Trending IT Courses in 2020 Part – 1

TRENDING COURSES IN 2020

Nowadays people should keep them updated to sustain in this competitive world. Especially in our IT field we should keep ourselves up to date to sustain our stand. These latest technologies help us to upgrade our carrier to next level. Following are some short time trend setting courses.

    • Python programming: Most trending programming language in 2019 is python. It is a high-level programming language. It is a general-purpose language. Most of the popular companies like IBM, NETFLIX, FACEBOOK, INSTAGRAM, etc., use python.

Do you think python is only for web design? Not at all.  In growing ecosystem, Python scores as a stable programming language. Automation development uses Python as its main programming Language. You can reduce working time from 3 hours to 30 minutes if you use python.

    • Cloud Computing: Cloud computing meant to save and use data via internet.  This reduces saving data in system memory. Tech giants GOOGLE, AMAZON, MICROSOFT, HP, etc., use cloud computing. Cloud serves all customers who need large storage.

Cloud Customers don’t need to hire their own server or external memories. Instead, they can rent from service providers. Cloud reduces worry with data Storage management.

  • Java/J2EE Programming: All Financial/Banking companies uses Java. Retail/Consumer, Telecom, Aviation, Life science use this. It seems to be unending trend in IT field. Java script is trending nowadays. Java scripting is simple. It is very fast.
  • CISCO Technologies: It is network training. It has five level of courses. They are from Entry to Architect level. It includes special area like design, routing & switching. Service provider & storage networking are also included. Network security, voice & wireless are also included.
  • Mobile Application Development: It is the most developing feature. It helps for growing social media. It helps in gaming app development. We can also upload our own app.
  • Web Designing: All people can design their own websites using this. It is the world of designing & developing a website and to maintain it.
  • Artificial Intelligence: AI is a branch of computer science. Intelligent machine developed by AI. Machine will act like human. Speech recognition is a new technology using AI. Speech recognition is one of the best examples for AI. Robotics plays a major role in the development of AI.  It is a specialized system. It is a highly technical region in development.
  • Animation & Graphics: Using the computer we can create moving object. 3D Graphics widely used to create such animated object. 2D Graphics used to create in low bandwidth.
  • Software Quality Testing: It is incredible part in the IT field. Each item after developing, we should conduct testing. After testing each item, we should qualify it. There are lots of tools to check these items.
  • DevOps: Basic knowledge of scripting languages is essential for DevOps. This is the combination of software development and IT operations. There is different set of tools involved in this process. Coding, Building and Testing are some of the tools used in Devops. Packaging and Releasing are also used by Devops as tool. Configuring and Monitoring are the tools used in this DevOps.

Programming Languages

Python

Python is a dynamic object-oriented language. Its elegant and simple to learn syntax. It has high level data types and elaborate library. It has portability and ease of extending. It can be embedding in other programming languages. These all contribute to its popularity.

Python is one of most popular programming languages. Python gets upgraded from 4th position to 3rd position with rating 8.262% in March 2019 by TOIBE index. It has the changing rate of +2.39%.

Most of the trending technologies use Python. Devops, Machine learning, cloud computing, automation, robotics, Data analysis etc., use python as one of its main language. Python can access all the directories and folders on your system. It can also read all file formats.

Python in Automation

Automation field is popular through Python. By writing simple scripts, we can achieve this. It helps to solve problem quickly. Selenium is an open source automation tool. To test automation, we should use Python along with Selenium. It helps to minimize development cycles. It is used to get maximum Return on Investment (ROI).

Python in Robotics

Pythons script is popular in Robotics. Using Python script, we can simulate the entire robotic program. It is used to control a wide variety of real & simulated robots. Python is binding with other languages. Programmer can use this binding in robotics.

Python can be bind with other languages like C/C++. This is one of big advantage of python. It helps maintain performance related codes in other languages.

Python in Data Analysis

Data analysis is a process. It is used to convert raw data to useful information. This information helps us in decision-making. Python is a familiar programming in data analysis.

Pandas, Matplotlib are some of the Python library used. It imports data from excel sheets. Process them. It will set for time series analysis. Then it will plot the data in useful models.

Python in Machine Learning

Machine learning is sub type of AI. There are more than 20 libraries in python for various purposes. Each library contains huge number of useful modules. IBM says python is the best machine learning language. By using data, it will help the machine to make correct decision.

Scrikit-Learn is one of the libraries used by python for machine learning. This library is used to inter-operate with SciPy and NumPy. SciPy is a scientific library used in python. NumPy is numerical library used in Python. These libraries are used in implementing all machine learning algorithms.

Python in Cloud Computing

Cloud computing is a cutting-edge technology. Cloud computing uses Python. It uses both software and hardware. It provides service through network. Cloud computing platforms uses python to deploy. It is also used to manage the cloud application.

It is also used to scale cloud applications. IBM Bluemix, Microsoft Azure and Google cloud are few infrastructures. These cloud computing infrastructures use python as its programming language. This proves the strength and ability of python programming.

Big Data spark using python

Big Data: Big data is gathering & storing large amount of information. It contains both structured and unstructured data. There are three Vs in big data. They are Volume, Velocity, Variety. Volume stands for amount of data. Velocity stands for speed of data. Variety stands for number of types of the data.

The most important aspect of big data is taking data from any sources. It analyses given data to find answer. Answer makes possible for cost saving, time reduction. New product development is feasible with big data. Understand the market conditions is easy with big data. Control online reputation is also effective with big data.

Big Data spark using Python: Spark is open source framework. It is from Apache Software Foundation. It is a computing engine. Using this we can process and analyze large amount of real time data. Mainly inter-connected platforms system uses this. It is also used by standards for big data processing.

Big data spark processes data quickly.  This can be done by transferring details from computer’s hard disk. Faster electronic memory stores the transferred data.  Many companies use this for storing and analyzing data. Last year spark has beaten world record of Hadoop. It sorts 100 terabytes in 23minutes.

It also works with the cluster and process data in parallel. Programming languages like Scala, Python, R, Java, Go are also used in big data spark.

There are many reasons to use python for Apache Spark. Some of them are it has simple syntax. It has powerful data analysis through its libraries. Apache community released a tool named PySpark. PySpark interact with Resilient Distributed Datasets (RDD). Python is used to achieve this by using Py4j library.

we can able to initialize Spark context. This is possible by linking Python API to the Spark core. PySpark Shell is used to do this. Py4j is used to launch a Java Virtual Machine. It will also create JavaSparkContext. By using Py4j we can establish the local communication. Python & JavaSparkContexts communicates on the drive.

In spark framework, Pyspark offers 12 important classes, and they are as follows,

  • SparkContext: It will act as a main entry. Especially for function of Spark.
  • RDD: It is the basic abstractions in the Spark.
  • Broadcast: Using this we can reuse variables anywhere in the task.
  • Accumulator: Only this task can add values. These values are added to the ADD-ONLY variables.
  • SparkConf:  Using SparkConf we can configure the Spark.
  • SparkFiles:  We can use SparkFiles to access the files related to the task.
  • Storage level: It gives the persistence level of cache present in Finer-grained.
  • TaskContext: It provides information about the current running task. It also provides information about the experiment.
  • RDDBarrier:  It is for executing wrap of RDD. This execution of RDD is for both barrier stage and barrier.
  • BarrierTaskContext: It gives more information about tooling. It also gives more information about barrier execution.
  • BarrierTaskInfo: It provides information about a barrier task.

All real time applications use Pyspark. This is one of the best advantages of Pyspark. One of the examples of Pyspark is Healthcare. Analyzing clinical data with old record uses Pyspark. Financial Sectors is another example for Pyspark. It helps in accessing various data. It also helps in analyzing the same data.

Banking sectors uses Pyspark. Retail and E Commerce is also the example for Pyspark. Video streaming like Netflix uses Pyspark. In Travel, it will analyze the travel data. It will recommend trips to travelers. In simple words it is a TripAdvisor. It uses Pyspark. Pyspark plays a major role in gaming Industries.

Data Science/Machine Learning

Data Science: It is a field of analyzing large amount of raw data. It categorizes the raw data into useful patterns. The patterns are used to form more concrete questions. The answers for these questions give the correlation between the data sets. This correlation and patterns will be useful in solving more problems.

Data science uses predictive analysis. It also uses statistics and AI for data analyzes. The queries framed in data science is used to form. It is also useful in groups. Groups is also known as clusters. This clustering is further used to identify business analysis. Data Science is taking unordered data. Convert it to ordered format. The ordered format is useful for further analysis.

Programing Language used by Data Science: There are widely 6 programming languages used for Data Science. They are as follows,

  • R: Data Science widely use R programming for data analyzes. It will prepare and analyze statistics data as simple as possible. We can able to produce graphs and Maths symbol. This is possible by using statics graphics in R.
  • Using R, we can also able to produce Vectors and Matrices. We can able to produce Arrays and data frames. It also serves as an alternative to SAS and Matlab.
    • Python: Data science uses Python. It is simple and general purpose. It is a multi-paradigm programming language. Python has large number of libraries. We can be able to do variety of tasks using this library.
  • Graphics user interface is the best task carried by Python. Automation and multi-media also use Python. Database uses Python. Text and image processing are some of the tasks done using Python.
  • Java: Java is another programming language used by Data Science. After compiling the code, we can able to run the code in any platform which supports Java. This is the special feature of Java. The greatest tool for Data Science is Java Virtual Machine (JVM). Lambda support and REPL support are two great development of Java.
  • Scala: Data Science uses Scala. It is user-friendly. We can modify as per the demand of the user. It has large user interface. We can able to run the data on Java. All platform which supports Java will also support Scala.
  • SQL: Data Science uses SQL. SQL deals with large database. It is useful in managing structed data. The only drawback with this SQL is the lack of portability.
  • Julia: Data science uses Julia. It helps to address all numerical needs. It helps to address all computing needs. We can handle floating-point calculation using libraries. It is easy to handle the linear algebra calculations. These are some of special features of Julia.

Data Science Applications: Data Science is a potential field. All popular companies use Data science. It is used to analyze and find useful answers from large amount of data. For example, Google and Amazon use data analysis. It is used to predict the search results based on input. It recommends the users about the most visited pages. It also recommends the products.

Data Science is also used in Banking sector. For assessing the risks for loans to customers we use Data science. It also helps to detect the fraud. Data science is also used in insurance. Data Science is very useful in retail field. It is Used to find the uncovered relations and connections. These connections are necessary for setting the targets.

It also helps in identifying new areas in marketing. Data Science reduces the marketing efforts. This is possible by its powerful analysis.

Machine Learning: Machine Learning is an application of artificial intelligence. Instead of writing programs it provides the system to learn automatic. Artificial intelligent makes the computer to think intelligent. Machine learning concentrates on development of computer programs to access the data and learn themselves. Some of the machine learning methods are as follows,

  • Supervised machine learning algorithms
  • Unsupervised machine learning algorithms
  • Semi-supervised machine learning algorithms
  • Reinforcement machine learning algorithms

Machine learning is possible for analyzing large amount of data. It produces accurate result very fast.

Programming Languages used in Machine Learning: Here are some of the most popular programming languages used in Machine Learning.

  • Python: Python is one of the best programming Language for AI. It is because of its simple and easy leaning syntaxes. Many of the AI algorithms are easy to implement. Comparing to other programming languages Python takes short time span.
  • It supports object oriental programs. It also supports functional programs. Procedure-oriented programs are also supported by Python. It has plenty of libraries. Using these libraries, we can able to make any task.
  • R:R is the most effective programming Language. It is used to analyze the data. It is used to manipulate the data for statistical purpose. We can able to easily produce the well-designed publication-Quality plots. we can also include maths symbols and formulas also. RODBC, Gmodels, Class, and Tm are some of the packages. Machine learning uses this Package. These packages help in the easier implementation of machine learning.
  • Lisp: Lisp is one of the oldest Language for the development of AI. We can able to process the symbolic information. It has the excellent prototype. It is easy for the dynamic creation of the object. We use automatic garbage collection for this.  It allows recompilation of files. It also allows the function even in the run time of the program.
  • Prolog: It is a designing expert of AI development. Pattern matching is one of the important features of Prolog.  Tree-based data structure is another important feature of Prolog. Automatic backtracking is also one of the important features. These features provide a powerful programming in framework. It also provides flexible programming in framework.  Medical project uses this.
  • Java: Java is also used for the development of AI. It is easy to use. It is easy for debugging. It provides package services. It is simple to work with large scale projects. These are some of the benefits of Java.

Machine Learning Applications: It plays a major role in the field of AI. One of the real time Example for machine learning is Image recognition. Another best real time example is speech recognition. In Medical diagnosis we use Machine learning. Statistical also uses Machine learning.

It is also used in the financial and banking sector. It improves our life style. This can be possible by improving the modern technology.

Trending IT Courses in 2019 Part – 2

January 24, 2020
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