an Iterable. Whereas "Map" is used to map a function to all the elements of the given Iterable. It is a one-lined code configuration of "for loop". So, if we give the same input and write code to get the same output using "map" and "for loop", we will see from the time of execution on the IDE that their speed of execution is different. Run the below codes in your Python IDE and you will see that "map" is faster than "for loop". So, in map vs for loop python speed , map wins. Example solution with "map" :  #definition of function to cube a given number   def numCube (n) :   return n * n * n   num = [0, 1, 2, 3, 4]   cubedNum = map(cubedNum, num)  #object of iterable  print(list(cubedNum)) #object is converted to Iterable list #output:         [0, 2, 8, 27, 64] Example solution with "for loop" :  num = [0, 1, 2, 3, 4]  #input list  for n in num :   cubedNum = n * n * n         #expression  print(cubedNum)  #output:      #         0  #         1  #         8  #         27  #         64 List Comprehension  Python List Comprehension is used for creating a list where each element is generated by applying a simple formula on the given list.  Syntax  resultant_list = [ <variable_expression> for <variable> in <input_list> ]                or resultant_list = [ <variable_expression> for <variable> in <input_list> if <condition> ]  Parameters  It takes two argument including a list. variable_expression- It is the formula containing a variable and the formula is used on the variable. variable- It is a variable to access each element.  input_list- It is the given list from where each element will be accessed as variable and applied on the formula.  condition- After calculating the expression, if it matches the given Cindy, then only it will appear in the resultant_list. Return type  It always returns a list. Example  Example 1: Python List comprehension without condition # a list comprehension to print cube  num= [-2, -1, 0, 1, 2]  cubedNum = [n**3 for n in num]   print(cubedNum)  #output:      [ -8, -1, 0, 1, 8]  Example 2: Python List comprehension with condition # a list comprehension to print cube if cube is positive  num= [-2, -1, 0, 1, 2]  cubedNum = [n**3 for n in num if n**3 >= 0]   print(cubedNum)      #output list contains only positive cubes #output:  [ 0, 1, 8]  python any list comprehension (use of "any" keyword) The "any ( )" function is very useful tool which works on iterables, and returns true if any of the element in the iterable is true. As list compression generates a list, we can use any() function with a python list comprehension.  Example: Print "not allowed" if any one of the ages is less than 18 ages= [22, 15, 20, 25, 34]   if any([a < 18 for a in ages]):  print("At least one is under-aged and they all are not allowed")  # there is one age 15 which is less than 18  #output:.    At least one is under-aged, and they all are not allowed python reduce vs list comprehension Reduce- Used to reduce the list where a formula is applied recursively on the elements of a list. It accepts two arguments, including expression as the first argument and Iterable as the second argument. This function can be defined in the "functools" module.  Firstly it took the first two elements of the Iterable and applied them to the expression. The calculated result and next element are applied to the expression. It is traversed till the last element of the Iterable, and the final output is thus generated.  Example 1: Reduce function using lambda #importing functools module where reduce( ) is defined  import functools    num = [ 1 , 2, 5, 3, 9 ]   #summation of elements using functools( )  print("The sum of all list elements is : ")   print(functools.reduce(lambda a,b : a+b, num))  #output: The sum of all list elements is: 20  How to reduce function works?  Step 1: 1 + 2 = 3   Step 2: 3 + 5 = 8   Step 3: 8 + 3 = 11   Step 4: 11 + 9 = 20  Example 2: Reduce function using operator module #importing functools module where reduce( ) is defined  import functools     #importing operator module where numerical operators are defined as function  import operator    num = [ 1 , 2, 5, 3, 9 ]   print ("The sum of the list elements is : ")   # "add" function is used as '+' operator  print (functools.reduce(operator.add, num))  #output: The sum of all list elements is : 20  How does reduce function with operator work? Step 1:    1 add 2 = 3   Step 2:      3 add 5 = 8   Step 3:      8 add 3 = 11   Step 4:       11 add 9 = 20  Python List comprehension- Used to create another list where a formula is applied once on each element of the list. So, we cannot use reduce in place of list comprehension.  We have seen no possible answer to python reduce vs list comprehension because these two can replace each other. Python filter vs list comprehension In python, a filter is a function that is used to filter out some elements from Iterable based on a certain condition. It returns an Iterable object. We can visualize that after printing it, and converting it to a list. It can be done by using list comprehension to create a list with the "if" condition.  When we are using a list with a small number of elements, then the list comprehension and filter method have almost the same execution speed. But when the list size is huge, then list comprehension is slightly faster than a filter. This is because list comprehension directly generates a list, whereas filter function returns an iterable object, which is then converted to a list. However, if we don't convert the object to a list, then the execution time is almost negligible. But to visualize the output, we need a list, not an object.  So, python list comprehension is better than a filter. Example 1: Using list comprehension to print positive numbers from a list. num= [-2, -1, 0, 1, 2]  positive Num = [n for n in num if n > 0]   print(positiveNum)      #output list contains only positive cubes #output: [ 1, 2]  Example 2: Using filter function to print positive numbers from a list. num= [-2, -1, 0, 1, 2]  print(list(filter(lambda n: n>0, num))) # output:    [1, 2] So, in python filter vs. list comprehension, list comprehension has better performance. Python Map VS List Comprehension  Assume that we have a formula. We want to apply that formula to each element of a list and thereby create a new list. Now, using for loop, we can traverse through that given list, pick each item, apply them into the formula and append it to the resultant list. That is a long process that consists of many lines of code. So, in one line, we can use the map function as discussed above.  Now, python list comprehension is a tool for creating a new list. We can use the functionality of lambda expression, filter method, and map function together using only this list comprehension.  List comprehension has a simpler configuration than the map function.  List comprehension can be used together with if condition as replacement of filter method. Map function has no such functionality. However, we can feed the map function output to the filter function.  List comprehension returns a list, whereas the map function returns an object of Iterable.  List comprehension execution is faster than that of map function when the formula expression is huge and complex. Map function is faster than list comprehension when the formula is already defined as a function earlier. So, that map function is used without lambda expression. Comparing Execution Time  Now to examine the execution performance of list comprehension and map function, we will import a module "timeit" to check the execution time. Let us see the result with a variety of methods. Without lambda: Map is faster than List Comprehension when function is already defined in case of map function. Example code 1: This code will print the time taken to evaluate numbers from 1 to 50. Map function is used without lambda. import timeit  # list comprehension  l1 = timeit.timeit( '[ l for l in range(50)]' , number = 999999)  print (l)   #map function  f= 'def num( ) : print (n)'  m1 = timeit.timeit( ' map (num, range(50))' , number = 999999, setup = f )   print (m) With lambda in map: List comprehension is better than map function when we don't define the function beforehand and use lambda expression inside map.   Example code 2: This code will print the time taken to add a number to itself and this is applied for each element of the list. The expression is defined as lambda expression. import timeit  # list comprehension  l2 = timeit.timeit( '[ n+n for n in range(50)]' , number = 999999)  print (l)  #map function  m2 = timeit.timeit( ' map (lambda a: a+a, range(50))' , number = 999999, setup = f )   print (m) Write the above codes on your IDE. You will see that for the first case, m1 is very less than l1. That means the map works faster than list comprehension. For the second case, m2 is greater than l2, implying that list comprehension is faster than map function when map function is used with Lambda expression.  ConclusionThere are no clear answers about which is the better option, in Python Map Vs List comprehension. We should know what the problem statement is, then use the method which suits us better because our main objective is to calculate the solution with optimal time and space consumption.

# Python Map Vs List Comprehension

8K

Python has many inbuilt methods, data structures, and modules to get the desired output. When we write a huge code consisting of many lines for a detailed problem statement, it is difficult to debug the code. Python provides the features we need to write code with a fewer number of lines.

Python Map and Python list comprehension are features that work differently but have some similarities. Their perfomance varies with the parameters we are using. Let's discuss what these maps and list comprehension features are, how they work, and their performances.

## Map function:-

The map function takes an Expression and an IterableThe output will be an Iterable object where the expression will work on each element of the given Iterable. The output of each expression will be an element of the resultant Iterable.

Syntax

map( expression, iterable)

Parameters

It accepts two parameters as arguments the "expression maps "and  "iterable".

• expression- It is the formula you need to calculate the result.
• Iterable- It is the iterable whose each element will be calculated on from the expression.

Return type

It returns an object of the iterable where the given formula calculates each element (as of 1st argument) applied on the elements of given iterable (2nd argument) of the map function. The output can be converted to an iterable.

Example

Example 1: Map ( ) using the function as the first argument.

#This code will add "2" to each element of the list
def add_two(n):  #parameter is "n"
return n + 2             #add "2" to "n"
#application of formula "add_two( )" on every element of "num"
num = [1, 2, 3, 4] #2nd argument
added_two = map(add_two, num)      #returns "added_two" as object
print(list(added_two))       #object "added_two" is converted to list.

#Output:

added_two = [3, 4, 5, 6]

Example 2: Using Lambda expression as first argumen

# Double up all numbers through map and lambda
num = [1, 2, 3, 4] #input list, 2nd argument
added_two = map(lambda i: i + 2, num)      #output list object
print(list(result))        #object is converted to list Iterable  

#Output:

added_two = [3, 4, 5, 6]

## map vs. for loop python speed

Map and For Loop can produce the same output, but they work differently. Inside "for loop", we iterate a variable fed to an expression within an Iterable. Whereas "Map" is used to map a function to all the elements of the given Iterable. It is a one-lined code configuration of "for loop".

So, if we give the same input and write code to get the same output using "map" and "for loop", we will see from the time of execution on the IDE that their speed of execution is different. Run the below codes in your Python IDE and you will see that "map" is faster than "for loop".

So, in map vs for loop python speed , map wins.

Example solution with "map" :

#definition of function to cube a given number
def numCube (n) :
return n * n * n
num = [0, 1, 2, 3, 4]
cubedNum = map(cubedNum, num)  #object of iterable
print(list(cubedNum)) #object is converted to Iterable list 

#output:

[0, 2, 8, 27, 64]

Example solution with "for loop" :

num = [0, 1, 2, 3, 4]  #input list
for n in num :
cubedNum = n * n * n         #expression
print(cubedNum)  

#output:

#         0
#         1
#         8
#         27
#         64 

## List Comprehension

Python List Comprehension is used for creating a list where each element is generated by applying a simple formula on the given list.

Syntax

resultant_list = [ <variable_expression> for <variable> in <input_list> ]

or

resultant_list = [ <variable_expression> for <variable> in <input_list> if <condition> ]

Parameters

It takes two argument including a list.

• variable_expressionIt is the formula containing a variable and the formula is used on the variable.
• variable- It is a variable to access each element.
• input_list- It is the given list from where each element will be accessed as variable and applied on the formula.
• condition- After calculating the expression, if it matches the given Cindy, then only it will appear in the resultant_list.

Return type

It always returns a list.

Example

Example 1: Python List comprehension without condition

# a list comprehension to print cube
num= [-2, -1, 0, 1, 2]
cubedNum = [n**3 for n in num]
print(cubedNum)  

#output:

[ -8, -1, 0, 1, 8]

Example 2: Python List comprehension with condition

# a list comprehension to print cube if cube is positive
num= [-2, -1, 0, 1, 2]
cubedNum = [n**3 for n in num if n**3 >= 0]
print(cubedNum)      #output list contains only positive cubes 

#output:

 [ 0, 1, 8]

## python any list comprehension (use of "any" keyword)

The "any ( )" function is very useful tool which works on iterables, and returns true if any of the element in the iterable is true. As list compression generates a list, we can use any() function with a python list comprehension

ExamplePrint "not allowed" if any one of the ages is less than 18

ages= [22, 15, 20, 25, 34]
if any([a < 18 for a in ages]):
print("At least one is under-aged and they all are not allowed")
# there is one age 15 which is less than 18  

#output:.

At least one is under-aged, and they all are not allowed

## python reduce vs list comprehension

Reduce- Used to reduce the list where a formula is applied recursively on the elements of a list. It accepts two arguments, including expression as the first argument and Iterable as the second argument. This function can be defined in the "functools" module.

Firstly it took the first two elements of the Iterable and applied them to the expression. The calculated result and next element are applied to the expression. It is traversed till the last element of the Iterable, and the final output is thus generated.

Example 1: Reduce function using lambda

#importing functools module where reduce( ) is defined
import functools
num = [ 1 , 2, 5, 3, 9 ]
#summation of elements using functools( )
print("The sum of all list elements is : ")
print(functools.reduce(lambda a,b : a+b, num))  

#output:

The sum of all list elements is: 20

How to reduce function works?

Step 1: 1 + 2 = 3
Step 2: 3 + 5 = 8
Step 3: 8 + 3 = 11
Step 4: 11 + 9 = 20  

Example 2: Reduce function using operator module

#importing functools module where reduce( ) is defined
import functools
#importing operator module where numerical operators are defined as function
import operator
num = [ 1 , 2, 5, 3, 9 ]
print ("The sum of the list elements is : ")
# "add" function is used as '+' operator
print (functools.reduce(operator.add, num))  

#output:

The sum of all list elements is : 20

How does reduce function with operator work?

Step 1:    1 add 2 = 3
Step 2:      3 add 5 = 8
Step 3:      8 add 3 = 11
Step 4:       11 add 9 = 20  

Python List comprehensionUsed to create another list where a formula is applied once on each element of the list. So, we cannot use reduce in place of list comprehension.

We have seen no possible answer to python reduce vs list comprehension because these two can replace each other.

## Python filter vs list comprehension

In python, a filter is a function that is used to filter out some elements from Iterable based on a certain condition. It returns an Iterable object. We can visualize that after printing it, and converting it to a list. It can be done by using list comprehension to create a list with the "if" condition.

When we are using a list with a small number of elements, then the list comprehension and filter method have almost the same execution speed. But when the list size is huge, then list comprehension is slightly faster than a filter. This is because list comprehension directly generates list, whereas filter function returns an iterable object, which is then converted to list. However, if we don't convert the object to a list, then the execution time is almost negligible. But to visualize the output, we need a list, not an object.

So, python list comprehension is better than a filter.

Example 1: Using list comprehension to print positive numbers from a list.

num= [-2, -1, 0, 1, 2]
positive Num = [n for n in num if n > 0]
print(positiveNum)      #output list contains only positive cubes 

#output:

[ 1, 2]

Example 2: Using filter function to print positive numbers from a list.

num= [-2, -1, 0, 1, 2]
print(list(filter(lambda n: n>0, num))) 

# output:

[1, 2]

So, in python filter vs. list comprehension, list comprehension has better performance.

## Python Map VS List Comprehension

Assume that we have a formula. We want to apply that formula to each element of a list and thereby create a new list. Now, using for loop, we can traverse through that given list, pick each item, apply them into the formula and append it to the resultant list. That is a long process that consists of many lines of code. So, in one line, we can use the map function as discussed above.

Now, python list comprehension is a tool for creating a new list. We can use the functionality of lambda expression, filter method, and map function together using only this list comprehension.

• List comprehension has a simpler configuration than the map function.
• List comprehension can be used together with if condition as replacement of filter method. Map function has no such functionality. However, we can feed the map function output to the filter function.
• List comprehension returns a list, whereas the map function returns an object of Iterable.
• List comprehension execution is faster than that of map function when the formula expression is huge and complex.
• Map function is faster than list comprehension when the formula is already defined as a function earlier. So, that map function is used without lambda expression.

### Comparing Execution Time

Now to examine the execution performance of list comprehension and map function, we will import a module "timeit" to check the execution time. Let us see the result with variety of methods.

Without lambda: Map is faster than List Comprehension when function is already defined in case of map function.

Example code 1: This code will print the time taken to evaluate numbers from 1 to 50. Map function is used without lambda.

import timeit
# list comprehension
l1 = timeit.timeit( '[ l for l in range(50)]' , number = 999999)
print (l)
#map function
f= 'def num( ) : print (n)'
m1 = timeit.timeit( ' map (num, range(50))' , number = 999999, setup = f )
print (m) 

With lambda in map: List comprehension is better than map function when we don't define the function beforehand and use lambda expression inside map.

Example code 2: This code will print the time taken to add a number to itself and this is applied for each element of the list. The expression is defined as lambda expression.

import timeit
# list comprehension
l2 = timeit.timeit( '[ n+n for n in range(50)]' , number = 999999)
print (l)
#map function
m2 = timeit.timeit( ' map (lambda a: a+a, range(50))' , number = 999999, setup = f )
print (m) 

Write the above codes on your IDE. You will see that for the first case, m1 is very less than l1. That means the map works faster than list comprehension. For the second case, m2 is greater than l2, implying that list comprehension is faster than map function when map function is used with Lambda expression.

Conclusion

There are no clear answers about which is the better option, in Python Map Vs List comprehension. We should know what the problem statement is, then use the method which suits us better because our main objective is to calculate the solution with optimal time and space consumption.

### Abhresh Sugandhi

Author

Abhresh is specialized as a corporate trainer, He has a decade of experience in technical training blended with virtual webinars and instructor-led session created courses, tutorials, and articles for organizations. He is also the founder of Nikasio.com, which offers multiple services in technical training, project consulting, content development, etc.

## Join the Discussion

Your email address will not be published. Required fields are marked *

## Top 12 Python Packages for Machine Learning

Lovers of vintage movies would have definitely heard of the Monty Python series. The programming language that it inspired continues to remain among the most popular languages. Guess why Python has consistently topped the charts of the most popular programming languages? Because of its rich environment of libraries and tools, its easy code readability and the fact that it is so easy to pick up.  You name the domain, and you will get Python libraries available, to help you out in solving problems. Right from Artificial Intelligence, Data Science, Machine Learning, Image Processing, Speech Recognition, Computer Vision and more, Python has numerous uses. These libraries and frameworks are open source and can be easily integrated with the development environment that one has.These software frameworks, the platforms which provides necessary libraries and code components, are backbones for devloping applications. Read on to see which are the top ML frameworks and libraries in Python.1. Numpy As the name implies, this is the library which supports numerical calculations and tasks. It supports array operations and basic mathematical functions on the array and other data types of Python. The basic data type of this library is ndArray object.   Numpy has many advantagesThe base data structure is N –Dimensional array. Rich functions to handle the N-dimensional array effectively. Supports integration of C, C++ and other language code fragments. Supports many functions related to linear algebra, random numbers, transforms, statistics etc. DisadvantagesNo GPU and TPU support. Cannot automatically calculate the derivatives which is required in all ML algorithms. Numpy performance goes down when high complex calculations are required. 2. PandasThis is the most useful library for data preprocessing and preparing the data for the Machine Learning algorithms. The data from various files like CSV, Excel, Data etc. can be easily read using Pandas. The data is available in a spreadsheet like area, which makes processing easy. There are three basic data structures at the core of Pandas library: Series - One-dimensional array like object containing data and label (or index). Dataframe - Spreadsheet-like data structure containing an order collection of columns. It has both a row and column index. Panel – Collection of dataframes but rarely used data structure. AdvantagesStructured data can be read easily. Great tool for handling of data. Strong functions for manipulation and preprocessing of data. Data Exploration functions help in better understanding data. Data preprocessing capabilities help in making data ready for the application of ML algorithms. Basic Plotting functions are provided for visualization of data.  Datasets can be easily joined or merged. The functions of Pandas are optimized for large datasets. DisadvantagesGetting to know the Pandas functionalities is time consuming. The syntax is complex when multiple operations are required. Support for 3D metrics is poor. Proper documentation is not available for study. 3. MatplotlibMatplotlib is an important Python library which helps in data visualization. Understanding the data is very important for a data scientist before devising any machine learning based model. This library helps in understanding the data in a visual way. Data can be visualized using various graphical methods like line graph, bar graph, pie chart etc. This is a 2D visualization library with numerous ways of visualizing data. Image SourceAdvantagesSimple and easy to learn for beginners. Integrated with Pandas for visualization of data in effective way. Various plots are provided for better understanding of data like Bar Chart, Stacked Bar chart, Pie chart, Scatter Plot etc. Forms a base for many advanced plotting libraries. Supports storing of the various graphs as images so that they can be integrated with other applications. Can plot timeseries data (with date) very easily. DisadvantagesComplex Syntax for plotting simple graphs. The code becomes lengthy and complex for visualizations. Support for plotting of categorial data is not provided. It is a 2D visualization library. When multiple fields are required to be plotted and visualized effectively, matplotlib code can become lengthy. Managing multiple figures is difficult. 4. Seaborn Visualizations are made simpler and more advanced with the help of Seaborn library. The base for Seaborn is Matplotlib. It is a boon for programmers as statistical visualizations are simplified. Image sourceAdvantagesBest high-level interface for drawing statistical graphics. Provides support for plotting of categorial data effectively. The library provides default themes and many visualization patterns. Multiple figures are automatically created. The syntax is very simple and compact. There are many methods to integrate with Pandas dataframe, making this library most useful for visualization. DisadvantagesMemory issues due to creation of multiple figures. Less customizable and flexible as compared to Matplotlib. Scalability issues. 5. Scipy   Scipy is a Scientific Python library based on Numpy. It has functions which are best suitable for Mathematics, Science and Engineering. Many libraries are provided for Image and Signal Processing, Fourier Transform, Linear Algebra, Integration and Optimization. The functions are useful for ML algorithms and programs. AdvantagesThe base library is Numpy. Many ML related functions are provided like Linear Algebra, Optimization, Compressed Sparce Data Structure etc. Useful Linear Algebra functions are available which are required for implementation of ML related algorithms. The functions can be applied with Pandas Dataframe directly. DisadvantagesComplex functions are available and domain knowledge is needed to understand and implement these functions. There are performance issues when data size increases. Many other effective alternative libraries are available with the needed functionality. 6. Scikit-Learn Scikit-Learn is a useful open access library for use to Python developers. It is an extensive and popular library with many Machine Learning Supervised and Unsupervised algorithms implemented. These algorithms can be fine-tuned with the help of hyperparameters. This library contains many useful functions for preprocessing of data, useful metrics to measure performance of algorithms and optimization techniques.  AdvantagesIt is a general Machine Learning library built on top of Numpy, Pandas and Matplotlib. Simple to understand and use even for novice programmers. Useful Machine Learning Algorithms, both Supervised and Unsupervised, are implemented. Popular library for doing Machine Learning related tasks. Rich in Data Preprocessing and Data Sampling functions and techniques. Plethora of evaluation measures implemented to track the performance of algorithms. Very effective for quick coding and building Machine Learning Models. DisadvantagesScikit learn, as is based on Numpy, requires additional support to run on GTP and TPU Performance is an issue with size of data. Best suitable for basic Machine Learning applications. This library may be useful if one wants to write easy code, but it’s not the best choice for more detailed learning. 7. NLTK Natural Language processing is a great field of study for developers who like to research and challenge themselves. This library provides a base for Natural Language processing by providing simple functionalities to work with and understand languages.AdvantagesVery simple to use for processing natural language data. Many basic functionalities like tokenizing the words, removal of stop words, conversion to word vectors etc. are provided which forms the basis to start with natural language processing models. It is an amazing library to play with natural language using Python. It has more than 50 trained models and lexical resources like wordnet available for use. Rich discussion forums and many examples are available to discuss how to use this library effectively. DisadvantagesIt is based on string processing, which itself has many limitations. Slower as compared to other Natural Language processing libraries like Spacy.8. Keras Keras is a library written in Python for Neural Network programming. It offers a very simple interface to code the neural network and related algorithms. It is an incredibly popular library for Deep Learning algorithms, models and applications and can also be combined with various deep learning frameworks. It provides support for GPU and TPU computation of algorithms. The API provided is simple, same as Scikit-learn. Keras is totally based on Models and Graphs. A model has Input, output and intermediate layers to perform the various tasks as per requirement. Effective functionalities and models provided to code deep learning algorithms like Neural Network, Recurrent Neural Network, Long Short-Term memory, Autoencoders etc. Allows to create products easily supporting multiple backends Supports multi-platform use. Can be used with TensorFlow, can be used in browser using web based keras and provides native ML support for iPhone app development. 9. TensorFlow TensorFlow is the talk of the town because of its capabilities suitable for Machine Learning and Deep Learning models. It is one of the best, and most popular frameworks, adopted by companies around the world for Machine Learning and Deep Learning. Its support for Web as well as Mobile application coupled with Deep Learning models has made it popular among engineers and researchers. Many giants like IBM, Dropbox, Nvidia etc. use TensorFlow for creating and deploying Machine Learning Models. This library has many applications like image recognition, video analysis, speech recognition, Natural Language Processing, Recommendation System etc. TensorFlow lite and TensorFlow JS has made it more popular for web applications and Mobile Applications. Advantages Developed by Google, it is one of the best deep learning frameworks. Simple Machine Learning tasks are also supported in TensorFlow. Supports many famous libraries like scikit learn, Keras etc. which are part of TensorFlow. The basic unit is Tensor which is an n-dimensional array. The basic derivatives are inherently computed which helps in developing many Machine learning Models easily. The models developed are supported on CPT, TPU and GPU. Tensorboard is the effective tool for data visualization. Many other supported tools are available to facilitate Web Development, App Development and IoT Applications using Machine Learning. Disadvantages Understanding Tensor and computational graphs is tedious. Computational graphs make the code complex and sometimes face performance problems. 10. Pytorch A popular Python framework, Pytorch supports machine learning and deep learning algorithms and is a scientific computing framework. This is a framework which is widely used by Twitter, Google and Facebook. The library supports complex Tensor computations and is used to construct deep neural networks. AdvantagesThe power of Pytorch lies in construction of Deep Neural Networks. Rich functions and utilities are provided to construct and use Neural Networks. Powerful when it comes to creation of production ready models. It supports GPU operations with rich math-based library functions. Unlike Numpy, it provides the functions which calculates gradient of the function, useful for the construction of the neural network. Provides support for Gradient based optimization which helps in scaling up the models easily to large data. Disadvantages It is a complex framework, so learning is difficult. Documentation support for learning is not readily available. Scalability may be an issue as compared to TensorFlow. 11. Theano Theano is a library for evaluating and optimizing the mathematical computations. It is based on NumPy but provides support for both the GPU and CPU. AdvantagesIt is a fast computation library in Python. Uses native libraries like BIAS to turn the code in faster computation. Best suited to handle computations in Deep Learning algorithms. Industry standard for Deep Learning research and development. Disadvantages It is not very popular among researchers as it is one of the older frameworks. It is not as easy to use as TensorFlow.12. CNTK CNTK is Microsoft’s Cognitive Toolkit for the development of Deep Learning based models. It is a commercial distributed deep learning tool. AdvantagesIt is a distributed open-source deep learning framework. Popular models like Deep Neural Network, Convolutional Neural Network models can be combined easily to form new models. Provides interface with C, C++ and Java to include Machine Learning models. Can be used to build reinforcement learning models as wide functions are available. Can be used to develop GAN (Generative Adversarial Networks). Provides various ways to measure the performance of the models built. High accuracy parallel computation on Multiple GPU is provided. Disadvantages Proper documentation is not available. There is inadequate community support. ConclusionPython, being one of the most popular languages for the development of Machine Learning models, has a plethora of tools and frameworks available for use. The choice of tool depends on the developer’s experience as well as the type of application to be developed. Every tool has some strong points and some weaknesses, so one has to carefully choose the tool or framework for the development of Machine Learning based applications. The documentation and support available are also important criteria to be kept in mind while choosing the most appropriate tool.
7443
Top 12 Python Packages for Machine Learning

Lovers of vintage movies would have definitely hea... Read More

Programming is at the core of software development, which is why there is a huge demand for programmers—a demand that is growing exponentially and is expected to rise at a steady rate even in the future. In today’s world, just about everything is getting automated and digitization has become the new normal.Recruiters are on the lookout for professionals who have solid programming and full-stack development skills. Every recruiting agency and organizational HR recruiting team has put in place a thorough screening process, and this active hiring in startups, SMEs, and multinational companies has raised the bar for many aspiring programmers. Having a software development, web development, or programming certification will give you an upper hand at the time of recruitment. A certification from a reputed accreditation body will validate your skills and make you stand out among your peers.Having an extra certification apart from your UG or PG degree makes you a better fit for the job role in which you have an interest. But before you opt for any certification, you need to understand which programming language will take you where; and the potential benefits of pursuing a certification course of that particular programming language.In this article, you will get to know about the top programming certifications of 2021 and how to achieve them.What are Programming certifications?Programming certifications are exam-oriented, and verify your skill and expertise in that field. Different organizations provide different programming certification exams that define your level, skills, and abilities vis `a vis that programming language. Having a programming certification will give you an edge over other peers and will highlight your coding skills.Most Popular Programming CertificationsC & C++ CertificationsOracle Certified Associate Java Programmer OCAJPCertified Associate in Python Programming (PCAP)MongoDB Certified Developer Associate ExamR Programming CertificationOracle MySQL Database Administration Training and Certification (CMDBA)CCA Spark and Hadoop Developer1. C & C++ CertificationsRegardless of your specialization in your UG or PG courses, clearing a developer-rated certification will not only make your resume stand out from others but also enhance your skills and boost your confidence. We have curated the top-most and popularly available certifications with descriptions that can help you decide which one is appropriate for your career path.C & C++ Certifications: C and C++ are often called the mother of Procedure-oriented and Object-oriented programming languages, which is absolutely true. These two programming languages have been around for many decades. Colleges and universities all over the world teach these as the base language. To get global recognition for the C and C++ certification, C++ Institute and Pearson VUE decided to carve a niche in this part of the certification landscape by offering the world's first international C/C++ certifications.Aspirants and professionals can choose either C or C++ as a career option/path and climb the certification ladder from associate to professional to senior. The C Programming Language Certified Associate (CLA) and C++ Certified Associate Programmer (CPA) are the core and first-level C and C++ certifications.CLA comprises of topics likeIntroduction to compiling and software development;Basic scalar data types and their operators;Flow control;Complex data types: arrays, structures and pointers;Memory management;Files and streams;Structuring the code: functions and modules;Preprocessor directives and complex declarations.CPA comprises of topics likeIntroduction to compiling and software development;Basic scalar data types, operators, flow control, streamed input/output, conversions;Declaring, defining and invoking functions, function overloading;Data aggregates;String processing, exceptions handling, dealing with namespaces;Object-oriented approach and its vocabulary;Dealing with classes and objects, class hierarchy and inheritance;Defining overloaded operators, user-defined operators, exceptions;Demand and Benefits: Having a CLA certification verifies that the programmer or the aspirant has an understanding of all the necessary and essential universal concepts of computer programming and developer tools. The course also covers all the syntax and semantics of different C constructs plus the data types offered by the language. This course brings crisp knowledge on writing programs using standard language infrastructure regardless of the hardware or software platform.A C++ Certified Associate Programmer (CPA) certification will give you an upper hand because it comprises syntax and semantics of the C++ language plus basic C++ data types. Apart from that, it contains principles of the object-oriented model and C++ implementation. Also, you will get to know about the various C++ standard libraries through this certification process. The average entry-level salary of a C/C++ developer with this certification will be $7,415 per annum. With two to three years of experience, the average salary hikes to$ 10,593 annually.Top companies and industries hiring CLA and CPA are Philips, Calsoft Pvt. Ltd., Cognizant, Synopsys Inc., private universities, Mphasis, etc.Where to take Training for Certification: CPP Institute has all the study resources you need to prepare for this examination. Apart from that, you can study from YouTube free resources.Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to expand their knowledge of C/C++ or start their career as a C/C++ programmer or developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification:CLA Certification: $147.50 (50% discount voucher)CPA Certification:$ 147.50 (50% discount voucher)Exam fee for certification:CLA Certification: $295CPA Certification:$ 295Retake fee for certification: Aspirants who have paid the complete exam price (USD 295) or have completed a course aligned with certification in the self-study mode (50% discount voucher) can have a free retake of the CPA or CLA exam. There is no limit to the number of times a candidate may retake the exam. You must wait 15 days before being allowed to re-sit that exam.2. Oracle Certified Associate Java Programmer OCAJPThis is a Java programming certification provided by Oracle. Java is among the most popular programming languages. James Gosling is the creator of Java which was earlier named Oak. It is a robust, high-level, general-purpose, pure object-oriented programming language developed by Sun Microsystems (now part of Oracle). Java consistently tops the 'most used programming languages’ list and is one of the most extensively used software development platforms. If you have the plan to get a proper training course online before appearing for the certification exam, KnowledgeHut (https://www.knowledgehut.com/programming/java-training) has that for you.It is the preliminary and most basic certification provided by Oracle for Java. It helps gain fundamental understanding of Java programming and builds a foundation in Java and other general programming concepts. The certification encompasses two subcategories –OCAJP Java Standard Edition 8 (OCAJP 8) and  OCAJP Java Standard Edition 11 (OCAJP 11)It comprises of topics likeJava BasicsWorking with Java Data TypesUsing Operators and Decision ConstructsCreating and Using ArraysUsing Loop ConstructsWorking with Methods and EncapsulationWorking with InheritanceHandling ExceptionsClass Methods and EncapsulationDescribing and Using Objects and ClassesHandling ExceptionsJava Technology and the Java Development EnvironmentInheritance and InterfacesUnderstanding ModulesUsing Operators and Decision ConstructsWorking with Java ArraysWorking with Selected classes, Java Primitive Data Types and String APIsDemand and Benefits: Having an OCAJP certification verifies that 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.Top companies and industries hiring Oracle Certified Associate Java Programmers are Smart Monitor Pvt. Ltd., Fiserv, Micron Semiconductor Asia Pvt. Ltd., private universities and many others.Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for beginners in Java programming. It has workshops with hands-on learning and 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 settle as 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 fee for Certification: $245Application fee for certification:OCAJP8:$ 245OCAJP11: $249Exam fee for certification:OCAJP8:$ 245OCAJP11: $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.3. Certified Associate in Python Programming (PCAP)Python is an interpreted, general-purpose, and high-level programming language developed by Guido Van Rossum. 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. 3498 Top-Paying Programming Certifications for 2021 Programming is at the core of software development... Read More ## 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 OCAJPIt 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.
6055
Top IT Certifications for Java Developers in 2021

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