How can You become a data scientist? Cost, salary, careers etc.

Your big, horn-rimmed glasses and nerdy looks can lead you to a career of immense importance – Data Science. As data changes the world, you should start thinking about becoming a data scientist. But first, you need to learn how to get the right degree in data science and programs that will prepare you for rewarding IT jobs.

How can I become a data scientist? Cost, salary, careers

Your big, horn-rimmed glasses and nerdy looks can lead you to a career of immense importance – Data Science. As data changes the world, you should start thinking about becoming a data scientist. But first, you need to learn how to get the right degree in data science and programs that will prepare you for rewarding IT jobs.

From the beginning of the 21st century to the present, Data Science is comfortably located as one of the strongest careers in the world. Data scientists receive a water salary, and job growth is good. The world generates more data every day that needs to be collected and analyzed by scientists for more productive uses.

Therefore, in this post, we will show you how to become a data scientist. You will see the types of academic programs and diplomas you need and how much it will cost you to become a data researcher. We will also show you the types of jobs you can get when you graduate and answer any other questions you may have on this topic.

What is data science?

Data science is the disciplinary field that gives birth to data scientists. It is a multi-disciplinary field that trains professionals to be able to extract ideas and knowledge from structured and unstructured data.

So, being a multidisciplinary field, data science extracts knowledge of mathematics , statistics, computer science , and information science . Specifically, it combines statistics, data analysis and machine learning, along with related methods, for the sake of understanding and analyzing data.

Due to the close relationship with these other disciplines, it has become difficult these days to distinguish the science of business analysis data, business information, predictive models and statistics. People (professionals and beginners) now use any of them interchangeably.

Also, the concept of data extraction and big data are the same as those of data extraction, which is why some universities will offer their data science programs under the name Big Data.

So, you see, data science is concerned with analyzing big data using computer programming methods and virtual mining.

Who is a data scientist?

A data scientist is simply the tool of data science. What does that sound like? Not pretty enough. So let’s break it down into human terms.

A data scientist is someone (the professional) who collects and analyzes data, with their multidisciplinary knowledge, to provide a solution. Therefore, he or she must have the statistical knowledge and computer skills needed to solve complex problems.

Moreover, data scientists will use techniques in mathematics and algorithm to solve some of the most complex business problems from an analytical point of view. For this reason, the data researcher is a treasure for large companies and companies that want to expand their operations.

What skills should a data scientist possess?

It is not enough to have gained the right education for data scientists or to understand what they do every day. Without a certain set of special skills, you may not be able to succeed in this profession.

So what are these skill sets that you need to have?

Here they are:

  • Expertise and fluency in computer programs and languages. You need to be very conversant with SAS, SPSS, MATLAB R, Python, Java, C / C ++, Hadoop Platform, SQL / NoSQL databases.
  • Business Acumen or Savviness. Because you will find yourself working in various business settings, you need to gain more than basic knowledge about the business industry. This will help you solve complex problems and create solutions that align with your company’s goals.
  • Communication skills. You certainly know how to communicate with data, but people will depend on you to communicate your conclusions and solutions in a language they would understand. You need to be able to clearly translate your technical findings and analyzes to the non-technical departments of the organization in which you work.
  • Advanced technical skills. Your skills in math, statistics, machine learning, data mining, data cleaning, data visualization, and unstructured data techniques should be out of this world.

What is the difference between a Data Scientist and a Data Analyst?

A word constantly appears when we describe what scientists do. This word is “analyze.” Scientists collect and analyze data, it’s true. But data analysts also collect and analyze data. So, does that make the data analyst a data scientist?

This is the basic question behind the Data Science vs Data Analytics argument. Where do data scientists differ from the data analyst?

Here is:

Data analysts examine large data sets to pick trends, develop charts, and create visual presentations that help companies make better strategic decisions. Data scientists, on the other hand, design and build new processes for modeling and producing data using prototypes, algorithms, predictive models, and custom analysis.

So, you see, while the analyst is using existing techniques to accomplish his tasks, the scientist is developing new processes and techniques to make data analysis easier.

In addition, data analysts in their routine work are masters of SQL. They can tell a story from data with a certain level of scientific curiosity. On the other hand, the data scientist, in addition to having all the skills of analysts, has a solid foundation in modeling, analysis, mathematics, statistics and computer science. He uses them to better communicate his findings to relevant business stakeholders to influence how he approaches a business challenge.

n addition, while the data analyst will solve the questions given to him by the enterprise, the data scientist will formulate questions whose solutions are likely to benefit the business.

Where do data scientists work?

Data scientists find jobs in any workspace that uses data to function – which is about every organization that aspires to grow. However, they are more focused on business with marketing departments, as well as in sectors of the economy, such as the auto industry or insurance.

Another work environment in which you will find data scientists is in the Department of Defense. Here, they help analyze the levels of threat that come to a nation.

What other job roles can a data scientist take?

Due to the interdependent nature of data science, scientists can find jobs in other related fields. This is especially true if you are advancing in your career by earning a Master’s degree in data science or other related fields.

Here are some of the roles that data scientists can play:

Data engineer

Data engineers use large amounts of data to develop software. Most of the time, they establish the architecture that allows data scientists to perform their tasks efficiently. It also manages database systems, scales data architecture across multiple servers, and writes complex queries to navigate through data. Therefore, they are fully familiar with programming languages ​​such as Python, Hadoop-based technologies such as MapReduce, and database technologies such as MySQL.

Data analyst

Data analysts are professionals who use data to provide reports and views that better explain the information contained in the data. Specifically, what data analysts do is help the company they work with understand specific chart questions. The best way to look at analysts is the little brother of data scientists. Their expertise is the foundation of the scientist.

Mechanical learning engineer

Machine Learning engineers build, implement and manage machine learning projects using programming languages ​​such as Python or C / C ++. For these professionals to start their careers, they may need to start with training in software engineering and then continue to gain knowledge in the field of statistics and machine learning.

Scientific researcher in the field of informatics and information

Computer scientists design computing technologies, as well as create new and better uses for existing technologies. You will find them working for the federal government of nations and in computer systems design companies.

What are the salary and job prospects of data scientists?

You just have to ask an honest data scientist to be happy with their payment. But since you may not have it, we’ll tell you what you gain.

According to the Bureau of Labor Statistics (BLS) , scientists in the field of computer and information research earn an average of $ 118,370 per year. We know that this role of work is different from that of Data Scientist. However, both are in the same field and are very closely related and are the ones that BLS recognizes.

Meanwhile, for scientists in the field of computer research and information, there were 31,700 positions available in 2018. The job also has an estimated growth rate of 16% from 2018 to 2028, which is much faster than the average growth of jobs.

BLS also provides job pay and job growth for another associated role – Mathematicians and statisticians. According to BLS, the Mathematician and Statistician earned an average of $ 88,190 per year and the job has an estimated 30% increase

Fortunately, we have a more specific salary for data scientists through Glassdoor . Glassdoor puts the average earnings of data scientists at $ 120,495 per year. This projection is even higher than the BLS estimate for computer science scientists.

Glassdoor continues to reduce this salary for Data Scientists by companies, as follows:

  • Facebook – $ 145,365
  • IBM – $ 114,635
  • Microsoft – $ 129,917
  • Uber – $ 125,672
  • apple – $ 137,560
  • Airbnb – $ 140,312
  • Google – $ 140,212
  • Amazon – $ 120,407
  • Twitter – $ 142,665
  • LinkedIn – $ 128,957

What are the education requirements for data scientists?

To start your career as a data scientist, you must have at least a four-year bachelor’s degree from an accredited college. The most preferred bachelor’s degree is B.Tech in data science. Fortunately, some schools in the United States offer a degree in data science.

However, if you can’t get a bachelor’s degree in data science. A diploma in a related discipline will be useful. One such discipline that will qualify you for a career in data science includes computer science, statistics, physics, social sciences , mathematics, applied mathematics, and economics.

You need to continue to get a master’s degree in data science after a B.Tech degree. The master’s degree is relevant because most employers prefer those with a master’s degree to those with a bachelor’s degree. Also, the proportion of professionals in the field with a master’s degree is higher than those who have a license.

You can continue to hold a PhD in data science after your master’s program. However, this is ideal if you want a leadership position in the field or to teach it in the higher institution.

So, to summarize, the minimum educational requirement to become a data researcher is a four-year baccalaureate degree in data science or related technical discipline. In the meantime, to improve your chances of getting a job as a data researcher, you’ll need a master’s degree in data science. Then you should get a Ph.D. degree if you aspire to a leading position in the field.

Where can I get the best science education in data?

Although a bachelor’s degree is the minimum requirement to obtain a job in the field of data science, we will discuss the master’s program here.

The reason for this is clear. The field of data science has more openings for master’s degree graduates than undergraduate ones.

So when thinking about getting a master’s degree in data science, you should consider accreditation, program structure, cost, financial aid, and concentration options.

If you want to learn data science in Mumbai, you can learn from MCTA which provides the best data science courses in Mumbai.

You can find more information on the website.

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