Which Career is More Promising Data Scientist or Software developer?

To know about which career is a more promising data scientist or software developer for that first let’s try to understand the difference between a data scientist and software developer.

Software Developer:

A person who writes the lines of code is usually known as the hardcore computer programmer or software developer. The design and develop complete software architectures for very complex systems. A typical career path leads them to system technology and product management

Data Scientist:

Data scientists are the ones who solve complex data problems with their solid expertise in certain scientific disciplines. They work with various elements related to mathematics, statistics, computer science, etc.

Basically, they do everything you can imagine in the world of analytics, and much more. They usually also have a doctorate.

To Answer the Question:

You will love it when you have both. A data scientist certainly knows what his backend data architecture should look like. A developer knows how to combine everything with his coding skills.

A data scientist is someone who puts things together so that the product has the greatest benefit for the company. A developer may not have such an experience; he focuses on creating things, not on his analysis.

In the end, it depends on your individual decision and your interest. If you want to design things and create algorithms that have a defined result and you know what to expect, software development is for you. However, if you like the unpredictable, in love with statistics and trends, and have an intrinsic economic intelligence, you're the data scientist the future is looking for.

Although the field of data science is evolving day by day, its importance will never dominate that of software developers, as we will constantly ask them to develop the software that data scientists will work with. And if we add more data, in the end, we will continue to need data scientists to interpret the data and drive business progress.

·         Data scientists write code as a medium to the end, while software developers write code to develop things.

·         Data Science is constitutionally different from software development in that data science is an analytical activity, while software development is significantly higher than traditional engineering as a standard.

·         Data scientists deal with topics such as detecting fraudulent transactions or predicting employees who are destined to leave a company. Software developers can select data scientist models and convert them into fully functional arrangements based on production quality principles. Software developers deal with problems such as creating an algorithm for more efficient operation or creating user interfaces.

Read More:   Top 5 Data Science Trends in 2020

Life of a Data Scientist

Data scientist loves big data. They appreciate a large number of encrypted data points (unstructured and structured) and use their overwhelming skills in math, statistics, and programming to clean them up and organize them. Then they use all of their analytical skills - industry knowledge, contextual knowledge, sarcasm of real hypotheses - to uncover hidden solutions for commercial provocation.

Life of a Software Developer:

The role of a software developer is to identify, design, install and test a software system that he has developed for a company from scratch. This can range from the creation of internal programs that allow companies to work more efficiently to the production of systems that can be sold on the open market.

Can the software developer become a data scientist?

Yes, it is possible. It can be easier for some people than for others. The ease with which you switch from a role as a data scientist to a role as a software developer depends on the type of software you are developing. Most likely, this software developer would require a part-time or full-time education in data science. The fact is that although data science is relatively new, it has been around for a long time. We have used data science since computers were used to predict the weather, the consequences of medical therapies, and the capital and product markets. Therefore, the maximum of these software developers who have developed predictive algorithms using statistical models would be much more suitable for a role as a data scientist than someone who has the only experience in software development.

Becoming a data scientist is a journey. If you are familiar with data analysis tools and languages ​​like SQL, R, Python, SPSS, and SAS, the journey will be noticeably easier. If you have knowledge or expertise in statistics or use statistical models to improve algorithms based on your education or work, it would even be satisfactory. The goal is to summarize your idea in the role of software development that does not resemble the role of a data scientist but obliges you to use statistical models.

If we see all overall in the long run then both fields have their great value in their field.

Conclusion:

I hope you have understood that which career will be better for you that is data science or software development.

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Why Python is good for Data Science?

The numbers don't lie. According to recent studies, Python is the most loving programming language for data scientists. You need a user-friendly language that provides adequate library availability and excellent community participation. Projects with inactive communities are generally less likely to have their platforms serviced or updated, which is not the case with Python.

What makes Python great for data science? We explored why Python is so common in the booming data science industry - and how you can use it in your big data and machine learning projects.

Why Python is best for Data Science?

Most of the programmers used python for the data science because python is easy to use and its syntax is very easy to understand.

Python has long been known as a programming language that is syntactically easy to understand anyway. Python also has an active community with a huge selection of libraries and resources. The result? They have a programming platform that makes sense for new technologies such as machine learning and data science.

Professionals who work with data science applications do not want to get stuck in complex programming requirements. You want to use programming languages ​​like Python and Ruby to perform easy tasks.

Ruby is great for tasks like cleaning and merging data, as well as other data preprocessing tasks. However, there aren't as many machine learning libraries as Python. This gives Python the edge in data science and machine learning

With Python, developers can also deploy programs and run prototypes, which speeds up the development process. Once a project becomes an analysis tool or application, it can be ported to more complex languages ​​such as Java or C if necessary.

New data scientists are attracted to Python because of its ease of use, which makes it accessible. So popular, in fact, that 48% of data scientists with five years or less experience-rated Python as their preferred programming language.

This number gradually decreases with increasing level of experience and the analyses become more intensive. Python has proven to be a great place to start for data scientists

Why Data Science and Python Good to use Together

In data science, useful information is extrapolated from huge pools of statistics, registers, and data. These data are generally unsorted and difficult to correlate with significant accuracy. Machine learning can link different data sets but requires serious sophistication and computing power.

Python fulfills this need by being a universal programming language. You can use it to create a CSV output for easy reading of data in a table. Alternatively, more complicated file output that machine learning clusters can include for computation.

Consider the following example:

The weather forecast builds on previous records from a century of weather data. Machine learning can create more accurate forecast models based on past weather events. Python can do this because it is easy and efficient for code execution, but also multifunctional. In addition, Python can support object-oriented, structured and functional programming styles so that it can be used anywhere.

The Python package index now contains over 70,000 libraries and that number continue to grow. As already mentioned, Python offers many libraries that are geared toward data science. A simple Google search shows many Python top 10 libraries for data science lists. We could say that the most popular data analysis library is an open-source library called Pandas. It is a collection of high-performance applications that make analyzing data in Python a much easier task.

Regardless of what scientists want to do with Python, be it predictive causal analysis or prescriptive analysis, Python has the toolbox to perform a variety of powerful functions. No wonder data scientists have adopted Python.

Conclusion:

I hope now you have understood why most data scientists are using python. NearLearn provides the best data science with python training in Bangalore. it also provides Artificial Intelligence, Machine

Near Learn provides  Best Data Science with python 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.

Read More: Top 5 Data science Trends in 2020

Why Data Science is a Most Exciting Career?

In today's world, wherever job you are doing and whatever job it is data science skills play an important role in your career development. Here I will discuss How to Shape Your Career with Data Science Course in Bangalore? And why data science is a most exciting career for the future.

If you go to any industry there is a need for data scientists. For example in digital marketing, data analytics and big data allow the marketer to build more effective campaigns.

The retail sector was turned upside down by Amazon thanks to its data-driven logistics functions. Amazon uses the data to keep track of its huge inventory, but it always ensures that there are enough inventories in each of its warehouses to ensure that consumers receive their products quickly after ordering online.

Healthcare industry is also revolutionized by data. They also need a number of data scientists. With the increasing digitization of patient records, healthcare professionals are finding new ways to make more accurate and time-sensitive diagnoses, as well as under extreme regulatory pressures, which means data can be incredibly sensitive Need to control with. Health organizations are looking for data scientists to build foundations and structures around the processing of this data.

Much more industry is there who are looking for enthusiastic data scientists for their projects. As data scientists are trained on multiple skills like mathematics, computing, data analytics and much more. So they will be always in huge demand. So we can say that data science is the most exciting career in the future.

What should I know to be a data scientist?

People with the background of computer science engineering, IT, or engineering generally find a master in data science because of their existing skillset. But as discussed before, marketers to management level people get benefit by having skills in data science.  So other background people also can do the data science and these skills will help them in their career development.

Now the question is, with the basic degree or relevant degree in data science what other skills you should have to be a professional data scientist?

Here I am going to explain some skills set which you must have to be a professional data scientist.

Programming: Data Science involves a lot of coding in languages ​​like MATLAB, Python, Hadoop, and SQL. It is important to understand these languages ​​in order to get the most out of every data science initiative you undertake in your professional career.

Quantitative analysis: Learn more about data visualization, data mining, statistical methods and database systems.

Product intuition: You will also learn how data can be used to inform about product development and iteration decisions to better meet consumer expectations.

Communication and storytelling: The ability to present data to key stakeholders in such a way that it corresponds to the information in the data core is an essential skill for a data scientist

Teamwork: Data science initiatives generally relate to all matters. It is important that a data scientist works with stakeholders both inside and outside of their own department.

Master in data science will develop skills

As discussed those skills which you must have to become a professional data scientist. A master's in the data science program can develop those skills.

Near learn the master's program in data science will help you to develop your data science skills. Master in data science will help you to develop your logical and analytic thinking, complex solving problem, management and much more. After completing a master's in data science you will be able to solve complex problems and will be ready to work for industry, government and also for the social environment.

A huge benefit of this master program is that professional can broaden their skillset without disturbing their existing career development because the master program is available online as well as offline. As data science skills are in peak demand so you can learn and showcase your skills in the industry.

As data science skills are in huge demand so jobs in data science are increasing day by day. That’s why
Harvard Business Review called it as sexiest job of the 21^st century. Professionals who are really wanted to make career in data science field can choose a data science master program which will help them to get data science skillset. For more information about this course kindly go through: 

Best data science Training in Bangalore

Conclusion:

I hope you have understood why data science is a most exciting career in future. How you can get data science skills and get into the industries which are using data science.  Near Learn provides the best data science training course in Bangalore and also provides training on Artificial intelligence, machine learning, deep learning, React Native, full-stack development, Golang, and other technologies as well.

What is Scope of Data Science in India

Data science has a very good scope in India. A report says that the statistics even increased in 2020. They estimated the open positions in data science at 2.9 million. Demand is growing as always and in the near future, it is said that companies need more data scientists. The need will increase and can never decrease.

Did you know that Flipkart has 700 vacancies for data science and other technical areas? In addition, Amazon, Netflix, and many of these large companies have massive demand and job opportunities for data scientists.

Data science is seeing an increase in jobs worldwide. India is one of those countries where there is a data explosion. The scope of data science in India and the need for IT professionals to improve their knowledge of data science are increasing.

India is the canter of the software and information technology industry. There is a new era of data and it professional is upgrading their knowledge on data. We can say that the IT industry is going to change with data science.

Data Science Career of the Future

Data science has been declared as the sexiest job of the 21^st century. There is a massive data revolution that has transformed around the world. Now data has become the fuel of the IT industry. Earlier most of the companies would rely on the experience to make major decisions. But after coming data science it becomes easy for the industry to make data-driven decisions. Now the industry can able to take decisions by using data science.

The industry can easily analyze the market by using data science. They can take the decision and analyze the risk involved decision. Data science has brought rapid growth in the industry to minimize industry loss.

Due to this, the demand for data scientists has increased in the IT industry. Therefore, data science has become a career in the future.

Read More:  How to Shape Your Career with Data Science Course in Bangalore?

The career of Data Science in India

In India, most of the start-up companies have shifted their traditional software development work to data science. They know that data science can make their development work so easy. With this, they can easily analyze their data workflow and can manage their work according to that. In India, there is a huge scope of data science. People are moving to this technology and making their careers in data science.

A data scientist has a deep learning curve. This technology includes various disciplines like mathematics, statistics, and computer science. You should have analytical thinking to become a data scientist. A full-fledged data scientists are those who proficient in these fields.  Due to the very high learning curve there are very few data scientists available in India. The industry needs those candidates who are proficient in these skills and can fulfill their requirements.

India is the second country after the United States of America where there are many universities available that provide degree in data science. As a result, there are very few candidates who process this degree. So there is a lack of data science roles.

 As I already told that various start-ups have emerged with the data science technology. Due to lack of this role, these companies are finding difficulties to get the right candidate for their business. Data science jobs have increased to 45% compared to last year. This figure will give you an idea that how much data science role increased in India.

Salary Demands by Data Scientist

A data scientist gets a huge salary in India. As everyone knows that there is a huge gap occurs between the demand and supply for the data scientist candidates. So because of the lack of data science candidates in India so there are more chances to get a high salary for these roles.

According to earnings data scientists in India are earning more salary rather than other IT positions. The average salary of a data scientist in India is 6,50,000 while other It roles average salary is 4,50,000 which is very high than other positions. That is why data science has a very good scope in respect of salary and added privileges. If you want to check the proper salary of a data scientist then you can go through what is the range of data scientist salaries in India.

 

Redefining Traditional Roles

With the advancement of automation and artificial intelligence, the roles are reducing. The roles like IT administrator, testing, database managers have found several years but due to artificial intelligence, these roles are decreasing day by day. Moreover, Indians IT workers are getting affected due to their skills gaps stated by the IBM chief Ginni Rometty.

These job losses in the computer industry are known as "digital bloodbath" and have hit the Indian IT workforce immensely. This is a challenging issue that requires special attention from employees to improve their skills and keep pace with the needs and requirements of the IT industry.

To do this, employees need to be familiar with the technology of the future - data. There is a huge field of activity for data in India - not only for data science but also for data analysis, big data engineers, big data managers, and data architects. If only the demand for data scientists is very great, imagine the demand for all of these roles together! The need for data scientists and other data-related professions is greater than ever. Therefore, data science in India can be seen as a new direction for people to build their careers for long-term benefits. It is clear that data science is very extensive in India.

Conclusion

In this article, we have described how India is seeing a massive increase in its data science jobs. We also understood various factors that are driving the need for data scientists. We also discussed why the position of data science brings high salary bonuses. We also discussed the "digital bloodbath" that has changed our IT industry and how a career in data science can promise a stable future.

 Near Learn is one of the best data science training institutes in Bangalore that provides the best education in Bangalore.

What Gives More Benefit to Get Data Science Job – Certification or Project?

 

Data science jobs are increasing day by day and the job seeker are doing certification and projects to get a good data science job. There are number of institute are available that provide data science Classroom Training in Bangalore. But what gives more benefit to get data science job. Would it be certification or project?

There is no question that it is imperative to use all platforms and tools to stand out from others. A prospective data seeker should also set priorities and choose the best approach. Nowadays, however, aspirants use many strategies at random, without having a clear vision of the desired goal, which only leads to burnout.

Main problem is that many aspirants cannot resolve is having more certifications or working on multiple projects. For this reason, most of them take part in certifications and rely on the projects that include data science programs in their courses. This allowed applicants to apply for a job, but recruiters cannot find promising data scientists who can able to solve business challenges.

Enhancement in certification

As we can see that there is huge rise in enrollment in certifications. Mostly students are seeking for the data science certification and want to get a good job in this field.

According to a Coursera report, the e-learning platform has seen a huge increase in enrollments in AI and data science courses. Recruiters find it difficult to hire qualified data scientists, which can be seen from the fact that 85% of AI projects fail due to a lack of good candidates. Certifications make the job interview easier, but do not necessarily guarantee jobs.

Parul pandey said in one report that data scientist aspirants post their data science certification in LinkedIn but they don’t post any projects of data science. Aspirants are lacking to get good job in data science because they don’t communicate about their project which can help more them to get a good job. They only focus on certifications. Certification only helps in getting interviews but do not necessarily to get assured job. Job will be assured only after working in projects which will help you to get more technical knowledge.

Data Science Project Importance

Data science projects are the most important thing for the data science aspirants because it helps to increase their technical knowledge.

But if I talk about the different organizations they all not deal with the same difficulties. Different organizations having different projects and different projects have their own limitations. So aspirants have to face difficulties during their project execution. To tackle these problems aspirants must participate in hackathon challenges where they can show their expertise and enhance their technical skills. Still, aspirants can able to diversify their portfolio by working on a wide range of projects.

With this aspirants should also have the ability to convert the business challenges into the data science problems. As I told you already that certificates help only to get an interview but the projects demonstrate to you how proficient you are to tackle real-world problems which certificate can never do.

Viewpoint

However, as per Sharath Kumar, a data scientist at IBM, with technical skills communication skills also matters. Without communication skills, it is hard to explain the projects to the teammate which will degrade you in that environment. So the communication skills play the vital role for the aspirants in the organization as a data scientist. So they must work on their communication skills as well.

However, certification has their own advantage because by looking that certification only recruiters call you for the interviews but the good number of projects with decent communication skill will help you to crack that interview. So the consequently, aspirant put their efforts on the projects and communicate their projects on the LinkedIn, medium and more which is more beneficial to them rather than only focusing on certifications.

There are number of data science companies are available which will provide you the best data science training in Bangalore with live projects. You can do the data science certification with live training from there and ready to give interviews in different organizations. 

Conclusion

I hope now you have understood that what will help you more to get a good data science job. Near Learn is the top data science training institute in Bangalore that provides the best education in the Bangalore.

Read More: Is Data Scientist Certification Training In Data Science Really Worth It?