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Why is Python of Paramount Importance in Data Analytics?

Posted on August 2, 2021March 29, 2024 by Imarticus Learning

Reading Time: 2 minutes

Why is Python of Paramount Importance in Data Analytics?

Python is a programming language that has become the de facto standard for many data analysts, programmers, and scientists. One of Python’s benefits over other languages like Java or C++ is how it allows developers to code much faster than they could in those more mature frameworks since there are fewer syntactical restrictions on what you can do with objects and variables moreover, as we move towards ‘big data analytics – where large amounts of information need to be analyzed quickly – this increased productivity.

In this article, we will discuss how Python is an essential and popular tool for data science.

The program started with a demonstration of the latest AI that analyzes pictures to identify their contents by automatically assigning them tags such as “walking dog” or “standing person.”

Python for Data Science

Python has been an increasingly popular language for data science because it is easy to use and free. The Python programming environment, called IDLE or Idle-python, even comes with a small editor that provides syntax highlighting, making coding easier and more fun – perfect for people who are just starting!

It also includes many libraries such as NumPy, SciPy, Matplotlib (among others), which make working on scientific projects much simpler than if you had to do all the work yourself from scratch in another type of language like C++ or Java. If you want to learn how this powerful tool can help your future career prospects, then check out our data science course.

How to get certified in python?

You can become a professional developer in the fast-growing python programming language with certification. Python is an open-source, high-level programming language that enables you to quickly and easily solve complex problems.

This certificate program will teach students about object-oriented design principles and how they are applied in practice through hands-on exercises using actual code samples of real-world applications written by industry professionals.

The course covers data structures, algorithms, functional decomposition concepts (including recursion), and file handling techniques for various types of files, including binary formats like PDFs or encrypted ZIP archives. They apply to any application that includes many domains, from bioinformatics research right up to web development. Upon completion, participants should be able to make informed decisions when evaluating new projects.

To get more information about Python certifications please visit Imarticus Learning.

Imarticus Learning is a leading technology-driven institute that gives accredited certifications in data science with the collaboration of KPMG.

Conclusion: Python is a simple programming language that has many uses in data analytics. It can be used to process and analyze large sets of data and create visualizations for those datasets.

This article explores the importance of Python in Data Analytics and explains how it’s helpful across industries from finance to science research, entertainment marketing, and more! Do you use Python? What do you think about this post on its usefulness? Please share your thoughts with us below, or contact our experts at Enquire Now to help answer any questions.

Why Data Security Should Be a Top Priority?

Posted on July 28, 2021April 2, 2024 by Imarticus Learning

Reading Time: 3 minutes

Data science has revolutionized how businesses utilize information and leverage it to achieve their objectives. Businesses in the contemporary heavily rely on the use of data and analytics to obtain valuable insights about the market, customer base, competitors, etc.

Decision-making is not random in the modern digital age; it involves complex analysis and data churning to make outcomes more favorable and predictable.

Data science training has gained new heights in the past decade and there is a growing demand for professionals in this field, making the data science career one of the most sought after and rewarding ones.

Today, data stored by companies is not just some random piece of information that no one looks at twice, it has gained a new significance and is kept confidential. Cybersecurity is a concern in the modern digital era, where data is not less than fuel for businesses.

Cybercrime has also gained momentum with the growing demand for data by businesses. Data security should be a top priority for businesses in the light of recent cybercrimes that has affected both small and large businesses. One can easily opt for a data science course from reputed organizations like Imarticus Learning and learn all about this discipline.

Data Security a Necessity

Data security entails protecting vital digital information from theft or corruption. Data security ensures that your confidential information is safe and secure and could not be altered by any other party.

Businesses also hold personal information related to their customers and protecting it is a necessity for the longevity and growth of the business. Let’s delve deeper into why data security should be a top priority for businesses.

Potentially Unwanted Program (PUP)

Data security is not just needed for large organizations but even the smaller ones need it to keep their confidential information secure. Potentially Unwanted Programs, popularly termed PUPs are one of the major data security threats that affect small businesses. Small businesses generally receive complimentary bundle software with the purchase of new tools.

At times, some spyware could get installed while working on these software packages if not sorted out properly. The consequences could be complete system slowdown, hacking, etc. One can easily avoid these PUPs by installing software from reliable sources only and inspecting the installed ones from time to time.

Increase in Data Breach Damages Reputation

Data breaches can turn out to be very costly for businesses. It can lead to a decrease in sales volume and also impact an organization’s reputation negatively. People will be more reluctant to trust a brand that has had cases of a data breach in the past and they won’t be comfortable availing products or services from these organizations.

A famous example of a data breach that resulted in a huge loss was Target’s 2013 breach, the profits decreased by more than 40% in the last quarter due to this event.

Hampers Productivity

Data breach not just hampers the reputation of an organization but also has other major consequences. It also leads to a reduction in productivity as it disrupts the whole process and systems in place that effectively integrate everything. Addressing and resolving the breach issue takes a little time but making things normally takes more than that and this causes a chain reaction of reduced productivity which adds to further losses for a business.

How Data Science is Taking Over Mobile Marketing?

Posted on July 26, 2021March 28, 2024 by Imarticus Learning

Reading Time: 3 minutes

How Data Science is Taking Over Mobile Marketing?

Did you know that more than 50% of global web traffic is on mobile? Mobile devices have already taken over the internet; thanks to their ease of access and flexibility. That is why they have become a necessary part of every digital marketer’s strategy.

You might be wondering how mobile marketing and data science have come together and working in sync to gain more traction in the global marketing scene.

The following points will cover how data science improves mobile marketing and why more and more marketers are utilizing this robust skill. Read on…

Use of Data Science in Mobile Marketing

Here is how data insights can improve mobile marketing campaigns, making them much more effective:

Better Targeting

Data science enables marketers to find patterns in large amounts of data. By using data science, marketers can identify unique customer behavior and hence, create better marketing campaigns. It also assists marketers in measuring the impact of their current marketing endeavors through competitive analysis, predictive analysis, and other methods.

Data science training allows marketers to find out what percentage of their targeted audience behaved in what way. For example, they can learn who showed the most interest and who did not express any interest at all. Such data can be useful in optimizing future campaigns.

Chatbots

They are a prominent example of how technology has revolutionized interactions between brands and consumers. Chatbots are automated machines that mimic human conversations. They greet users visiting a website and help them find answers to general queries.

On mobile, chatbots are quite effective as they mimic the behavior one has while texting. Therefore, they make it more comfortable for new users to get familiar with the brand. They also improve the customer service quality of the company. Note that chatbots are AI- and data science-based tools.

More Data, More Insights

A huge aspect of data science is data collection and cleaning. Data scientists can improve the data gathering systems and methods used by an organization and enhance the quality of data used in analytics. They fix missing values, help get rid of overfitting, remove outliers, and prepare the data for the necessary procedures.

This way, the accuracy of their data science implementations remains high. As the quality and quantity of the generated data increases, it would allow the data scientist to gather more insights and enhance their results.

Enhanced Personalization

Data science enables marketers and businesses to offer more personal and special experiences to their users. Many companies are already utilizing data science to offer personalized experiences to their customers. For example, when you use the Amazon app, it recommends the products that match your past history and which have been bought by other users who showed interests similar to yours.

How to Become a Data Science Professional?

As you can see, there are many applications of data science in digital marketing. However, digital marketing is not the only field that benefits from the expertise of data scientists. Other industries that rely heavily on talented data science professionals include finance, healthcare, transport, and entertainment.

To pursue a career in data science, you will need to join data science courses. The best data science courses with placement will help you in getting a certification in the domain. Recruiters in this field are always on the lookout for certified professionals. Hence, you would benefit greatly from getting a certification in data science.

However, in the current time, when there is a contagious virus spreading globally, it would be best to join a data science online course. That is because an online data science course will allow you to study safely from the comfort of your home without going anywhere.

Lockdowns or curfews would not affect your studies and you would not be at the risk of exposing yourself to the virus.

In the nutshell, there are many applications of data science in mobile marketing. The implementation of data science in mobile marketing will only get better with time. Hence, if you are interested in pursuing a career in the data science field, now would be a perfect time. If you want to join a data science online course, then you can go here.

Python Developer Salary in Terms of Job Roles

Posted on July 13, 2021April 2, 2024 by Imarticus Learning

Reading Time: 3 minutes

What is Python?

The second most liked Programming language in the world, Python is one of the widely used term in the web-development world.

Who are Python Developers?

The web-developers who design and code the software applications with the help of Python language are referred to as Python Developers.

Roles and responsibilities of Python Developers

Python Developers as Data Scientists

Major businesses in today’s world require tools and skilled people for the data-related tasks such as data collection, data cleaning and processing.

Data Scientists are the programmers who do these tasks for the organizations. Data Scientists gather a large quantity of data and convert it into a useful form, followed by recognizing data-analytics solutions for organizational growth.

Data Scientists encourage the data-driven approach in organizations to deal with complex business problems.

Artificial Intelligence

In AI Python Developers create and implement the required Machine Language algorithms. They analyze the success and failure of the algorithm and rank them according to their performance for future use. Training and Retraining ML algorithms is one of the key tasks performed by Python Developers.

Salaries of Python Developers in India

The changing focus of the organizations on data-driven solutions is resulting in a manifold increase in the salaries of the Python Developers. In the coming years the demand for AI-skilled people will increase, and hence the salaries.

Major IT giants like Google, YouTube, Amazon etc. are adopting Python-driven systems and hence, manifold increases in the salaries of the Python Developers.

The entry level salary of a Data Scientist is approximately INR 500,000 per annum (Source: payscale.com) Data scientists with 5-9 years of work experience gets +100% more salary in India. With the experience and refinement of the skill, the salary of Data scientists shows exponential growth.

An entry-level engineer who develops the ML Algorithms earns the average salary of approximately INR 700,000 annually. With further experience and refinement of the skills, the average salary of the Data Scientist shows exponential growth.

Factors affecting the Salaries of Python Developers

Job location: Considering the increasing demand of Python Developers, not only in India, the faster salary growth is visible in UK and in US.

Location	Approximate Average Salary in INR
Gurgaon	700,717
Bangalore	669,787
Delhi	600,000
Mumbai	579,728
Chennai	540,131
Hyderabad	475,000

UK Python Developer annual salary	£67,000
US Python Developer annual salary	$117,000

Experience:

Python Developer	Approximate Salaries in INR
Entry Level Python Developer	427,293
Med-Level Python Developer	909,818
Experienced Python Developer	1,150,000

Refined Skills:

Mere understanding of the Python is of no use, till is integrated with the problems and solutions. How one uses the well-known Python tools define the person’s skill set, which is a determiner of the salary.

Job Role:

Python Developer	Approximate Average Salary in INR
Data Scientists	700,000
ML Engineer	670,000
DevOps Engineer	660,000
Software Engineer	500,000
Web Developer	300,000

Should know more interesting things about Python programming training and Python career.

Which Career Is More Promising: Data Scientist or Software Developer?

Posted on July 11, 2021January 23, 2024 by Imarticus Learning

Reading Time: 4 minutes

As per a study, about 2.5 quintillion bytes of data are produced every day. The number is about to increase by many folds in the future. The pace at which data is being generated demands more data science professionals. That’s the reason data science jobs are now regarded as one of the highest-paid jobs across the globe. And, people are joining data science training to explore a career in data.

As, firms are hiring employees who can build technical applications, and collect, analyse and predict data. That’s the reason students are enroling in data science training courses that are best and give job assurance.

Companies are keen to employ data scientists, data analysts, web developers, full-stack developers, and much more technology professionals. According to Imarticus Learning and Analytics India Magazine’s research, there will be a 30.1% increase in data science and analytics jobs in 2022 compared to 2021.

However, more competition in Information Technology requires software career enthusiasts to learn the latest technology for survival. The primary responsibility of data scientists is business analytics. They also work on constructing data and software products using algorithms. The demand for data scientists is high due to the shortage of skilled people for start-ups and corporates. Global companies are searching for professional data scientists to up their game in this competitive world.

Who is a data scientist?

Data scientists collect and interpret large amounts of structured and unstructured information. They analyse, process, and model data before interpreting the findings to develop actionable plans for businesses. The data scientist uses computer science, statistics, and mathematics for data interpretation.

Who is a software developer?

Software developers are the thinkers and doers behind computer programs of all kinds. While some software developers specialise in a single program or application development, others build massive networks or underlying systems that trigger and power other programs. As a result, there are two types of developers: applications software developers and systems software developers.

The approach of data scientists and software developers

To use and organise data in a structured or unstructured form, a data scientist becomes essential and can master the art with the right Data Science Training. Hence, there is a need for a data scientist, with sound knowledge of handling data that demands the best data science training and experience.

Software developers collaborate with computer programmers to test the convenience of using a software program. The component that powers digital systems is software which is the core element of computers. To be precise, software engineering can be termed as the application of engineering principles for designing, structuring, developing, and implementing software. An efficient software developer understands the client’s needs and then develops the software as a solution to the client’s needs.

Important tools used in software development and data science

To be productive, data science career aspirants should be knowledgeable about statistics, software programming, and analytical thinking. In addition, a data scientist should have the urge to learn and be updated about new programming languages. Python, Swift, and Ruby, apart from mastering SQL, Spark, machine learning, and Hadoop are the most important to learn.

A software developer should know the latest programming languages along with a bachelor’s degree in computer science. A good understanding of computer programming by understanding the functionality of new tools is a plus. Along with various skills, a software developer should possess to flourish in the field of analytical skills. Followed by attention to detail, creativity, problem-solving techniques, and interpersonal skills are vital.

The pay scale for data scientists versus software developers

A report from Payscale.com reveals that the median salary for a data scientist is around 6,00,000. It is not much different in the US. According to a survey by Glassdoor, the median salary for a data scientist is $91470, which is great. So, it is evident that data science can offer ample money and growth. But, the salary also depends on the qualifications, skills, experience, and location. Firms also play a vital role in deciding the pay.

Currently, the median salary for software developers is $1,00,80 and the growth in employing software developers is set to increase by 24% in a decade. In addition, an estimate states that there are 3.7 billion mobile users worldwide. It demands newer and better applications for smartphones and tablets. It increases the demand for software developers globally. Factors that affect the salary of a data scientist are similar to those of software developers.

Roles and job titles for a career in data science versus software development

A few important job titles that a data scientist can do are data architect, data scientist, and business analyst. In fact, the roles of data administrator, and business intelligence manager are also in demand. Global companies in the digital world, like Facebook, Twitter, IBM, and Apple, are racing to hire the best data scientists. So, the demand for data scientists is growing like never before.

Some of the interesting career options that connect to software developers are Computer programming. Computer system analysts, web developers, computer network architects, and computer hardware engineers are also on the list. Though data science is a relatively newer concept than software development, it is increasing rapidly.

Conclusion

This article has showcased the digitisation, technology, data rise, and demand of technology professionals. The high demand for data scientists, and software developers have given rise to the skills gaps. If you are eager to learn new tools for efficient data handling, you can grab a chance to flourish as a data scientist in the long run. Enrol for the best data science training course and enter the world of data.
But, selecting the best institute for data science training is not easy.

You should enquire about the faculty, course curriculum, and the job assistance that they provide. It is always good to get interview assistance, career monitoring, and resume-building tips from the experts. If the institute provides all this then it is the best for the learners.

It should be your priority to learn coding and programming if you want to become a software developer. Furthermore, the amount of data being produced every day is high, which signifies long-term growth and attractive salary await a talented data scientist. So, in the end, we can say that both careers in data science and software development are promising. It depends on individual preferences and interests. Both, career options are good and offer career growth.

For more details, you can also search for – Imarticus Learning. You can drop your query by filling up a form from the site. You can also contact us through the Live Chat Support system. To explore more you can visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, and Gurgaon.

Advanced Data Science Skills to Stay Relevant in the Post-Pandemic World!

Posted on July 9, 2021March 26, 2024 by Imarticus Learning

Reading Time: 3 minutes

The need to upskill to meet the dynamic demands of a technology-first world has been around for the past few years; it has only become more urgent in the wake of the COVID19 pandemic. The emergence of new technologies such as Artificial Intelligence, machine learning and data science has set the tone for the future.

In the post-COVID19 world, there are a few advanced data science skills that, when added to the toolkit of a data scientist, can make or break their career.

To ensure that your core competencies are strengthened as a data scientist, you can sign up for a comprehensive data science training course that explores the following:

#1: Geospatial Technologies

With more people working on data-driven decision processes, geospatial data has helped better planning and processing of the system. This knowledge has proved invaluable in tracking the COVID19 outbreak all over the world; come the near future, geospatial technology will likely be extended to other research areas as well.

A geospatial data scientist will need to sift through vast geographic and demographic datasets that hide gold nuggets of insight across diverse research topics.

#2: Natural Language Processing (NLP)

NLP gained traction even before the pandemic reared its ugly head. That said, it is only set to increase in importance and reach in a post-pandemic world.

Most organisations often implement self-service systems, such as bots that come with multi-language optimized NLP to help solve customer problems.

Data scientists of the future must understand NLP and master it enough to help companies develop automated solutions for a better post-COVID outcome.

#3: Computer Vision

Computer vision is an artificial intelligence field which trains computers to interpret and comprehend the visual world. It uses digital images from cameras and videos as well as deep learning models to recognise and distinguish objects correctly. With the help of algorithms, computer vision is also integrated to follow up with a programmed response. In current scenarios, computer vision has proved helpful in containing the outbreak and regulating quarantines and social distancing in cities across the world. In the future, where maintaining distance might become the norm, data scientists specializing in computer vision will automatically become more hire able.

#4: Data Storytelling

With data analytics becoming a prime concern for companies across industries, the need for good data storytelling has increased. The benefit of data analysis is not just in the evidence it provides but also in how it is made meaningful and impactful. Gripping storytelling makes it easier for non-data-scientist stakeholders to understand the value of the information and the possibilities it poses.

Data presented as contextual stories, rather than isolated data points, makes individuals more likely to understand the impact, decipher patterns and make more informed decisions.

In turn, as data storytelling would help business leaders with powerful insights, it would help them better prepare for the post-pandemic world’s opportunities.

#5: Explainable AI

Considering that AI has reached into nearly every area of human life, companies must be able to trust computers and their decisions. This is where the need for explainable AI emerges. Until now, companies build and sourced AI models that predicted accurate insights from large data dumps. In a post-pandemic world, they may well shift to models that also provide explanations for predictions. Explainable AI is a step forward in reducing the mistrust in non-human workflows. It makes AI systems more transparent and much fairer and all-inclusive than they were earlier.

Conclusion

Advanced data science skills are crucial to the cause of innovation and growth. Advanced upskilling is an integral step for data scientists looking to become more than relevant in the coming years.

Data Literacy Is Very Much a Life Skill– Here Are 4 Reasons Why?

Posted on July 8, 2021March 22, 2024 by Imarticus Learning

Reading Time: 3 minutes

The world is no stranger to data; in fact, in recent times, the world has found itself being bombarded by more facts and statistics than ever before. At quite the same speed, people have also been faced with fake facts, viral social media forwards with little to no truth.

Being data literate has moved from being a niche requirement to being a life skill that allows people to distinguish between fact and fiction. Data literacy is a way of exploring and understanding statistics in a manner that provides meaning and insight.

This meaning isn’t relegated only a data science career or to businesses looking for an edge over competitors. It applies to society and its interconnected systems as a whole.

To drive the point home, here are a few advantages that data literacy offers when looked at as a life skill:

Recognising the Sources of Data

Data is everywhere, especially in a world where nearly everything is digital and produces and consumes more data. There are many different ways in which data exists, including graphs, images, text, speech, video, audio and more. Recognising the different sources of data is the first step towards working with data. The sources, formats and types all have a role to play in determining the use (and potential misuse) of data, which in turn drives data literacy.

Acknowledging the Self as a Consumer and Producer of Data

The messages you send, images you post and likes you leave on social media are examples of data. So are the transactions you make and the searches you conduct on search engines such as Google and Bing. Today, nearly every single person in the world is a data producer; those sources of data are vital to value-generating processes across industries and markets.

Similarly, people are daily consumers of data even if they don’t perceive it as that. The COVID19 pandemic has brought this into the light even further– front page statistics are at the back of everyone’s mind, as are the names of containment zones and the best practices for sanitisation.

Recognize Biases and Fallacies

Data literacy gives the people more agency to call out those producing statistical data that is biased, twisted or outright incorrect. As citizens, consumers and valued members of a society, it is imperative that every individual is able to identify false promises or glossed-over issues that allow wrong-doers to continue as they were.

Data literacy gives people the power and the evidential backing to call out those intentionally or unintentionally propagating mistruths and fallacies through awry statistics. This way, data literacy plays a pivotal part in politics, economics and ethics of a society, indeed of the world.

Improves Data Storytelling

Instead of data points presented on their own, data that is presented as descriptive stories make individuals more likely to understand the effect, decipher trends and make more educated decisions. While data storytelling is imperative to learn for those taking a data science course, it is just as important for members of all other fields to better present their arguments such that they catch eyes.

Data has never been a strictly academic factor; however, it has often been painted as complicated, invasive or unnecessary to penetrate everyday lives. Data storytelling ensures that data is taken even further out of that box and presented as actionable insights to even the average Joe Bloggs.

Conclusion

The focus on data science and literacy shouldn’t just be restricted to mathematics and algorithms but everyday applications of data in daily lives. Data understanding allows people all over the world to take more control of what they’re producing and consuming. Data fluency and literacy is achievable by all.

How Imarticus Helps For A Data Science Career in Pandemic Times?

Posted on July 5, 2021March 26, 2024 by Imarticus Learning

Reading Time: 3 minutes

Data-driven strategies have shot up in popularity after the coronavirus pandemic wreaked havoc on business plans. Data science is a key player in sustaining businesses not just now, but in the future when similar turbulent circumstances threaten to bring down shutters on previously stead organisations.

As a result, hundreds of companies across India and overseas are looking to add more data scientists to their repertoire. This comes from a need to drive more data-driven decisions and make businesses more resilient to change.

Here are some specific reasons that make a case for how choosing a data science career can be beneficial in times like these:

A need for general expertise

While previously companies favored data science specialists, today they prefer generalists and Jacks of all trades. While specialists come with in-depth knowledge and specific skill sets, they often cannot think beyond their domain. Companies today need someone who has skills to use across the board so that they can both learn on the job and be useful where they’re needed.

A need for understanding project flows

Many companies who are delving into data science now are probably unsure of their footing and their way forward.

A data scientist is critical in companies like these, as they bring expertise to the table and understand the flow of projects much better than anyone else. With a data scientist at the helm, all other players in the process can fall into place. This reduces the pressure on upper management to figure out project flows; they can now leave it to the experts.

Higher chances for growth

Data scientist generalists are more likely to grow with the company– something many organisations prefer. Unlike a specialist, who already has a defined skill set, rookie data scientists can be shaped and molded into an ideal employee for the company. In the process, the data scientist becomes an intrinsic part of the organisation, learns business tactics and applications and develops skills through experience rather than through specializations. As a result, they become both experts and creative problem-solvers.

Immediate requirements

Businesses are struggling to stay afloat during the aftermath of the pandemic and are realizing their urgent need for data-driven business plans. As a result, many of them have put out feelers and immediate job offers for data scientists. This is in complete contrast to other fields that are seeing scores of job cuts, furloughs and pink slips, and goes to show that data science is only increasing in popularity.

It is worth keeping in mind that, despite recruitment into data science roles, many companies have slashed budgets and can’t afford to pay more experienced scientists at this stage. Rookie data scientists form the perfect compromise– they’re eager to learn, have the necessary skills and can be accommodated within tighter budgets without reduced salaries.

Opportunities for upskilling

As rookie data scientists settle into their roles, many companies consider upskilling them for higher positions or specific technical projects.

This is an invaluable opportunity for fresh data scientists as the company takes care of all the costs and only asks for your attention and application in exchange. Adding a data science course to your CV will also help you get a leg up on the competition when you’re ready to switch roles or companies.

The final word

The data science landscape has shifted significantly in response to the coronavirus pandemic. As a result, rookie data scientists who are only just entering the field have a once-in-a-lifetime chance to make their mark and cement their place for when things stabilize.

All You Need to Know About Hadoop!

Posted on July 3, 2021April 2, 2024 by Imarticus Learning

Reading Time: 3 minutes

Hadoop is an open-source software framework to store data and running applications on clusters of commodity hardware. It provides massive storage for different data types, enormous processing power, and the ability to handle virtually limitless concurrent tasks or jobs.

Hadoop programming is a vital skill in today’s world for people looking to build a career in Data Science. Hadoop processes large data sets across clusters of computers using simple programming models called MapReduce jobs.

Importance of Hadoop for Organizations?

The ability to store & process enormous data quickly makes Hadoop development a much-needed thing for organizations.
Hadoop’s distributed computing model processes big data in no time. With more computing nodes, you have better processing power.
Hadoop is equipped with fault tolerance and guard against hardware failure. If a node goes down, tasks are automatically redirected to other nodes to ensure that distributed computing doesn’t fail.
You can quickly scale your system and handle more data simply by adding nodes.

How is Hadoop Used?

Hadoop development is used in a variety of ways. It can be deployed for batch processing, real-time analysis, and machine learning algorithms. The framework has become the go-to technology to store data when there’s an exponential growth in its volume or velocity. Some common uses of Hadoop include:

Low-cost storage and data archive

Hadoop stores and combines data such as transactional, sensor, social media, machine, scientific, clickstreams, and the modest cost of commodity hardware makes it more likable. The low-cost storage lets you keep data and use it as & when needed!

Secure for analysis & discovery

Since Hadoop was designed to deal with massive data, it is efficient in running analytical algorithms. Big data analytics on Hadoop can help organizations operate efficiently, uncover opportunities and derive next-level competitive advantage. This approach provides opportunities to innovate with minimal investment.

Data lake

Data lakes back up data stored in original form. The objective is to offer a raw view of data-to-data scientists and analysts for discovery and analytics. It helps them ask new questions without constraints. Data lakes are a huge topic for IT and may rely on data federation techniques to create logical data structures.

IoT and Hadoop

Hadoop is commonly used as a data store for millions of transactions. Massive storage and processing allow Hadoop to be used as a sandbox to discover and define patterns monitored for instruction.

Build a Career in Data Science:

Data analytics is a lucrative career and is high in demand and low in supply. It’s a field requiring plenty of expertise to master. But what if you have the ambition but lack the know-how? What do you do?

Data science courses or Data Analytics courses can help you gain better insights into the field. For a person to be technically sound, education, training, and development are the foremost steps.

Imarticus Learning offers some best data science courses in India, ideal for fresh graduates and professionals. If you plan to advance your Data Science career with guaranteed job interview opportunities, Imarticus Learning is the place to head for today!

The certification programs in data science are designed by industry experts and help students learn practical applications to build robust models and generate valuable insights.

The rigorous exercises, live projects, boot camps, hackathons, and customized capstone projects will prepare students to start a career in Data Analytics at A-list firms and start-ups throughout the program curriculum.

The industry connections, networking opportunities, and data science course with placement are other salient features that draw attention from learners.

For more details on the transformative journey in data science, contact Team Imarticus through the Live Chat Support system and request virtual assistance!

Complete Guide: Object Oriented Features of Java!

Posted on June 5, 2021April 2, 2024 by Imarticus Learning

Reading Time: 4 minutes

What is OOPs?

OOPs, the concept brings this data and behavior in a single place called “class” and we can create any number of objects to represent the different states for each object.

Object-oriented programming training (OOPs) is a programming paradigm based on the concept of “objects” that contain data and methods. The primary purpose of object-oriented programming is to increase the flexibility and maintainability of programs.

Object-oriented programming brings together data and its behavior (methods) in a single location(object) makes it easier to understand how a program works. We will cover each and every feature of OOPs in detail so that you won’t face any difficultly understanding OOPs Concepts.

Object-Oriented Features in Java

Classes
Objects
Data Abstraction
Encapsulation
Inheritance
Polymorphism

What is Class?

The class represents a real-world entity that acts as a blueprint for all the objects.

We can create as many objects as we need using Class.

Example:
We create a class for “ Student ” entity as below

Student.java

Class Student{
String id;
int age;
String course;
void enroll(){
System.out.println(“Student enrolled”);
}
}

The above definition of the class contains 3 fields id, age, and course, and also it contains behavior or a method called “ enroll ”.

What is an Object?

Object-Oriented Programming System(OOPS) is designed based on the concept of “Object”. It contains both variables (used for holding the data) and methods(used for defining the behaviors).

We can create any number of objects using this class and all those objects will get the same fields and behavior.

Student s1 = new Student();

Now we have created 3 objects s1,s2, and s3 for the same class “ Student ”.We can create as many objects as required in the same way.

We can set the value for each field of an object as below,

s1.id=123;

s2.age=18;

s3.course=”computers”;

What is Abstraction?

Abstraction is a process where you show only “relevant” data and “hide” unnecessary details of an object from the user.

For example, when you log in to your bank account online, you enter your user_id and password and press login, what happens when you press login, how the input data sent to the server, how it gets verified is all abstracted away from you.

We can achieve “ abstraction ” in Java using 2 ways

1. Abstract class

2. Interface

1. Abstract Class

Abstract class in Java can be created using the “ abstract ” keyword.
If we make any class abstract then it can’t be instantiated which means we are not able to create the object of an abstract class.
Inside Abstract class, we can declare abstract methods as well as concrete methods.
So using abstract class, we can achieve 0 to 100 % abstraction.

Example:
Abstract class Phone{
void receive all();
Abstract void sendMessage();
}
Anyone who needs to access this functionality has to call the method using the Phone object pointing to its subclass.

2. Interface

The interface is used to achieve pure or complete abstraction.
We will have all the methods declared inside Interface as abstract only.
So, we call interface 100% abstraction.

Example:
We can define interface for Car functionality abstraction as below
Interface Car{
public void changeGear( int gearNumber);
public void applyBrakes();
}

Now, these functionalities like changing gear and applying brake are abstracted using this interface.

What is Encapsulation?

Encapsulation is the process of binding object state(fields) and behaviors(methods) together in a single entity called “Class”.
Since it wraps both fields and methods in a class, it will be secured from outside access.
We can restrict access to the members of a class using access modifiers such as private, protected, and public keywords.
When we create a class in Java, it means we are doing encapsulation.
Encapsulation helps us to achieve the re-usability of code without compromising security.

Example:
class EmployeeCount
{
private int numOfEmployees = 0;
public void setNoOfEmployees (int count)
{
numOfEmployees = count;
}
public double getNoOfEmployees ()
{
return numOfEmployees;
}
}
public class EncapsulationExample
{
public static void main(String args[])
{
EmployeeCount obj = new EmployeeCount ();
obj.setNoOfEmployees(5613);
System.out.println(“No Of Employees: “+(int)obj.getNoOfEmployees());
}
}

What is the benefit of encapsulation in java programming
Well, at some point in time, if you want to change the implementation details of the class EmployeeCount, you can freely do so without affecting the classes that are using it. For more information learn,

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What is Inheritance?

One class inherits or acquires the properties of another class.
Inheritance provides the idea of reusability of code and each sub-class defines only those features that are unique to it ghostwriter diplomarbeit, the rest of the features can be inherited from the parent class.

Inheritance is the process of defining a new class based on an existing class by extending its common data members and methods.
It allows us to reuse code ghostwriter bachelorarbeit, it improves reusability in your java application.
The parent class is called the base class or superclass. The child class that extends the base class is called the derived class or subclass or child class.

To inherit a class we use extends keyword. Here class A is child class and class B is parent class.

class A extends B
{
}

Types Of Inheritance:
Single Inheritance: refers to a child and parent class relationship where a class extends another class.

Multilevel inheritance: a child and parent class relationship where a class extends the child class Ghostwriter. For example, class A extends class B and class B extends class C.

Hierarchical inheritance: where more than one class extends the same class. For example, class B extends class A and class C extends class A.

What is Polymorphism?

It is the concept where an object behaves differently in different situations.
Since the object takes multiple forms ghostwriter agentur, it is called Polymorphism.
In java, we can achieve it using method overloading and method overriding.
There are 2 types of Polymorphism available in Java,

Method overloading

In this case, which method to call will be decided at the compile time itself based on the number or type of the parameters ghostwriter deutschland. Static/Compile Time polymorphism is an example of method overloading.

Method overriding

In this case, which method to call will be decided at the run time based on what object is actually pointed to by the reference variable.