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How to Install MongoDB on a Mac

MongoDB is one of the most popular unstructured database management systems that can store a high volume of data. It is a document-oriented database system that belongs to the family of NoSQL (non-SQL). Here the data and records are stored as documents that behave more like JSON objects. Documents are a combination of key-value pairs that form the basic unit of data in MongoDB. This database system came into action in mid-2000.What is NoSQL and why should we use NoSQL?NoSQL stands for Not Only SQL or non-SQL and is an unstructured database that helps store and retrieve data. In the year 1998, Carl Strozz introduced NoSQL. It models the data by means other than the tabular relations. It means such databases do not have a fixed schema, but are intended explicitly for the distributed data that demands humongous data storage. We use NoSQL databases for real-time web apps, mobile apps, big data, etc. Websites like Google, Twitter, Amazon, Facebook, Instagram, etc., collect terabytes of data every day.Earlier, web applications were simple and did not generate such huge amounts of data. But with the advent of big companies like Facebook, Google, Amazon, etc., huge volumes of data are generated, because of which NoSQL databases have become popular. Traditional RDBMS (like SQL) uses simple queries to store and retrieve textual data. But NoSQL database management systems embrace a wide range of file systems storing structured, unstructured, semi-structured, and polymorphic data.Features of NoSQLNoSQL databases do not follow the relational model. They are schema-free, or they do not follow any specific schema. NoSQL renders heterogeneous data structures (graph, tree, column family, key-value pair, document, etc.) on the same domain. Data is not stored flat in rows and columns (table). NoSQL does not demand data normalization and object-relational mapping. NoSQL does not demand setting up complex concepts like joins, referential integrity, ACID properties, etc. Who should use MongoDB?Developers who want to deal with structured, semi-structured, or unstructured data need to use MongoDB for their applications. Those who are into Big data analysis can also use MongoDB. Again, if an application's data needs agility, scaling, and high performance, MongoDB is the best solution.   It supports a broad spectrum of use cases, from real-time exploratory and predictive analytics to parallel data processing. MongoDB can provide high-performance data storage even when spread across multiple servers.PrerequisitesSoftware Requirement:macOS 10.13 or later MongoDB 4.4 Community Edition (we will show the download procedure later) Install Xcode Command-Line Tools: Homebrew demands to install the Xcode command-line tool from Apple's Xcode before using it. To install Xcode, you have to run the following command in your macOS Terminal:  xcode-select --install Homebrew package manager: By default, macOS does not incorporate the Homebrew package. You can install Homebrew using the documentation given on their official website (https://brew.sh/#install).  Hardware Requirement:Intel Processor / Apple M1 Processor 4 GB RAM preferred Installation StepsInstall Manually without BrewStep 1: Let us now download MongoDB. For this, open your web browser and type: google.comStep 2: From Google search, type: MongoDB and hopefully, the first link the search throws up would be the MongoDB link. From here, we have two ways of installing MongoDB. Follow these steps to install using the macOS terminal.Step 3: Go to mongodb-community Select the version, platform, and package. Make sure you choose macOS as the platform and 'tgz' as the file format and click the download button.Step 4: Once the tgz file gets downloaded, go to the macOS terminal to extract it. Step 5: Mostly, your MongoDB will get downloaded in the Downloads folder. For this, type the following command in the terminal:cd Downloads/ ls tar xzf mongodb-osx-ssl-x86_64-4.4.tgz Step 6: Now, we have to move the MongoDB folder to our local binary storage. sudo mv mongodb-osx-ssl-x86_64-4.4 /usr/local/mongodbThis will ask for your system password. Provide the password. You can change the directory to /usr/local/mongodb and see whether all the files exist or not using the ls command. Note that this step is optional. To change the directory, type the command cd /usr/local/mongodb Next, you have to create the db folder. By default, MongoDB writes or stores the data in the folder called data/db. The command for this will be sudo mkdir -p /data/db The -p flag will allow us to create the directory structure. Now, to check whether this path and directory have been created or not, we use the command: cd /data/dbTo check whether we are on the right directory or not, just type the command: pwdFor changing the permission, you need to know your username first. To know your username, type the command: whoamiNow change the permission of this directory. To do this, the command is: sudo chown <user-name> /data/db Finally, you are eligible to directly run the mongo process.  Install using Brew –If you want to install MongoDB through Homebrew manually, follow these steps – Step 1: Homebrew helps in installing and managing applications on MacOS. If you haven't downloaded or installed Homebrew, then click the link (https://github.com/mongodb/homebrew-brew) to download the official Homebrew formula for MongoDB, by running the command in your macOS Terminal:  brew update  brew tap mongodb/brew Step 2: Once the Homebrew package resides in your system, you can download MongoDB using brew. Step 3: Type the following command in your macOS Terminal: brew install mongodb-community@version-numberStep 4: This installation will add the following binaries: The mongod server The mongo shell The mongos sharded cluster query router Step 5: The installation will take a few seconds. Once done, you can create a directory to store MongoDB data using the following command. sudo mkdir -p /data/db Step 6: Now, you have to note that your data directory should have the appropriate permissions. To do this, execute the command: sudo chown -R `id -un` /data/db Step 7: This will ensure that the data directory is ready and has all the proper permissions. Step 8: Apart from that, the MongoDB installation will produce the following files and directories at the locations given below – Intel Processors Apple M1  Log directory/usr/local/var/log/mongodb/opt/homebrew/var/log/mongodbConfiguration file/usr/local/etc/mongod.conf/opt/homebrew/etc/mongod.confData directory/usr/local/var/mongodb/opt/homebrew/var/mongodbStep 9: Let us now run the MongoDB community Edition. You can use the brew command to run MongoDB as a macOS. A manual procedure is needed to run MongoDB services on macOS. To execute MongoDB daemon, which resides by the name mongod (process), use the following command: brew services start mongodb-community macOS will run this process as a macOS service. Step 10: For stopping a mongod process running as a macOS service, apply the following command: brew services stop mongodb-communityStep 11: For running MongoDB in the background manually and listening for connections on a given port, use the following command - For Mac systems with Intel processors: mongod --config /usr/local/etc/mongod.conf --fork For Mac systems with Apple M1 processors: mongod --config /opt/homebrew/etc/mongod.conf –fork Step 12: Next, verify your MongoDB version. To do this, type the following command: mongo –version Step 13: The command line will display the installed version of MongoDB on your Mac system. Developers recommend using the newest version of libraries and software whenever feasible. It will keep you away from any compatibility issues with client-side applications. Step14: You can view the installation list by typing the command: mongodb Step15: Use the command mongod --config /usr/local/etc/mongod.conf to start the MongoDB Step 16: To connect to mongodb service, type the command: mongo Step17: Use the ‘show dbs’ command to see all databases. You can learn more about the working of MongoDB and become an expert NoSQL database administrator by joining the course mongodb-administrator. This course covers features of MongoDB 4.0 and future releases. Uninstall MongoDB on macOS X –Uninstalling MongoDB from your system will entirely remove MongoDB along with its associated files. Before uninstalling MongoDB, check whether any mongo service is running by using the command: launchctl list | grep mongo If any running process exists before uninstallation, you should stop or kill it. To kill all the processes related to mongod, use the command: pkill -f mongod The command to uninstall MongoDB from your system is: If installed via brew: brew uninstall mongodb-communityOr, if installed manually you can simply delete the folder: rm -rf <mongodb_folder>If you have a separate folder for the database, use the command to remove that database directory: rm -rf /data/db MongoDB is the leading NoSQL, document-based, open-source database system. It is a cross-platform system - licensed under the Server-Side Public License (SSPL). Due to its broad spectrum of features and benefits, it became popular very quickly. Hopefully, this article has helped you understand the basics of installing MongoDB in your Apple system.   In this article, we have walked you through the two ways to install MongoDB in a macOS. Also, this article explicitly talked about installing MongoDB in Apple systems with Intel processors and with Apple M1 processors. So, you can navigate this article as per your system. You can learn more about MongoDB installation and join the course from mongodb-administrator.
How to Install MongoDB on a Mac
Gaurav
Gaurav

Gaurav Kr. Roy

Author

Mr. Gaurav is a cybersecurity engineer, developer, researcher, and Book-Author who did his B.S.-Cybersecurity from EC-Council University & Masters from LPU. He is an India Book of Record holder, Guest speaker with 7+ years of experience in IT. 

Posts by Gaurav Kr. Roy

How to Install MongoDB on a Mac

MongoDB is one of the most popular unstructured database management systems that can store a high volume of data. It is a document-oriented database system that belongs to the family of NoSQL (non-SQL). Here the data and records are stored as documents that behave more like JSON objects. Documents are a combination of key-value pairs that form the basic unit of data in MongoDB. This database system came into action in mid-2000.What is NoSQL and why should we use NoSQL?NoSQL stands for Not Only SQL or non-SQL and is an unstructured database that helps store and retrieve data. In the year 1998, Carl Strozz introduced NoSQL. It models the data by means other than the tabular relations. It means such databases do not have a fixed schema, but are intended explicitly for the distributed data that demands humongous data storage. We use NoSQL databases for real-time web apps, mobile apps, big data, etc. Websites like Google, Twitter, Amazon, Facebook, Instagram, etc., collect terabytes of data every day.Earlier, web applications were simple and did not generate such huge amounts of data. But with the advent of big companies like Facebook, Google, Amazon, etc., huge volumes of data are generated, because of which NoSQL databases have become popular. Traditional RDBMS (like SQL) uses simple queries to store and retrieve textual data. But NoSQL database management systems embrace a wide range of file systems storing structured, unstructured, semi-structured, and polymorphic data.Features of NoSQLNoSQL databases do not follow the relational model. They are schema-free, or they do not follow any specific schema. NoSQL renders heterogeneous data structures (graph, tree, column family, key-value pair, document, etc.) on the same domain. Data is not stored flat in rows and columns (table). NoSQL does not demand data normalization and object-relational mapping. NoSQL does not demand setting up complex concepts like joins, referential integrity, ACID properties, etc. Who should use MongoDB?Developers who want to deal with structured, semi-structured, or unstructured data need to use MongoDB for their applications. Those who are into Big data analysis can also use MongoDB. Again, if an application's data needs agility, scaling, and high performance, MongoDB is the best solution.   It supports a broad spectrum of use cases, from real-time exploratory and predictive analytics to parallel data processing. MongoDB can provide high-performance data storage even when spread across multiple servers.PrerequisitesSoftware Requirement:macOS 10.13 or later MongoDB 4.4 Community Edition (we will show the download procedure later) Install Xcode Command-Line Tools: Homebrew demands to install the Xcode command-line tool from Apple's Xcode before using it. To install Xcode, you have to run the following command in your macOS Terminal:  xcode-select --install Homebrew package manager: By default, macOS does not incorporate the Homebrew package. You can install Homebrew using the documentation given on their official website (https://brew.sh/#install).  Hardware Requirement:Intel Processor / Apple M1 Processor 4 GB RAM preferred Installation StepsInstall Manually without BrewStep 1: Let us now download MongoDB. For this, open your web browser and type: google.comStep 2: From Google search, type: MongoDB and hopefully, the first link the search throws up would be the MongoDB link. From here, we have two ways of installing MongoDB. Follow these steps to install using the macOS terminal.Step 3: Go to mongodb-community Select the version, platform, and package. Make sure you choose macOS as the platform and 'tgz' as the file format and click the download button.Step 4: Once the tgz file gets downloaded, go to the macOS terminal to extract it. Step 5: Mostly, your MongoDB will get downloaded in the Downloads folder. For this, type the following command in the terminal:cd Downloads/ ls tar xzf mongodb-osx-ssl-x86_64-4.4.tgz Step 6: Now, we have to move the MongoDB folder to our local binary storage. sudo mv mongodb-osx-ssl-x86_64-4.4 /usr/local/mongodbThis will ask for your system password. Provide the password. You can change the directory to /usr/local/mongodb and see whether all the files exist or not using the ls command. Note that this step is optional. To change the directory, type the command cd /usr/local/mongodb Next, you have to create the db folder. By default, MongoDB writes or stores the data in the folder called data/db. The command for this will be sudo mkdir -p /data/db The -p flag will allow us to create the directory structure. Now, to check whether this path and directory have been created or not, we use the command: cd /data/dbTo check whether we are on the right directory or not, just type the command: pwdFor changing the permission, you need to know your username first. To know your username, type the command: whoamiNow change the permission of this directory. To do this, the command is: sudo chown /data/db Finally, you are eligible to directly run the mongo process.  Install using Brew –If you want to install MongoDB through Homebrew manually, follow these steps – Step 1: Homebrew helps in installing and managing applications on MacOS. If you haven't downloaded or installed Homebrew, then click the link (https://github.com/mongodb/homebrew-brew) to download the official Homebrew formula for MongoDB, by running the command in your macOS Terminal:  brew update  brew tap mongodb/brew Step 2: Once the Homebrew package resides in your system, you can download MongoDB using brew. Step 3: Type the following command in your macOS Terminal: brew install mongodb-community@version-numberStep 4: This installation will add the following binaries: The mongod server The mongo shell The mongos sharded cluster query router Step 5: The installation will take a few seconds. Once done, you can create a directory to store MongoDB data using the following command. sudo mkdir -p /data/db Step 6: Now, you have to note that your data directory should have the appropriate permissions. To do this, execute the command: sudo chown -R `id -un` /data/db Step 7: This will ensure that the data directory is ready and has all the proper permissions. Step 8: Apart from that, the MongoDB installation will produce the following files and directories at the locations given below – Intel Processors Apple M1  Log directory/usr/local/var/log/mongodb/opt/homebrew/var/log/mongodbConfiguration file/usr/local/etc/mongod.conf/opt/homebrew/etc/mongod.confData directory/usr/local/var/mongodb/opt/homebrew/var/mongodbStep 9: Let us now run the MongoDB community Edition. You can use the brew command to run MongoDB as a macOS. A manual procedure is needed to run MongoDB services on macOS. To execute MongoDB daemon, which resides by the name mongod (process), use the following command: brew services start mongodb-community macOS will run this process as a macOS service. Step 10: For stopping a mongod process running as a macOS service, apply the following command: brew services stop mongodb-communityStep 11: For running MongoDB in the background manually and listening for connections on a given port, use the following command - For Mac systems with Intel processors: mongod --config /usr/local/etc/mongod.conf --fork For Mac systems with Apple M1 processors: mongod --config /opt/homebrew/etc/mongod.conf –fork Step 12: Next, verify your MongoDB version. To do this, type the following command: mongo –version Step 13: The command line will display the installed version of MongoDB on your Mac system. Developers recommend using the newest version of libraries and software whenever feasible. It will keep you away from any compatibility issues with client-side applications. Step14: You can view the installation list by typing the command: mongodb Step15: Use the command mongod --config /usr/local/etc/mongod.conf to start the MongoDB Step 16: To connect to mongodb service, type the command: mongo Step17: Use the ‘show dbs’ command to see all databases. You can learn more about the working of MongoDB and become an expert NoSQL database administrator by joining the course mongodb-administrator. This course covers features of MongoDB 4.0 and future releases. Uninstall MongoDB on macOS X –Uninstalling MongoDB from your system will entirely remove MongoDB along with its associated files. Before uninstalling MongoDB, check whether any mongo service is running by using the command: launchctl list | grep mongo If any running process exists before uninstallation, you should stop or kill it. To kill all the processes related to mongod, use the command: pkill -f mongod The command to uninstall MongoDB from your system is: If installed via brew: brew uninstall mongodb-communityOr, if installed manually you can simply delete the folder: rm -rf If you have a separate folder for the database, use the command to remove that database directory: rm -rf /data/db MongoDB is the leading NoSQL, document-based, open-source database system. It is a cross-platform system - licensed under the Server-Side Public License (SSPL). Due to its broad spectrum of features and benefits, it became popular very quickly. Hopefully, this article has helped you understand the basics of installing MongoDB in your Apple system.   In this article, we have walked you through the two ways to install MongoDB in a macOS. Also, this article explicitly talked about installing MongoDB in Apple systems with Intel processors and with Apple M1 processors. So, you can navigate this article as per your system. You can learn more about MongoDB installation and join the course from mongodb-administrator.
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How to Install MongoDB on a Mac

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Types of Probability Distributions Every Data Science Expert Should know

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution.  What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values.  Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution  Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm  import matplotlib.pyplot as mpl  import numpy as np  def normalDist() -> None:      fig, ax = mpl.subplots(1, 1)      mean, var, skew, kurt = norm.stats(moments = 'mvsk')      x = np.linspace(norm.ppf(0.01),  norm.ppf(0.99), 100)      ax.plot(x, norm.pdf(x),          'r-', lw = 5, alpha = 0.6, label = 'norm pdf')      ax.plot(x, norm.cdf(x),          'b-', lw = 5, alpha = 0.6, label = 'norm cdf')      vals = norm.ppf([0.001, 0.5, 0.999])      np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))      r = norm.rvs(size = 1000)      ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2)      ax.legend(loc = 'best', frameon = False)      mpl.show()  normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial.   Probability Mass Function of a Bernoulli’s Distribution is:  where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli  import seaborn as sb    def bernoulliDist():      data_bern = bernoulli.rvs(size=1200, p = 0.7)      ax = sb.distplot(          data_bern,           kde = True,           color = 'g',           hist_kws = {'alpha' : 1},          kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'})      ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution')  bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def uniformDist():      sb.distplot(random.uniform(size = 1200), hist = True)      mpl.show()  uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl  def lognormalDist():      muu, sig = 3, 1      s = np.random.lognormal(muu, sig, 1000)      cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y')      x = np.linspace(min(bins), max(bins), 10000)      calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2))             / (x * sig * np.sqrt(2 * np.pi)))      mpl.plot(x, calc, linewidth = 2.5, color = 'g')      mpl.axis('tight')      mpl.show()  lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np  from matplotlib import pyplot as plt  from scipy.stats import pareto  def paretoDist():      xm = 1.5        alp = [2, 4, 6]       x = np.linspace(0, 4, 800)      output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp])      plt.plot(x, output.T)      plt.show()  paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def expDist():      sb.distplot(random.exponential(size = 1200), hist = True)      mpl.show()   expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb    def binomialDist():      sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal')      sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial')      plt.show()    binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl  def probability_to_occur_at(attempt, probability):      return (1-p)**(attempt - 1) * probability  p = 0.3  attempt = 4  attempts_to_show = range(21)[1:]  print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p))  mpl.xlabel('Number of Trials')  mpl.ylabel('Probability of the Event')  barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show)  barlist[attempt].set_color('g')  mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson  import seaborn as sb  import numpy as np  import matplotlib.pyplot as mpl  def poissonDist():       mpl.figure(figsize = (10, 10))      data_binom = poisson.rvs(mu = 3, size = 5000)      ax = sb.distplot(data_binom, kde=True, color = 'g',                       bins=np.arange(data_binom.min(), data_binom.max() + 1),                       kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'})      ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency')      mpl.show()      poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np  import matplotlib.pyplot as mpl  np.random.seed(99)   n = 12                      pvalue = [0.3, 0.46, 0.22]     s = []  p = []     for size in np.logspace(2, 3):      outcomes = np.random.multinomial(n, pvalue, size=int(size))        prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes)      p.append(prob)      s.append(int(size))  fig1 = mpl.figure()  mpl.plot(s, p, 'o-')  mpl.plot(s, [0.0248]*len(s), '--r')  mpl.grid()  mpl.xlim(xmin = 0)  mpl.xlabel('Number of Events')  mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments.  Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl   import numpy as np   from scipy.stats import nbinom    x = np.linspace(0, 6, 70)   gr, kr = 0.3, 0.7        g = nbinom.ppf(x, gr, kr)   s = nbinom.pmf(x, gr, kr)   mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion  Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course. 
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Types of Probability Distributions Every Data Scie...

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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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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. 
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Top IT Certifications for Java Developers in 2021

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

Role of Unstructured Data in Data Science

Data has become the new game changer for businesses. Typically, data scientists categorize data into three broad divisions - structured, semi-structured, and unstructured data. In this article, you will get to know about unstructured data, sources of unstructured data, unstructured data vs. structured data, the use of structured and unstructured data in machine learning, and the difference between structured and unstructured data. Let us first understand what is unstructured data with examples. What is unstructured data? Unstructured data is a kind of data format where there is no organized form or type of data. Videos, texts, images, document files, audio materials, email contents and more are considered to be unstructured data. It is the most copious form of business data, and cannot be stored in a structured database or relational database. Some examples of unstructured data are the photos we post on social media platforms, the tagging we do, the multimedia files we upload, and the documents we share. Seagate predicts that the global data-sphere will expand to 163 zettabytes by 2025, where most of the data will be in the unstructured format. Characteristics of Unstructured DataUnstructured data cannot be organized in a predefined fashion, and is not a homogenous data model. This makes it difficult to manage. Apart from that, these are the other characteristics of unstructured data. You cannot store unstructured data in the form of rows and columns as we do in a database table. Unstructured data is heterogeneous in structure and does not have any specific data model. The creation of such data does not follow any semantics or habits. Due to the lack of any particular sequence or format, it is difficult to manage. Such data does not have an identifiable structure. Sources of Unstructured Data There are various sources of unstructured data. Some of them are: Content websites Social networking sites Online images Memos Reports and research papers Documents, spreadsheets, and presentations Audio mining, chatbots Surveys Feedback systems Advantages of Unstructured Data Unstructured data has become exceptionally easy to store because of MongoDB, Cassandra, or even using JSON. Modern NoSQL databases and software allows data engineers to collect and extract data from various sources. There are numerous benefits that enterprises and businesses can gain from unstructured data. These are: With the advent of unstructured data, we can store data that lacks a proper format or structure. There is no fixed schema or data structure for storing such data, which gives flexibility in storing data of different genres. Unstructured data is much more portable by nature. Unstructured data is scalable and flexible to store. Database systems like MongoDB, Cassandra, etc., can easily handle the heterogeneous properties of unstructured data. Different applications and platforms produce unstructured data that becomes useful in business intelligence, unstructured data analytics, and various other fields. Unstructured data analysis allows finding comprehensive data stories from data like email contents, website information, social media posts, mobile data, cache files and more. Unstructured data, along with data analytics, helps companies improve customer experience. Detection of the taste of consumers and their choices becomes easy because of unstructured data analysis. Disadvantages of Unstructured data Storing and managing unstructured data is difficult because there is no proper structure or schema. Data indexing is also a substantial challenge and hence becomes unclear due to its disorganized nature. Search results from an unstructured dataset are also not accurate because it does not have predefined attributes. Data security is also a challenge due to the heterogeneous form of data. Problems faced and solutions for storing unstructured data. Until recently, it was challenging to store, evaluate, and manage unstructured data. But with the advent of modern data analysis tools, algorithms, CAS (content addressable storage system), and big data technologies, storage and evaluation became easy. Let us first take a look at the various challenges used for storing unstructured data. Storing unstructured data requires a large amount of space. Indexing of unstructured data is a hectic task. Database operations such as deleting and updating become difficult because of the disorganized nature of the data. Storing and managing video, audio, image file, emails, social media data is also challenging. Unstructured data increases the storage cost. For solving such issues, there are some particular approaches. These are: CAS system helps in storing unstructured data efficiently. We can preserve unstructured data in XML format. Developers can store unstructured data in an RDBMS system supporting BLOB. We can convert unstructured data into flexible formats so that evaluating and storage becomes easy. Let us now understand the differences between unstructured data vs. structured data. Unstructured Data Vs. Structured Data In this section, we will understand the difference between structured and unstructured data with examples. STRUCTUREDUNSTRUCTUREDStructured data resides in an organized format in a typical database.Unstructured data cannot reside in an organized format, and hence we cannot store it in a typical database.We can store structured data in SQL database tables having rows and columns.Storing and managing unstructured data requires specialized databases, along with a variety of business intelligence and analytics applications.It is tough to scale a database schema.It is highly scalable.Structured data gets generated in colleges, universities, banks, companies where people have to deal with names, date of birth, salary, marks and so on.We generate or find unstructured data in social media platforms, emails, analyzed data for business intelligence, call centers, chatbots and so on.Queries in structured data allow complex joining.Unstructured data allows only textual queries.The schema of a structured dataset is less flexible and dependent.An unstructured dataset is flexible but does not have any particular schema.It has various concurrency techniques.It has no concurrency techniques.We can use SQL, MySQL, SQLite, Oracle DB, Teradata to store structured data.We can use NoSQL (Not Only SQL) to store unstructured data.Types of Unstructured Data Do you have any idea just how much of unstructured data we produce and from what sources? Unstructured data includes all those forms of data that we cannot actively manage in an RDBMS system that is a transactional system. We can store structured data in the form of records. But this is not the case with unstructured data. Before the advent of object-based storage, most of the unstructured data was stored in file-based systems. Here are some of the types of unstructured data. Rich media content: Entertainment files, surveillance data, multimedia email attachments, geospatial data, audio files (call center and other recorded audio), weather reports (graphical), etc., comes under this genre. Document data: Invoices, text-file records, email contents, productivity applications, etc., are included under this genre. Internet of Things (IoT) data: Ticker data, sensor data, data from other IoT devices come under this genre. Apart from all these, data from business intelligence and analysis, machine learning datasets, and artificial intelligence data training datasets are also a separate genre of unstructured data. Examples of Unstructured Data There are various sources from where we can obtain unstructured data. The prominent use of this data is in unstructured data analytics. Let us now understand what are some examples of unstructured data and their sources – Healthcare industries generate a massive volume of human as well as machine-generated unstructured data. Human-generated unstructured data could be in the form of patient-doctor or patient-nurse conversations, which are usually recorded in audio or text formats. Unstructured data generated by machines includes emergency video camera footage, surgical robots, data accumulated from medical imaging devices like endoscopes, laparoscopes and more.  Social Media is an intrinsic entity of our daily life. Billions of people come together to join channels, share different thoughts, and exchange information with their loved ones. They create and share such data over social media platforms in the form of images, video clips, audio messages, tagging people (this helps companies to map relations between two or more people), entertainment data, educational data, geolocations, texts, etc. Other spectra of data generated from social media platforms are behavior patterns, perceptions, influencers, trends, news, and events. Business and corporate documents generate a multitude of unstructured data such as emails, presentations, reports containing texts, images, presentation reports, video contents, feedback and much more. These documents help to create knowledge repositories within an organization to make better implicit operations. Live chat, video conferencing, web meeting, chatbot-customer messages, surveillance data are other prominent examples of unstructured data that companies can cultivate to get more insights into the details of a person. Some prominent examples of unstructured data used in enterprises and organizations are: Reports and documents, like Word files or PDF files Multimedia files, such as audio, images, designed texts, themes, and videos System logs Medical images Flat files Scanned documents (which are images that hold numbers and text – for example, OCR) Biometric data Unstructured Data Analytics Tools  You might be wondering what tools can come into use to gather and analyze information that does not have a predefined structure or model. Various tools and programming languages use structured and unstructured data for machine learning and data analysis. These are: Tableau MonkeyLearn Apache Spark SAS Python MS. Excel RapidMiner KNIME QlikView Python programming R programming Many cloud services (like Amazon AWS, Microsoft Azure, IBM Cloud, Google Cloud) also offer unstructured data analysis solutions bundled with their services. How to analyze unstructured data? In the past, the process of storage and analysis of unstructured data was not well defined. Enterprises used to carry out this kind of analysis manually. But with the advent of modern tools and programming languages, most of the unstructured data analysis methods became highly advanced. AI-powered tools use algorithms designed precisely to help to break down unstructured data for analysis. Unstructured data analytics tools, along with Natural language processing (NLP) and machine learning algorithms, help advanced software tools analyze and extract analytical data from the unstructured datasets. Before using these tools for analyzing unstructured data, you must properly go through a few steps and keep these points in mind. Set a clear goal for analyzing the data: It is essential to clear your intention about what insights you want to extract from your unstructured data. Knowing this will help you distinguish what type of data you are planning to accumulate. Collect relevant data: Unstructured data is available everywhere, whether it's a social media platform, online feedback or reviews, or a survey form. Depending on the previous point, that is your goal - you have to be precise about what data you want to collect in real-time. Also, keep in mind whether your collected details are relevant or not. Clean your data: Data cleaning or data cleansing is a significant process to detect corrupt or irrelevant data from the dataset, followed by modifying or deleting the coarse and sloppy data. This phase is also known as the data-preprocessing phase, where you have to reduce the noise, carry out data slicing for meaningful representation, and remove unnecessary data. Use Technology and tools: Once you perform the data cleaning, it is time to utilize unstructured data analysis tools to prepare and cultivate the insights from your data. Technologies used for unstructured data storage (NoSQL) can help in managing your flow of data. Other tools and programming libraries like Tableau, Matplotlib, Pandas, and Google Data Studio allows us to extract and visualize unstructured data. Data can be visualized and presented in the form of compelling graphs, plots, and charts. How to Extract information from Unstructured Data? With the growth in digitization during the information era, repetitious transactions in data cause data flooding. The exponential accretion in the speed of digital data creation has brought a whole new domain of understanding user interaction with the online world. According to Gartner, 80% of the data created by an organization or its application is unstructured. While extracting exact information through appropriate analysis of organized data is not yet possible, even obtaining a decent sense of this unstructured data is quite tough. Until now, there are no perfect tools to analyze unstructured data. But algorithms and tools designed using machine learning, Natural language processing, Deep learning, and Graph Analysis (a mathematical method for estimating graph structures) help us to get the upper hand in extracting information from unstructured data. Other neural network models like modern linguistic models follow unsupervised learning techniques to gain a good 'knowledge' about the unstructured dataset before going into a specific supervised learning step. AI-based algorithms and technologies are capable enough to extract keywords, locations, phone numbers, analyze image meaning (through digital image processing). We can then understand what to evaluate and identify information that is essential to your business. ConclusionUnstructured data is found abundantly from sources like documents, records, emails, social media posts, feedbacks, call-records, log-in session data, video, audio, and images. Manually analyzing unstructured data is very time-consuming and can be very boring at the same time. With the growth of data science and machine learning algorithms and models, it has become easy to gather and analyze insights from unstructured information.  According to some research, data analytics tools like MonkeyLearn Studio, Tableau, RapidMiner help analyze unstructured data 1200x faster than the manual approach. Analyzing such data will help you learn more about your customers as well as competitors. Text analysis software, along with machine learning models, will help you dig deep into such datasets and make you gain an in-depth understanding of the overall scenario with fine-grained analyses.
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Role of Unstructured Data in Data Science

Data has become the new game changer for busines... Read More