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amit4002020 · 5 years ago

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How to Get a Job in Machine Learning Technology

You have completed your engineering degree and are now looking for a break from the extremely slow labor market. Unlike ten years ago, when engineering studies inevitably led to comfortable employment, the focus on the job market has shifted from traditional jobs to technology-driven roles. As more and more companies use artificial intelligence technologies and change virtually every industry, there is a demand for engineers and data scientists who can work with a variety of learning platforms and languages. Automation has increased significantly.

The artificial intelligence and machine learning market is expected to reach $ 8.8 billion in 2022. Unfortunately, companies find it difficult to find people with relevant experience. This has led to a huge deficit in the number of qualified machine learning (ML) developers. In this article, we look at some factors that can help you become a sought-after machine learning expert.

How to become a Professional machine learning engineer

Learn the require Languages

If you need a career in machine learning, it is important to have a good command of programming languages such as C ++, R, Python, Java and SQL. Among these, Python and R are the most popular programming languages for machine learning and are often a prerequisite for most machine learning courses.

Solid Knowledge of Data Modeling

You don't have to be a data scientist to become a machine learning expert. However, you should learn about data modeling and evaluation to identify and analyze unstructured data models. Machine learning engineers need data modeling to find data models, predict the properties of invisible instances, and determine the level of precision / error.

Solid knowledge in Statistics Skills

A good understanding of statistics and probability is the cornerstone of machine learning. Statistical concepts such as mean, standard deviations and Gaussian distributions are just as necessary as the probability theory for algorithms such as naive bays, Gaussian mixed models and hidden Markov models.

Learn ML Algorithms

Machine learning professionals must have a solid understanding of the theory of algorithms and how they work. This includes knowledge of topics such as partial differential equations, convex optimization language, quadratic programming and more.

Other technical skills that a machine learning expert must master are UNIX tools and advanced signal processing techniques.

While mastering technical skills is extremely important, it is also important to have good general skills. Machine learning professionals must have good expertise, excellent communication and problem-solving skills. Most importantly, a machine learning engineer has to keep up with the rapidly changing technology. As new technologies and paradigms explode on the scene, you need to stay up to date and improve your knowledge regularly. This could be achieved by subscribing to online courses, subscribing to the latest technology blogs, and regularly following research articles.

According to Analytics India Magazine, around 78,000 jobs in data science and machine learning were vacant in India in 2017. If you master the key skills of machine learning, you will find great opportunities in this area. Given the rapid growth of AI and machine learning, no matter what sector you work in, new-age technologies will soon affect your work if not. not finished yet. For this reason, it is important to improve the skills to follow these revolutionary trends in order to remain competitive in the world managed by AI.

Read More: 7 tips to get success in machine learning

Machine Learning Job Roles

There are various roles available under machine learning technology. Let’s discuss about those roles:

Machine Learning Engineer

Machine learning requires the knowledge of programming and computer science background. As a machine learning engineer you must be able to play with algorithms, data sets and coding part.

Data Scientist Role

Data scientists have a high degree of mathematical and IT know-how. With regard to ML, they both rely on data records at the same time. The art of data science combines theory and statistics.

These two work together to behave in a certain way towards large amounts of data. Using algorithmic processes, they extract information from data.

Artificial Intelligence Role

AI is known as computer intelligence and is the ability of machines to be intelligent. This intelligence uses arguments that are similar to human intelligence. Everything is based on theory.

But the goal is to make the machines work, learn and think like people.

Software Developer (ML)

Software engineering is similar to ML engineering in terms of basics and programming.

The exception concerns the deepening of computer science in software development through the design of systems. These engineers design, build, teach, and experiment with ML models.

Conclusion

I hope you have understood that how can you get job in machine learning. And to get job in machine learning what skills you must know. So follow this article and prepare for machine learning engineer. You will definitely get a good job in this technology.

NearLearn is the best institute that provides the best machine learning training in Bangalore. It provides other courses also like artificial intelligence, data science, blockchain, deep learning, and full-stack development etc.

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amit4002020 · 5 years ago

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How to become successful React Native Developer?

React Native, a framework for building cross-platform applications, recently made headlines for the right reasons. It is supported by a renowned team from Facebook and the entire JavaScript community. The framework aimed to achieve record heights with the slogan "learn once, write everywhere". After Facebook created React Native Open Source, there was a lot of discussion about whether cross-platform applications could be built using JavaScript. Most developer communities were unsure whether building applications for two types of platforms using JavaScript was a good solution. However, React Native surprised everyone with its exceptional performance, which is comparable to native applications (iOS / Android). However, since it was a relatively new framework, developers had many performance issues when building complex applications.

We'll look at six key tips to help mobile developers become better React Native developers.

If you are planning to become a react native developer and want to make future in react native development field then follow these 6 steps that will help you to become a react native developer. Here I am going to share a guide to make your react native developer dream.

Correct choice of Navigation Library is important from the start

React Native has a long history of pain and discomfort related to navigation. At the beginning of version 0.5, many navigation libraries were published and outdated, but only a few managed to maintain the effectiveness and usability of a native application. However, there can be many situations in which developers may find that the navigation library used in their application is not helpful for a better user experience. One possible example of such a use case is Airbnb, which has found that React Navigation - the recommended navigation library for React Native - does not work with its Brownfield application. For this reason, Airbnb developers have created their own navigation library, which is the second most used navigation library after React Navigation.

Read More: Why Corporate Training is Important for the Business

Focus on JavaScript

You must learn first react before interacting with react native. Note that without having knowledge of JavaScript, it is very difficult to learn react. So you first step should be to learn JavaScript. Then you have to go for react native. So first step focus on learning JavaScript then go to react native.

Learn Basic Fundamentals of ES6+

After you master the basics of JavaScript, you should start learning the latest version of the ECMAScript standard. While you may not need to use ES ^ + in React Native, this is an innovative and easy way to use JavaScript. You can work optimally while creating responsive native apps. ES6 + (ESNext) is equipped with a number of functions and syntax that make your coding experience much more convenient and easier.

Focus on React

After you become familiar with the JavaScript and ES6 + ecosystem, you can continue learning React. First, discover React Js, which offers the same interface as React Native. It is advisable to learn it from one or two tutorials to master the basics from different angles.

Focus on React Native

Once you understand the basics of using React, you should start using React Native now. You have access to a large number of free or paid online sources to learn how to respond to Native. Do your research and find the ones that will enable you to gain a good understanding of React Native.

Create Basic React Native Apps

On your way to becoming a responsive native developer, you will face a number of challenges. The transition between learning the code and actually building on React Native can be difficult and exciting. You need to find the right resources to learn how to develop on Reaper Native.

State Management

State management is a very important aspect in any serious application. If you are familiar with the development of mobile applications, you know that every component of an application has its own status. When building small applications, managing reports is not that difficult if you can manage reports using accessories. On the other hand, a real-time mobile application requires that your status be fully accessible throughout the application. Redux and MobX are two popular status management libraries for React Native.

After learning how to develop a simple application in React Native, you need to prepare for some complex application scenarios. As your applications grow and become more complex, you need to choose a reliable architecture that leaves room for scalability and helps maintain problems in the future. Redux will help you here.

Conclusion

I believe that you have understood the 6 steps of becoming a react native developer. If you follow these steps then definitely you can also develop react native apps and become a successful react native developer.

NearLearn is the best institute that provides React native online training in India. It provides other courses as well like machine learning, artificial intelligence, data science, blockchain and full-stack development.

#online blockchain course #online blockchain trainig

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amit4002020 · 5 years ago

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How Ai Helps fight corona virus outbreak?

The COVID-19 epidemic (coronavirus) has affected most countries in the world. The number of deaths and deathbed patients increases from year to year. Since its first fall in China, the coronavirus has found its way into countries that are generally considered to be concerned about their health.

And to stop this epidemic, there is currently no vaccine or antidote that guarantees 100% results. However, modern technologies have covered us with various aspects that offer excellent services and help to combat this situation and improve over time. Yes, data science technologies destroy misunderstandings and offer excellent services for the search for coping mechanisms.

How Is AI Currently Helping Helm a Solution to Corona Outbreak?

Artificial intelligence helps to find the countermeasures that can stop the coronavirus epidemic. By analyzing the data available, it becomes easier to predict many factors and aspects that can help address this global situation.

1. Detection

Coronavirus is not that easy to spot. However, a company called Infer vision has released a corona virus detection mechanism that can further identify and prevent the virus epidemic by analyzing patient conditions. This AI-based program helps to identify and prevent the corona epidemic.

Healthcare providers can rely on this software solution to predict corona infection, and it helps manage countermeasures because it can detect and isolate symptoms to effectively stop the epidemic.

2. Robot for Sterilize Everything

During the coronavirus epidemic, hygiene is the most important aspect to counter the attack of the virus. Therefore, it is possible to assign robots to sterilize everything that comes in contact with people, everything that is used during the day. The sterilization process includes food, clothing, utensils, medical devices and much more.

Because robots are protected from the worry of virus infection, the work can be done more efficiently. In addition, robots can work without taking a break or anything else that an average person needs. You can therefore rely on these systems. These types of robots are already used to prevent infections in this way.

3. Drone Helps in Delivering Medical Supplies

Artificial intelligence and machine learning may not be directly involved in the delivery of drones, but they make an important contribution to the automation of these processes and to an excellent delivery experience. And now that contact or human contact has become dangerous due to the risk of corona infection, drones can provide efficient delivery services.

Because medical vendors can receive orders on their special channels, they can use efficient drone delivery systems to provide delivery services. For example, if a medical provider has installed and registered a Gojek clone app, health services can order the consumables they need to get the drug without having to contact people in between. It helps prevent hygiene and supports prevention requirements.

4. Track Outbreaks of Future

The likelihood of a corona outbreak increases with each passing day. But no measure can help prevent this epidemic. However, what we can do is predict the opportunities and deliver great services based on time and place. And AI is leading the way in effectively predicting and tracking these epidemics.

But how? By analyzing social media platforms, the latest news, updates and official government statements and much more, you get all the resources that give you a signal in advance. It becomes easier to predict epidemics in order to deploy the coping mechanism in time. In times when the spread of the COVID-19 virus spreads over us, it is important to take our safety and precautions into account.

5. Drugs Delivery and Development

It will be much easier to develop and deliver drugs with smarter systems that help achieve the results you need. With the help of multiple supercomputers and the ability to adapt quickly to changes, AI systems can help achieve great results.

Regardless of whether you want to make a copy of DNA to test for possible vulnerabilities or to speed up the drug development process, these systems are just as effective. With all the data available, it becomes easier to perform each operation as planned and achieve excellent results.

For example, Insilico Medicine was able to identify possible molecules that could change the effects of the virus on our body and deliver excellent results. This helps to speed up the development of the possible remedy and shows how an average person can take a little longer than usual.

The possible cures for this dangerous virus have yet to be found. Data science technologies, particularly AI, can create a better world, where every process is monitored and supported by intelligent systems to achieve results over time.

Read More: Fighting Corona virus using AI, Data Science and Machine Learning

Summing up

Fighting this global crisis that we are facing has become crucial. While artificial intelligence feels obliged to provide support in all possible forms, the world is convinced of positive results.

By following the guidelines of doctors and the WHO (World Health Organization), we can also support this technology and its processes. The best way to cure and stop this disease is to follow the measures mentioned and live a healthy life.

Because scientists have always found a way to address these global threats, we need to understand and be patient. Data science technologies help us build a better world and define coping mechanisms to combat the global threat posed by the spread of COVID-19.

Conclusion

I hope you have understood how Ai technology is helping in fight corona virus outbreak.

NearLearn is the best institute in Bangalore that provides Online Artificial Intelligence Course. It provides online and classroom training as well. It provides other courses also like machine learning, data science, blockchain, full-stack development etc.

#online training #online course

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amit4002020 · 5 years ago

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What is the Average Python Developer Salary

In today's fast-paced world, Python offers better salary and growth opportunities than other programming languages. Given the various factors that affect it, here we have tried to explain the content of the Python developers in this blog.

Given the popularity of Python, it may be time to familiarize yourself with the average salary of Python developers based on their experience, location, skills, etc. This blog about "Python Developer Salary" will help you understand the main aspects to decipher on which salaries are based. You will also learn whether or not to learn this lively and dynamic language.

What Does Python Developer work?

Python developers are usually responsible for writing the server-side (web) application logic. This includes developing core components, linking applications to third-party web services, and helping front-end developers integrate their work with Python applications.

Although web development and data analysis are still the main applications of Python, language is a big step in the area of machine learning. This comes from several salary reports that show that a Python developer deserves a lot more in the field of data science.

Geographic Based Python developer Salary

Although the average base salary of a Python programmer is high, this is not the only reason for its popularity. There are also many other factors that contribute to its popularity.

Technology giants around the world love it. NASA, Amazon, Google, Facebook, YouTube, etc. are just a few of the big names in the technology world that use Python for several reasons and are still looking for Python engineers.

New York: The average Python developer salary in New York, US is $ 132,598 / year.

California: In California, Python developers have an average annual salary of $ 138,466.

San Francisco: Python developers in San Francisco have an average annual salary of $ 143,476.

Virginia: The average salary for a Python developer in Virginia is $ 108,649 per year.

Average salary of python developer in India is 489,514

Read More: Why corporate Training is Important for Business

Salary in Python Programming Compare to other Programming Language

According to Stack Overflow, Python was the hottest technology in 2018. According to the latest Stack Overflow annual report, Python 2019 ranks third in the list of most popular computer skills. At the same time, Python is one of the most popular more popular technical skills, demand exceeds its supply. Therefore, Python can open many doors for you.

Let us now consider the content of a Python developer compared to other languages.

In the US, Ruby on Rails developer salaries are $ 122,149 a year.

The average Java Developer salary is $ 103,460 per year.

Perl developers have an annual salary of $ 121,428.

C ++ developers have an average annual salary of $ 114,148.

The average annual salary for JavaScript developers is $ 113,730.

.NET developers have an average annual salary of $ 93,714.

PHP developers average an average of $ 83,925 a year.

Python developers have an average annual salary of $ 118,124.\

Source: indeed

Python developer salary Based on Experience

There are currently around 25 million software developers worldwide. According to SlashData, nearly 8.2 million developers use Python, and now that number has surpassed the Java developer population (7.6 million).

Here's a Python programmer's salary based on experience.

Entry-level salary for Python developers: The average starting salary for Python developers ranges from $ 59,888 a year for first-time software developers to $ 111,605 a year for full-stack developers.

Python Intermediate Programmer Salary: The average annual salary for Python Intermediate Level developers is $ 117,940.

Senior Python Developer Salaries: Average senior Python developer salaries range from $ 132,789 a year for full-stack Python developers to $ 145,923 a year for advanced software developers.

Why Should You Learn Python Programming?

After you fully understand a Python developer's salary, you should know what you can do with Python and how you can make a career there.

Here are some short overviews:

It is widely used by companies because it is powerful and simple

Its simplicity and clarity make it ideal for beginners

Due to high demand, it offers excellent career opportunities, particularly in the United States and India

It has a number of frameworks to make website development as easy as possible

There is a large community that continues to contribute to its development

It is considered the best for artificial intelligence (AI) and machine learning (ML).

Python has already replaced Java as the second preferred language for GitHub

With Raspberry Pi you can create our own Python craft!

Conclusion

Now you have understood that the average salary of python developer. If you are also planning to make career in python programming language then start and learn python programming now.

NearLearn is the best institute that provide best online python course in India. It provides other online courses also like artificial intelligence, machine learning, ReactJs, react native, blockchain, data science and full-stack development etc.

#online training #online course

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amit4002020 · 5 years ago

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What is the Block chain Developer Salary in India?

The block chain era is here and now. Companies from different industries are familiar with the concept of decentralized general ledger. There is a worldwide wave of block chain adoption by companies and businesses to solve basic business problems. In fact, the advent of block chain technology is happening so quickly that Gartner predicts that the commercial value of block chain technology will exceed $ 3 trillion by 2030.

According to a 2018 PwC survey of 600 executives from 15 different regions, almost 84% of respondents said they used block chain in one way or another. India is also catching up with the upward trend in block chain adoption. Given the growing interest of state and private companies in block chain, the labor market in this area is currently booming.

Demand of Block chain Developers in India

As an emerging technology that has only recently gained a foothold in the past few years, block chain talent is hard to find. Block chain is one of the fastest growing skills today. Jobs in this area are increasing by an astonishing 2000 to 6000% and salaries for block chain developers by 50 to 100%. Higher than traditional developer jobs. Although there are many vacancies in block chain, the talent pool in this area is limited. The demand for block chain technologies, especially block chain developers in India, is generated not only by the BFSI sector, but also by healthcare, education, supply chain management and IT cloud, stock trading, real estate and even government agencies.

The most sought-after block chain capabilities are hyper ledger, Solidity, Ripple and Ethereal. However, since this area is relatively new, companies are often content with professionals with specific skills. For example, block chain developers must have basic knowledge of mathematics and algorithms. You should be familiar with C, C ++, Java and Python as most block chain projects are written in these languages.

In addition, block chain developers need to know at least some of the tools required for block chain development, such as Geth, Remix, Mist, Solium, Parity, BaaS and Truffle, to name a few. They should also have experience working on open source projects. Usually, most companies hire block chain developers with at least a bachelor's degree in math or computer science.

Overall, a block chain developer must have solid technical training and always be curious to learn new technologies.

Read More: Why Corporate Training is important for Business

Average Salaries of Block chain Developer in India

Due to the lack of talent and skills in this area, employers are always willing to pay blockchain professionals a high salary if they are worth it. In fact, a blockchain technician's salary is much higher than that of an average IT professional. If you have the right blockchain skills, you can double or even triple a software developer's salary in a year.

As more and more Indian companies and organizations join the blockchain train, the average annual salary of a blockchain developer in India has a wide range. Typically, a blockchain developer's salary in India is somewhere between Rs. 5,00,000-30,00,000 LPA. As can be seen, the higher your experience and skills, the higher your annual remuneration. The salary package also depends on whether a candidate has advanced certifications or not (job level, mid level, senior level).

In addition, blockchain job wages are very dynamic. For example, if a professional has three years of blockchain experience, the annual remuneration can reach Rs 45,000,000 or more. This is more than twice as much as a professional with five years of work experience (but no experience in blockchain technology).

High-level salaries for technological positions (without blockchain expertise) ranged from 1.5 to 2.5 billion rupees in 2018. As the need for security in various sectors, particularly in the United States, has increased significantly BFSI sector, companies are ready to pay more than Rs. 4 crores for high-level security experts and blockchain technicians.

There is a significant gap in the demand and supply of blockchain professionals in India. Of the 2 million software developers in India, only 5,000 have blockchain knowledge. Public sector banks currently dominate the game and, with around 4,000 specialists in this area (2018), represent the highest demand for blockchain developers compared to 2,300 experts in 2017. This corresponds to an increase of 75% in demand from blockchain specialists. According to TeamLease research, there are around 2,000 blockchain experts in the NBFCs and 2,400 in public sector companies.

Conclusion

However, since blockchain skills are primarily developed and promoted internally by employers, we hope that there will be more talented blockchain professionals in the near future.

NearLearn provides the best Blockchain online Training in India. It provides both classroom and online training to students. It provides other courses also like machine learning, artificial intelligence, data science, Reactjs, React-native, full-stack development etc.

#blockchain online training in india #blockchain course in india

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amit4002020 · 5 years ago

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10 Jobs Artificial Intelligence will replace by 2030

With artificial intelligence technologies used worldwide, theorists have pointed out that this would also kill many of the work currently being done by humans.

The emerging technology will eventually replace most jobs that involve "repetitive" and "manual" tasks. In this article, we'll look at the other job profiles that also have the same dilemma.

Huard Smith, who heads Forrester Consulting's financial services practice, said recently that AI's impact on various professions will be profound over the next 11 years. He says that cabin and location related jobs are severely affected by the advancement of new technologies. A common example is the replacement of human workers with robots.

1. Bookkeeping clerk jobs

With the industrial revolution 4.0, it is very likely that this profession will be completely replaced by new technologies in the next 10 years. For example, Microsoft Office offers a double-entry accounting application called Easy Bookkeeping that allows small businesses to record daily business transactions in newspapers and view accounts in book form. A poll mentioned that there is a 82% chance of automation in the next two decades.

2. Location Based jobs

Many things have changed due to the development of artificial intelligence. In the current scenario, grocery stores are switching to intelligent ATMs for bill payments instead of cashiers. For example, in the Amazon Grocery Smart Store, Amazon Go, there are no payment counters, and customers don't have to wait to buy something.

3. Market Research Analyst jobs

Experts believe that the job of market research analysts and marketers to be replaced by automation and AI is 61%. In a report from AmazonAWS, some market researchers plan to adjust their role in AI, while 12% of market researchers plan to change their careers outside of market research to protect their jobs from AI.

4. Retail Workers

Popular retail giants like Walmart have developed new technologies to minimize the time retail employees spend on the most routine and repetitive tasks like cleaning floors or checking inventory on a shelf. In a press release, the company mentioned robots such as Auto-C, Auto-S, Fast Unloader, and Pick-Up Tower, which can be used to clean floors independently, scan shelves in real time, and sort inventory to introduce products. Shelves faster than ever and online orders. However, these companies have not yet fully reported the hardest hit jobs.

5. Development

Most organizations use artificial intelligence faster. Mass layoffs in IT have already started. Technology giants such as Cognizant, Infosys and Capgemini have announced layoffs at medium and high levels for several reasons. One of them is the emergence of technologies like AI and automation. This helps companies, among other things, to work cost-effectively.

Read More: What is Artificial Intelligence Course Fee in Bangalore

6. Telemarketing jobs

You are probably already getting robot calls for various products and services, and telemarketing career growth is expected to decrease 3% by 2024. This is mainly due to the success requirements: unlike other sales telemarketers, you don't need a high level of social or emotional intelligence to be successful. Think about it - are you likely buying from a telemarketer? Conversion rates for direct phone sales are generally below 10%, which makes this role a mature option for automation.

7. Advertising Salespeople

When advertising moves from print and television to the web and social media landscapes, people simply don't have to manage those sales for marketers who want to buy from them. More and more social media platforms are enabling people to buy storage space via free application program interfaces (APIs) and self-service ad markets to suppress the seller and enable users to make money. faster and easier - and this is reflected in a 3% drop in industry forecasts.

8. Compensation and benefits managers Work

This is surprising as employment growth should increase by 7% by 2024. However, this is not because the demand is immune to automation. As companies grow - especially in multinational markets - a human and paper system can create more obstacles, delays and costs. Automated performance systems can save time and effort to bring benefits to a large number of employees, and companies like Ultipro and Workday are already widespread.

9. Receptionist Jobs

Pam predicted it in The Office, but if you're not a fan, automated planning and phone systems can replace much of the role of traditional receptionists - especially in modern technology companies that don't do office-wide phone systems or multinational companies.

10. Computer support jobs

The domain is expected to grow by 12% by 2024. With so much Internet content with instructions, step-by-step instructions, and hacks, it's no surprise that companies are turning to bots and automation to answer future questions about employee and customer support.

Conclusion

So I have mentioned some jobs which can be affected by artificial intelligence in 2030. What do you think AI is increasing the jobs or jobs are affected by artificial intelligence? Give your suggestions in the below comment section.

NearLearn is the best online machine learning institute in India. It provides various courses like data science, artificial intelligence, block chain, full-stack development etc.

#online artificial intelligence training in india

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amit4002020 · 5 years ago

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5 Common Myths about Machine Learning

Machine learning has been the main media coverage lately, and several articles and emotional stories have been published every second. Machine learning is proving to be the most useful, and there is no doubt that we have begun to penetrate commercial work models to make many notable advances such as language translation, speech recognition, recommendation systems, etc. . Indeed, artificial intelligence and machine learning beat our experts on certain complex problems. Ultimately, this advance is, in one way or another, the main motivator for being excited and occupied by reading and researching machine learning.

As we study machine learning and its progress, we are often tempted to believe that there are countless ways to discover machine learning to solve all of our problems and apply it to any situation. But the sad truth is that any organization has not yet fully exploited the BC due to misunderstandings that have arisen around it and that resolve from the first step. Break through the prevailing myths and misunderstandings about machine learning to create more amazing things.

MYTH#1 MACHINE LEARNING WILL SOON PAVE THE WAY FOR SUPERHUMAN INTELLIGENCE

Well, from the daily headlines about advances in artificial intelligence, we often get the impression that computers will soon take control. Many popular AI films talk about how machines develop their ability to speak, see, and argue, and ultimately leave people in the dust. It is true that we have come a long way in digital advances, and the main reason for recent success is the rise of AI, machine learning, and deep learning, but we still have a long way to go. The machines are super-fast and can do tedious tasks at lightning speed, but they lack one of the most important things, common sense, and no one knows how to teach them.

MYTH#2 BOTH MACHINE LEARNING AND DATA MINING ARE SAME

Thousands of articles are published every day about the difference between data mining and machine learning, but it is often confused that it is the same. Data mining is similar to the work of a miner who wins and wins coal, but doesn't know how to make a beautiful diamond ring out of it. Data mining involves digging data to identify unknown properties or patterns. Machine learning is later used to use paving data with specific properties or models to feed machines and obtain useful information. Although data mining and machine learning operate on similar principles, there is a thin line between the two that illustrates the differences.

Read More:

7 best free online data science course in 2020

2. MUST-HAVE SKILLS

· Mathematics

It is also considered one of the elixirs of life in data science. This is very important in the field of data science because there are many concepts that help a data scientist use algorithms. In addition, concepts such as statistics and probability theory are essential for the implementation of algorithms. So make sure you put a lot of effort into improving your math skills.

· Programming Languages

There are many people who would suggest a variety of programming languages to learn if you are aiming for a career in data science. However, don't overwhelm yourself with all the hype discussions. In data science, Python and R are the two most important programming languages. Concentrate on these two languages at an early stage. If you later gain both trust and great trust, you can move on to the next one (Java could be one of them).

To learn how to program, you can take short courses or online courses at any time. Practice a lot too. The more you encode, the better you become an encoder.

Communication and Presentation Skills

Mastery of all technical aspects is one of them. However, to be a successful data scientist, you must have exceptional communication and presentation skills. You shouldn't just be a data scientist, but also a data storyteller. Why? Once you get the valuable information from the overcrowded data, your next task is to present it. If you don't have storytelling skills, how can others understand what the information is capable of and the value it would bring?

Real-Time Practical Knowledge

Learning and mastery skills are certainly mandatory, but to get the most out of your learning you need to practice - practice with real-time problems and add value to your data science learning. The more you solve these problems, the more experience, and self-confidence you gain and shorten the path to your dream job. There are many hackathons on the internet - you can pick one at any time, participate and see where you stand in this increasingly competitive area of data science.

· Advice from Leaders

It is always good practice to seek advice from someone who already knows the area. And you can make the most of platforms like LinkedIn to connect with some of the industry leaders.

Another great way to make contacts is to attend data science conferences, where you can not only attend lectures and masterclasses, but also meet many industry representatives to help you take the lead. On track when you start your journey into data science.

· Accept the Reality

It's no surprise that Data Science is currently one of the highest-paid and most reputable jobs in the industry. And no company would pay someone a respectable paycheck and give it a high-level title until it demonstrates that it is able to solve some of the complex business problems. To accept the fact that at the start of your career you may not even get the title of a data scientist (in some exceptional cases). However, if you are determined and learn more and more about the field, the chances are that you will reach a higher position with a considerably high paycheck.

Make sure you don't hesitate to ask another data specialist for help if you need it. Knowledge and skills are the keys to success.

These are some factors that you have to focus on if you want to make a career in data science. A one can also become the data scientist by implementing these factors with having no prior experience.

CONCLUSION

If you want to get a job in data science then you have to follow the above mentioned factors and you will definitely get a good job in data science with having no prior experience also. So start today and follow these steps and land your first job as a data scientist.

Near learn provides the best

online data science training in Bangalore

. It provides other courses as well as artificial intelligence,

machine learning

, Deep Learning, Blockchain, ReactJs,

React Native

, Golang, and full-stack development, etc.

#Data Science Training in Bangalore #Data Science Course In Bangalore

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amit4002020 · 5 years ago

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How is Data Science Changing the World?

In this article you will learn what role a data scientist plays. There is a mysterious veil in data science. While the buzzword of data science has been around for a while, very few people know the real purpose of being a data scientist.

So let's examine the goal of data science.

Data science goal

The main goal of data science is to find patterns in the data. It uses various statistical techniques to analyze and learn from the data. A data scientist must thoroughly examine data from data extraction, wrestling, and preprocessing. Then it is responsible for making predictions from the data. The goal of a data scientist is to draw conclusions from the data. Thanks to these conclusions, he can help companies make smarter business decisions. We'll divide this blog into several sections to better understand the role of a data scientist.

Why Data Matters in Data Science

Data is new stream. We live in the era of the fourth industrial revolution. It's the era of artificial intelligence and big data. There is a massive data explosion that has led to new technologies and smarter products. About 2.5 exabytes of data are created daily. Data requirements have increased significantly in the past ten years. Many companies have focused on data. New sectors has been created by data in IT industry. However,

1. Why do we need data?

2. Why do industries need data?

3. What makes data valuable?

The answer to these questions lies in the way companies have tried to transform their products.

Data science is a very new terminology. Before data science we had statisticians. These statisticians have experience in qualitative data analysis, and companies have used it to analyze their overall performance and sales. With the advent of an IT process, cloud storage and analysis tools, the IT area has merged with the statistics. This created data science.

Early analysis of data based on surveys and finding solutions to public problems. For example, interviewing a number of children in a district would lead to a decision to develop the school in that area. The decision-making process was simplified with the help of computers. As a result, computers can solve more complex analytical problems. As the data began to multiply, companies began to see their value. Its importance is reflected in the many products that are designed to improve the customer experience. Industry has been looking for experts who can harness the potential of data. Data helps them to take the right business decisions and maximize their profits. In addition, the company was able to examine and respond to customer behavior based on their buying habits. The data has helped companies expand their sales model and create a better product for their customers.

Data refer to products, electricity to household appliances. We need data to design the right products for users. This motivates the product and makes it usable. A data scientist is like a sculptor. He chisels out the data to create something meaningful. While this can be a tedious task, a data scientist must have the right expertise to deliver the results.

Why data science is important?

Data creates magic. Industries need data to make prudent decisions. Data Science converts raw data into meaningful information. Industries therefore need data science. A data scientist is an assistant who knows how to create magic with data. A competent data scientist knows how to extract meaningful information from the data he encounters. It helps the company in the right direction. Society needs solid data-driven decisions, of which he is an expert. The data scientist is an expert in various underlying areas of statistics and IT. He uses his analytical skills to solve business problems.

Data Scientist is good at solving problems and is responsible for finding patterns in the data. The aim is to recognize redundant samples and to learn from them. Data science requires a variety of tools to extract information from data. A data scientist is responsible for collecting, storing and managing the structured and unstructured form of data.

Although the role of data science focuses on data analysis and management, it depends on the area in which the company specializes. This assumes that the data scientist has knowledge of the field in this particular industry.

Target Data-Centric Industries

As mentioned above, companies need data. They need it for their data-driven decision models and to create better customer experiences. In this section, we will explore the specific areas that these companies focus on to make smart data-driven decisions.

I. Data Science Helps for Better Marketing

Companies take helps from data to analyze their marketing strategies and create better ads. Companies often spend astronomical sums to market their products. Sometimes this cannot lead to the expected results. By studying and analyzing customer feedback, companies can create better ads. To do this, companies carefully analyze the behavior of online customers. By tracking customer trends, the company can also get better market information. That's why companies need data scientists to help them make informed decisions about marketing campaigns and advertising.

ii. Data Science Helps in Customer Acquisition

Data scientists help the company attract customers by analyzing their needs. This allows companies to customize the products to best meet the needs of their potential customers. Data is the key for companies to understand their customers. The purpose of a data scientist is therefore to give companies the ability to recognize customers and help them meet their customers' needs.

iii. Data Science Helps in Innovation

Companies create better innovations with a wealth of data. Data scientists contribute to product innovation by analyzing and creating information in traditional designs. They analyze customer reviews and help companies create a product that fits perfectly with reviews and comments. With the help of customer feedback data, companies make decisions and take appropriate measures in the right direction.

iv. Data science Helps in Enrich Life

Customer data is important to improve their lives. The healthcare industry uses the data provided to support their customers in their daily lives. Data scientists in these industries want to analyze personal data and medical history and develop products that address customers' problems.

The data-driven company examples above show that each company uses data differently. The use of the data depends on the requirements of the company. Therefore, the goal of data scientists depends on the interests of the company.

Other skills-set for data scientist

In this blog about the purpose of data science, we will now see what other skills a data scientist needs. In this section, we will examine how data scientists go beyond analyzing and collecting information from data. One goal of data scientists is not only to use statistical techniques to draw conclusions, but also to share their results with the company. A data scientist not only needs to master the calculation of numbers, but also to be able to translate mathematical jargon to make the right business decisions.

Example: Imagine a data scientist who analyzes the company's monthly turnover. It uses various statistical tools to analyze the data and draw conclusions. In the end, he gets results that he has to share with the company. The data scientist needs to know how to communicate the results in a very precise and simple way. Sales managers may not understand technical results and processes. Therefore, a data scientist must be able to tell a story. Thanks to the data narration, he can easily transfer his knowledge to the management team. This therefore extends the goal of a data scientist.

Data Scientist's goal is not just limited to statistical data processing, but also to managing and communicating data to help companies make better decisions.

So it was all for the purpose of data science. I hope you enjoyed our article.

Conclusion

At the end of the article - the goal of data science - we conclude that data scientists are the backbone of data-intensive companies. The goal of data scientists is to extract, preprocess and analyze data. Thanks to this, companies can make better decisions. Different companies have their own requirements and use the data accordingly. Ultimately, the goal of a Data Scientist is to make companies grow better. With the decisions and information provided, companies can define appropriate strategies and adapt to improved customer experience. Want to learn these skills then go to Best online data science training in Bangalore and gain the right skills for your future. Other skills which is in demand are machine learning, artificial intelligence, react native, blockchain etc.

However, if you have any questions about the goal of data science, post them freely with comments. We will definitely come back to you.

#online data science training in bangalore #online data science course in bangalore #data science with python online training in bangalore

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amit4002020 · 5 years ago

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5 Reasons Why You Should Create React Native Apps in 2020

In the modern world, mobile apps have become mandatory for every business. But how to build apps this question is still remains. Few business entrepreneurs think that native apps should be created for outstanding performance while other entrepreneurs think that hybrid apps will be good for their business.

While both approaches have their own advantages and disadvantages - what suits your business should decide which route is best for you. Native apps are known to deliver incredible performance with integrated new technologies. With cross-platform apps, companies are exploding their earnings and spending less.

So is there any way you get the benefit of both native and cross-platform app development? Yes, it will only possible if you create React Native apps in 2020.

While there is lots of reason to create React Native apps for your business, we are going to list the top 5 reasons why you should create React Native apps in 2020. But before starting those who don’t know much about React Native, here is an introduction of React Native app.

React Native App: An Introduction

React Native is a mobile app development framework that is for both android and iOS platforms. React Native is a cross-platform development framework. Because of cross-platform, it has found huge popularity in recent times.

It is launched by Facebook in 2015; React Native is a widely-used open source programming platform that was never invented before. It enables developers to create high-performance applications for Android and iOS without sacrificing quality and robustness. With JavaScript as the primary programming language, developers can use React (a JavaScript library platform) to build the user interface while building a native React application.

Now you have understood that what is React Native app and now let’s go straight and talk about why we should create React Native apps in 2020.

Read More: Top 20 Reactjs Interview Question and Answer for Fresher in 2020

5 Reasons Why Should You Create React Native Apps in 2020

Time and money are important factors to consider when developing a business strategy. The same applies to the application development process. Native React apps have been a huge success in the past and will continue to do so in the years to come.

When we say that companies around the world will develop increasingly responsive native apps by 2020, there's no exaggeration. Rather, it becomes the standard. Here are 5 reasons why you should build React Native apps in 2020.

1. Lesser Code, Fast Development

With using React Native framework you can easily transfer your code from one platform to another platform. Suppose you want to create an app for both iOS and android then with minor changes in code you can easily build the app for both platforms and also we can minimize the development time because of lesser code.

2. Code Reusability

React Native uses the same code for both iOS and android with minor changes. With this, you have to write the same code for both platform and you can deploy your code on both platform and your code will work. So it will reduce your development time and code reusability will increase. There is no need for any programming languages like Java, C, and C++. Only JavaScript developers can work on creating native apps by using the native UI library.

Also, react the native language is supported by a huge community of developer so if some issue arises in the React Native then it can be fixed by the community of developers.

3. Consume Less Memory

Since React Native offers compatibility with third-party plug-ins, you don't have to rely on WebView to add features like Google Maps to your app. With React Native you can link the plug-in to a native module and use the functions of the device such as zoom, rotation, etc. All of this is possible with less memory and thus faster loading of the application.

4. Update Feature

Another additional benefit of React Native is the live updates. Using JavaScript, developers can send live updates directly to users' phones without going through the app store update process.

This feature allows developers to apply code changes in real-time and make corrections while the application is loading. This way, users can get updated versions of the app instantly. In addition, the process is very transparent and rationalized.

5. Stunning UI and UX

React native apps are designed to maximize the user experience. Respond Native apps load quickly and are easy to navigate.

Mobile applications developed with the React Native Framework work just like a native application. The React Native application user interface consists of native widgets that work seamlessly. With React Native, even the most complex applications work without a problem. Building React Native apps is, therefore, the best option for businesses to stand out from the market while spending less.

Conclusion

I hope you have understood the importance of React Native and why you should create the React Native apps in 2020. NearLearn is the best React Native institute in Bangalore. It provides various courses like Artificial Intelligence, Machine Learning, Data Science, Blockchain, and full-stack development as well.

#reactnative #reactnative institute in bangalore #reactnative training in bangalore #reactnative course in bnagalore

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amit4002020 · 5 years ago

Text

5 Reasons Why You Should Create React Native Apps in 2020

In the modern world, mobile apps have become mandatory for every business. But how to build apps this question is still remains. Few business entrepreneurs think that native apps should be created for outstanding performance while other entrepreneurs think that hybrid apps will be good for their business.

While both approaches have their own advantages and disadvantages - what suits your business should decide which route is best for you. Native apps are known to deliver incredible performance with integrated new technologies. With cross-platform apps, companies are exploding their earnings and spending less.

So is there any way you get the benefit of both native and cross-platform app development? Yes, it will only possible if you create React Native apps in 2020.

While there is lots of reason to create React Native apps for your business, we are going to list the top 5 reasons why you should create React Native apps in 2020. But before starting those who don’t know much about React Native, here is an introduction of React Native app.

React Native App: An Introduction

React Native is a mobile app development framework that is for both android and iOS platforms. React Native is cross-platform development framework. Because of cross-platform, it has found huge popularity in recent times.

It is launched by Facebook in 2015; React Native is a widely-used open source programming platform that was never invented before. It enables developers to create high-performance applications for Android and iOS without sacrificing quality and robustness. With JavaScript as the primary programming language, developers can use React (a JavaScript library platform) to build the user interface while building a native React application.

Now you have understood that what is React Native app and now let’s go straight and talk about why we should create React Native apps in 2020.

Read More: Top 20 Reactjs Interview Question and Answer for Fresher in 2020

5 Reasons Why Should You Create React Native Apps in 2020

Time and money are important factors to consider when developing a business strategy. The same applies to the application development process. Native React apps have been a huge success in the past and will continue to do so in the years to come.

When we say that companies around the world will develop increasingly responsive native apps by 2020, there's no exaggeration. Rather, it becomes the standard. Here are 5 reasons why you should build React Native apps in 2020.

1. Lesser Code, Fast Development

With using React Native framework you can easily transfer your code from one platform to another platform. Suppose you want to create an app for both iOS and android then with minor changes in code you can easily build the app for both platforms and also we can minimize the development time because of lesser code.

2. Code Reusability

React Native uses the same code for both iOS and android with minor changes. With this, you have to write the same code for both platform and you can deploy your code on both platform and your code will work. So it will reduce your development time and code reusability will increase. There is no need for any programming languages like Java, C, and C++. Only JavaScript developers can work on creating native apps by using the native UI library.

Also, react the native language is supported by a huge community of developer so if some issue arises in the React Native then it can be fixed by the community of developers.

3. Consume Less Memory

Since React Native offers compatibility with third-party plug-ins, you don't have to rely on WebView to add features like Google Maps to your app. With React Native you can link the plug-in to a native module and use the functions of the device such as zoom, rotation, etc. All of this is possible with less memory and thus faster loading of the application.

4. Update Feature

Another additional benefit of React Native is the live updates. Using JavaScript, developers can send live updates directly to users' phones without going through the app store update process.

This feature allows developers to apply code changes in real-time and make corrections while the application is loading. This way, users can get updated versions of the app instantly. In addition, the process is very transparent and rationalized.

5. Stunning UI and UX

React native apps are designed to maximize the user experience. Respond to Native apps load quickly and are easy to navigate.

Mobile applications developed with the React Native Framework work just like a native application. The React Native application user interface consists of native widgets that work seamlessly. With React Native, even the most complex applications work without a problem. Building React Native apps is, therefore, the best option for businesses to stand out from the market while spending less.

Conclusion

I hope you have understood the importance of React Native and why you should create the React Native apps in 2020. NearLearn is the best React Native institute in Bangalore. It provides various courses like Artificial Intelligence, Machine Learning, Data Science, Blockchain, and full-stack development as well.

#reactnative #reactnative institute in bangalore #react native course in bangalore #react native training in bangalore

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amit4002020 · 5 years ago

Text

PROS & CONS OF CHOOSING A CAREER IN DATA SCIENCE

In today's world, the internet is saturated by the article of why data science is the sexiest job of the 21st century. But very few have spoken about the data science cons. undoubtedly, data science has rapid growth and this skill is in high demand and it also pays well. This technology is a good combination of programming, statistics and business analysis.

Here I will provide you the important insights of the data science field that will help you to choose the right course for you.

Pros of being a data scientist

Data Science in Demand

With year-over-year growth in this field, a data the scientist has taken up the top position in LinkedIn analysis for most promising job of the 21st century. A study we conducted estimated that even in a larger analytical ecosystem, 70% of vacancies are for data scientists with less than five years of work experience. In addition, potential job seekers with very few people who have the skills to succeed in this area have many options.

High Paying Job

According to Glassdoor, data scientists can earn an average salary of $113,309 per year. Data science is one of the top lucrative career options for the student. There may be a one reason for being a high paying job that data science makes companies smarter. The company takes a smart decision and can make an important place in the top companies.

Diversify

Data science is industry-independent and has many applications in a variety of industries, including healthcare, banking, e-commerce, and marketing. Therefore, you are not tied to a specific company or role and can work in any area where data is used for decision making. For example, the advent of machine learning (ML) marked significant improvements in the healthcare sector. One of the most important applications was the early detection of tumors.

Challenging Work

Data science has multiple disciplines including mathematics, statistics, analysis, and programming, etc. since it is a growing skill day by day it demands new skills to learn. So it can be a challenging work for a data scientist. There is no single template by which you can use that template for multiple projects. For each project, you have to learn new skills.

Read More: How to Use Artificial Intelligence To Growth Hack Social Media Engagement?

Cons of a data scientist

The ambiguous job role of a data scientist

Although it has become a buzzword over time, data science has no clear definition. This is essentially the study of data, and this can include extraction, analysis, visualization, etc. Create information to make business decisions. It would also depend on the area in which the company specializes. However, it is certain that all data scientists have to deal with a lot of raw data, which can take a long time. In addition, companies often provide arbitrary data that may not deliver the expected results.

Difficult To Master in Data Science

As mentioned above, data scientists need to work on large amounts of data to solve business problems. This includes expertise in a long list of skills, including computer programming and software applications, statistics, data analysis, and data visualization - and these are just technical skills. It is therefore far from possible to master every area and to be equally competent in each of them. Although many online courses have attempted to fill this skills gap, it remains difficult given the breadth of the subject. That brings us to the next point.

Simplifying Technical Concept

With all the skills you have acquired for your work, it is useless if you cannot pass your results on to stakeholders in a way that you understand. Explaining technical concepts to a non-technical audience is a major challenge for most data scientists who find it difficult to step back from something they have been in for a long time. This means that in addition to a long list of technical skills, you also need to acquire communication skills. And that's not all.

The technical concept should be acquired for your work, most data scientist who find it difficult to step back from something they have been in for a long time.

Multiple department Expertise

Data science must have multiple department expertise because, without industry knowledge, data scientists cannot make the right decision in order to assist the company. So he or they should be experts in their industry where they work. So this can be a challenging task for them. It arises difficulties for data scientists when they migrate from one industry to another industry.

Problem with data privacy

Data is fuel for many industries. Data scientists help industries to make data-driven decisions. However, the data used can violate customers' privacy. The customer's personal data is visible to the parent company and can sometimes lead to data leaks due to a security error. Ethical issues related to maintaining the confidentiality of data and how it is used have been a problem for many industries.

Conclusion

After weighing the pros and cons of data science, we can imagine the full picture of this area. Although data science is an area with many lucrative advantages, it also suffers from its disadvantages. As a less saturated and well-paid field that has revolutionized multiple horizons, it also has its own background when one looks at the breadth of the field and its interdisciplinary nature. Data science is a constantly evolving field that will take years to acquire. Ultimately, it is up to you to decide whether the benefits of data science will motivate you for your future career or the disadvantages that will help you make a prudent decision!

Near Learn is the Best Data Science with python classroom Training in Bangalore and provides training on Artificial Intelligence, Machine Learning, Deep Learning, Full-Stack Development, Mean-Stack development, Golang, React Native and other technologies as well.

#Data Science Training in Bangalore #Data Science Course In Bangalore #Data science with python training in bangalore #data science Python Classroom Training Bangalore

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amit4002020 · 5 years ago

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How to Prepare for Data Science Interview?

Appearing in data science interviews but struggling to crack the interview. Are you scaring to get into a data science interview? Or you don’t know what to expect in data science interview then don’t worry I have come up with the 6 steps that will definitely help you to crack data science interviews.

Cracking data science interviews need a massive amount of knowledge and research. So practicing only will help you to crack the interview on that big day.

Read on to understand a quick, step-by-step approach to specific areas of skills, technical know-how, and skills that are required not only to end the interview but also to excel in big data and machine learning provide.

The thing about data science is that its application, and therefore expectations vary widely across industries. The role is interpreted differently depending on the company, some could call a doctorate. Statistician as a data scientist, for others it means an excellent skill, while for some it can be a generalist for artificial intelligence and machine learning.

6 steps for Preparing a Data Science Interview

Here I am going to mention 6 steps that will help you to prepare and crack you data science interview. So brush up your skills and follow these steps.

Step 1:

Before appearing in data science interview first read the job roles or job profile especially for Skills, Techniques, and Tools. If the job description has not enough detail mentioned the research on the company website and check what type of data science position is available there and what kind of knowledge they are expecting from the candidate.

Mostly data science interview is a combination of the Aptitude, Technical Knowledge and Analytical Reasoning.

Step 2:

Don’t forget to brush up your knowledge of relevant skills before the interview. To test your technical skills, the interviewer will generally ask you about statistics, machine learning, and programming, etc. Ensure to brush up on languages like Python, R, and Tableau. The interviewer generally asks the programming question from these languages and will check your knowledge on these languages.

Step3:

Brush up your skills on some primary important topics like:

1. Probability

2. Statistical Models.

3. Machine Learning and Neural Networks etc.

So here, you will essentially have your exam through a case study or a discussion of your problem-solving skills. If you are able to define the problem for them on the scenario presented and will help add the suggested solution and its impact on the business. In doing so, cite examples of case studies or research papers to support the suggested solution.

Step4:

Although you can develop the necessary skills and qualities, make sure throughout the interview that you are willing to learn and that you can adapt flexibly to the current organization such as data science and its applications is unique.

Step5:

Having a tight resume and predicting how you will relate your experience to the position given during the interview.

Step6:

If you are doing data science projects specifically, when you are fresher, there are many public areas available. In addition, it is advisable to attend MOOC - Massive Open Online courses to be exposed to various and targeted applications.

Keep in mind that lately the role of a data scientist is seen as someone who can bridge the gap between the different functions of a company. It is not intended or required that you are a specialist in all aspects, but you should be able to link functions, ideas, and solutions across domains. In order to stand out in an interview, you not only need to demonstrate your individual strength and expertise in this area, but also act as a person with sufficient management skills and good communication and technical skills who can fit in and participate in the heart of a problem.