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Data Science and Data Scientist : Career Scope

Data Science and Data Scientist : Data is studied in detail in data science, which includes algorithms, machine learning principles and many other tools. It is used to record, collect and analyse data to obtain important and useful information. Data scientists extract and examine data from a wide range of sources such as log files, social media, sensors, customer transactions.

Software technology is growing very fast in the world, the major work of our life has become dependent on software technology, Due to the rapidly increasing software technology in every sector in the world, becoming a data scientist is a good option for students. Therefore, in this article complete information about data science will be given, how appropriate it would be to make a career in this field, all these will be told.

What is Data Science?

What is Data Science?

Data Science is made up of two words Data and Science. First of all let us try to know here what is data? Any kind of information sent through the electronic devices called the data. Data may have different definitions but all have the same basic meaning. An electronic device can contain any type of data like- photos, videos, software, games, etc. Data can also be called the fact that is stored inside the brain of a person. The purpose of saying is that data can have different forms but all mean the same thing.

Data science is a part of computer science in today's era and it uses principles and techniques in many fields like statistics, mathematics, statistics, information theory, information technology etc.

Data science may seem like a very general term to say and hear but it does not have a definite definition. It would not be wrong to say that writing the exact definition of Data Science is a very difficult task. Their specific role depends on the area in which the company wants to specialise. It is a field that has a mix of many fields such as statistics, computer science and mathematics. So it is very difficult to became a master in each and every field and be equally expert in all of them.

The field of data science is very broad. It is generally used in e-commerce industries, healthcare, banking, and consulting services. Data Science is a very multifaceted field, so those working in it get an opportunity to work in various fields.

In Data Science, Data Wrangling, Data Mining, Machine Learning, Linear Algebra, and Cluster Analysis etc. can extract large amounts of data. By using the data science big companies or any startup use all the data that can make their customers better experience.

Most of the work done in the present time is done through data only. Data Science is such a science, which in some places works in such a secret way. Apart from this, data science can easily transform the ideas seen in Hollywood sci-fi movies. That's why it is called the future of artificial intelligence.

Therefore, most of the people have very little knowledge about data science and how to make profit in it. So if you want to know more about Data Science, then here you are being provided information about, how to become a data scientist, salary, courses, qualifications etc.

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How to become a Data Scientist?

To become a data scientist, candidate has to obtain graduate, undergraduate or diploma certificate from certain institutions. Your resume can be recognise for a  data scientist by getting the degree or diploma certificate, and internship in networking. Many academies offer qualifications for pursuing this course. You can secure your career in Statistics, Data Mining and Data Analysis.

88% of the candidates who become data scientists have at least a master's degree and 46% of them have a PhD degree. Apart from this, to become a data scientist, candidates can also become a data scientist with a bachelor's degree in computer science, social science, physical science, and statistics because, in terms of study, mathematics and statistics (32%), followed by computer science. (19%) and Engineering (16%).

If you have obtained any of these degrees, then you can make a career in the field of data scientist, but most of the data scientists have master's degree and PhD degree and they become a successful data scientist.

At present, data scientists work in skill sets equivalent to statistics, analytical, programming skills and business skills as most of the data scientists have a strong background in mathematics or other domains of science. The value of big data cannot be harnessed unless a data scientist wants to. Therefore, there is a lot of demand for Data Scientist in the present time. The knowledge of data basics of data science is of great use in today's data driven world of science.

Eligibility Criteria for Data Scientist

To obtain the degree of B.Tech or B.Eng in computer science, physical science, mathematics, mathematics and computing, statistics or engineering, one has to take admission in these courses for which higher secondary education (12th) with physics, chemistry and maths grade) i.e. PCM subjects are important. 

Getting a master's degree and PhD in the relevant field after doing a degree course is also a plus point for a data scientist. To become a Data Scientist, some special technical skills are required, which I am going to discuss.

Syllabus of Data Science

Data Science Syllabus typically consists of three main components i.e. Business Intelligence, Machine Learning and Big Data. These three main components will be discussed in further detail with the topic Elements of Data Science. Apart from this the Data Science Syllabus to cover different areas is also given below-

Mathematical and statistical skills
Tool learning
Algorithms used in machine Learning
Statistical foundations for data science
Data structures and algorithms
Scientific computing
Optimisation techniques
Data visualisation
Matrix computers
Scholastic model
Experimentation, evaluation and project deployment tools
Predictive analysis and segmentation using clustering
Applied mathematics and informatics
Exploratory data analysis
Business acumen and artificial intelligence

Data Science courses

Elements of Data Science

The field of data science is vast yet divided into three main elements. Let us get a detailed look at these three components. 

1. Business Intelligence

Computing technologies are used to conduct any type of business. Every organisation produces huge amounts of data every day. Upon careful analysis of this produced data, data scientists present it in various graphs and charts. It then helps the management to make the best business decisions based on the representation of the data.

2. Machine Learning

Incorporating mathematical and statistical model algorithms, machine learning is adopted by almost all business organisations to understand and respond to daily situations and the progress of machines. By the use of machine learning in the sector of data science, a machine can predict various trends in markets or financial systems based on historical data patterns.

3. Big Data

With the exponential growth in internet users every day, viewers or customers generate many clicks in the form of videos, images, articles, comments, orders, etc. Usually, all these activities result in unstructured data generation. The data scientist completes the task of converting that unstructured data into structured data.

Data Science skills

Technical Skills Required for a Data Scientist

Being an interdisciplinary field, a data scientist not only requires one or two skills, but the field of computer science requires a diverse set of technical skills and knowledge. Some of the technical skills being discussed below are very important for a Data Scientist, irrespective of the experience gained.

Python Coding: Python is very important coding language in data science as it helps in developing models for data mining, machine learning or web scraping. Collecting different formats of data, Python can help you create and search datasets and import SQL tables into your code.

R Programming: R is a program, it is generally designed for the data analysis and also provides methods and formulas for information processing and statistical analysis.

Machine Learning and AI: Learning different techniques of Machine Learning like Logistic Regression, Decision Trees, Supervised Machine Learning, Time Series, Computer Vision, Outlier Detection, Survival Analysis, Natural Language Processing etc. to tackle many challenges in the field naturally are important.

Hadoop Platform: Whenever the amount of data is too much, it can exceed the memory of the system and the Hadoop platform helps the data scientists to send or transfer the remaining data to different servers. This Hadoop platform is also very useful for data sampling, data filtration and summarization, exploration etc.

SQL: Structured Query Language (SQL) is a programming language that helps you communicate, access and manage databases by adding, subtracting or deleting data. The data scientist needs to be proficient in SQL, as it is specifically designed through its compact commands, to save time and reduce the amount of programming required for difficult queries.

Knowledge of some additional skills is also very important to become a data scientist. If a Data Scientist has all the above-mentioned skills and does not have these skills, then he is not considered a good Data Scientist. These skills are - Java, Unix, PHP, Data Visualization, Unstructured Data, Apache Spark, Communication and Persuasive skills, Business Acumen, Data Wrangling, Algebra and Calculus, Statistics etc.

Roles & Responsibilities of Data Scientist

After completing education and acquiring the necessary technical skills in data science, there are also some roles and responsibilities on the data scientist, which are also important to maintain.

You always have to be up-to-date with all the techniques in Statistical Modelling and emerging tools, Machine Learning etc. Always mine the data and generate a hypothesis to help achieve high-level business objectives. Never complete the work by being overconfident always work in collaboration with other experts, IT managers, statisticians, programmers to make business decisions or build products and services. Data scientists also develop specially designed algorithms to solve various analytical problems with incomplete datasets.

Every work in the world is becoming dependent on software technology, due to the increasing software technology, becoming a data scientist is the best career option for students in today's time. In today's time, this sector is counted in the top 3 in terms of job and salary, while the future will also belong to it. If you want to become a Data Scientist and want to make a career in this field, then today we are giving you such tips, by which you can make a better career in this field.

There are many job options available to you after completing this course. These are the posts where you can make a career by working - Data Scientist, Software Tester, Senior Information Analyst, Information Officer, Data Analyst, Senior Data Officer, Business Analyst and Assistant Analyst.

Apart from this, in today's world there are many employment opportunities in the private sector as well. Here you can get employment in many places like Telecom, Bank, Insurance company, Utility, E-commerce, Finance, Hospital, Transportation, Construction plan, Oil or gas company, Manufacturing company.

A data scientist in data science is a designation that deals with the identification, representation, and data science extraction of meaningful information from data sources used primarily for business purposes. Furthermore, people working in data science work to extract useful insights, with huge amounts of facts being generated every single minute.

By now you must have understood that it is very important for a data scientist to always keep learning. A data scientist is someone who knows well how to find-out information from a raw data, and make that information usable in business. Most data scientists spend a lot of time collecting and filtering data, as the data is never properly filtered.

If you also want to become a good data scientist then it is important that you keep practising your skills. In addition to learning, you can practice the skills you've learned by creating an app, starting a blog, or exploring data analysis so you can learn more.

What is Career Scope in Data Science?

It is important to mention that the job of a Data Scientist is as good as it is difficult. As I have already mentioned that about 90% of the people working as Data Scientist are Master's degree and about 50% are PhD holders. This means that to become a data scientist, the educational background should be very strong.

Data science has made technology very simple and easy. Since data science has progressed very rapidly, machine learning has also become very easy. Studying Data Science offers a plethora of career opportunities in various fields. Studying Data Science will not only make you a Data Scientist but can also opt for various other job profiles under this vast domain.

Career as a Data Analyst

Responsibility of a data analyst is convert a dataset into a useful structure, such as presentations, graphs, reports, etc. They collect, refine, perform and analyze statistical data to support and influence the objectives of a business.

Being an entry level position in the organizational chart of business, a data analyst should have deep knowledge in Python, R, C, C++, HTML, SQL, Machine Learning, Excel, Probability and Statistics. They work closely with various departments and experts in the business to identify key business risks and performance in compliance data and convert them into a simple and legible format.

Career as a Business Analyst

Although a business analyst is technically less skilled in data science than their other counterparts, they still have a strong knowledge of all commercial processes and have a solid business intelligence. Acting as a liaison between IT and business administration, a business analyst is responsible for processing basic data through various data visualization tools and data modeling.

They mostly focus on preparing the data in the form of graphs, charts, reports etc which can be read easily and ultimately serves the interest of the business. If you plan to work as a business analyst, you will need a strong educational background in computer science, statistics, mathematics, business administration, economics, finance or other related fields.

Career as a Data Engineer

Proficient in coding languages   such as Python, SQL, R, Java, Ruby, Hive, Pig, SAS, etc., data engineers design, produce and manage large chunks of information or data. Data engineer handle the hardware systems that provide the data activities of a business. We can say that it is one of the most exceptional careers in data science.

Data engineers are responsible for developing an architecture that helps to process and analyze data in a way that is best suited for a business organization. One can secure a senior position under this career profile, having acquired an advanced degree in data science and significant years of experience. Apart from these three main and most sought after careers in data science, here are some other job profiles in which you can make your career-

Data and Analytics Manager, Marketing Analyst, Database Administrator, Statistician, Machine Learning Engineer, Data Mining Specialist.

Which companies hire Data Scientists?

According to my knowledge, the following companies hire Data Scientist-

Google, Twitter, Amazon, LinkedIn, Adobe, DHL, Microsoft, HP, IBM, Flipkart, Visa, Snapdeal, Yahoo, Bing, Spotify, Oracle, PepsiCo, Facebook, Coursera, Coca-Cola, Motorola, Uber, Logitech, Reddit, Dell, Johnson and Johnson, Slack etc.

These types of profiles are provided in the jobs obtained through data science. Which are below-

Data Scientist, Data Analyst Manager, Business Intelligence Manager, Data Analyst, Business Analyst, Data Architect, Data Administrator etc.

Top university for Data science 

As per my knowledge there are some universities which are considered to be top and very good education is given in data science. Below are the names of some top universities with annual fees-

1. Massachusetts Institute of Technology- Annual Fees - USD 52,566
2. Imperial College London - Annual Fees - GBP 28,489
3. The University of Texas at Austin - Annual Fess-USD 10,000
4. ESSEC – CentraleSupele - Annual Fees- Euro 12,500 
5. University of Melbourne - Annual Fees - AUD 36,512
6. University of Warwick - Annual Fees - GBP 28,007

In Asia, India also has some very good colleges where data science education is given very well. Below are the names of all those colleges-

IIT - Delhi, IIT - Hyderabad, Indian Institute of Science- Bangalore, IIT- Calcutta, IIT- Madras, Ahmadabad University, IIM- Calcutta, Goa Institute of Management, SP Jain School of Global Management Data Science, Symbiosis Institute, Pune
Great Learning - Mumbai, International Institute of Digital Technology Andhra Pradesh, Manipal Pro Learn, Indian Statistical, Institute, Kolkata, Indian Institute of Management, Ranchi, Indian Institute of Science, Bangalore, International School of, Business, Hyderabad, Indian Institute of Technology, Mumbai, Indian Institute of Technology, Kharagpur.

Advantages And Disadvantages Of Data Science 

The demand for data scientists is constantly increasing due to the growing world of software technology. In such a situation, if you are also thinking of becoming a Data Scientist, then this is a very good decision because at this time a Data Scientist is earning more than CA and Engineers. However, there are many advantages and disadvantages to becoming a data scientist.

Advantages of Data Science

High Job Demand : Nowadays there are very huge demand for data science professionals.Experts believe that in this field there are many job options.. It is the fastest growing job sector for the related professionals and expected that 12 million jobs by 2026. We can say that for data science it is the most employing platform. platform.

High Salary : Employees working in the data science sector are paid the highest. It can be believed that a Data Scientist can earn up to 40000$ per year. Due to which data science is considered as a highly lucrative career option.

More Opportunities in Data Science : Very little amount of people who already have all the skills to become a Data Scientist. However, even after this, everyone gets equal opportunities, because data science is a huge field and has many opportunities. The requirement in the field of data science is more but there are huge shortage of data scientists.

Disadvantages of Data Science

Wrong results of Data  : The task of a data scientist is analyzing the data and prepare future plans, But it is often seen that many times the given data turns out to be wrong. Which may have wrong results, may fail due to weak management and poor utilization of resources.

Data Privacy : Privacy of the customers are the biggest issue in today's time. For many industries, data is their fuel. On the basis of data promotion and e-commerce companies decide the selection of customers. However, sometimes customer privacy is breached because of the data used in this process. Client's personal data is visible to the company and at times security lapses can lead to data leaks.

Last Word

Friends, I hope that you must have got complete information about Data Science and Data Scientist. As I already mentioned that Data Science is a vast ocean of education and knowledge and to become a Data Scientist one has to dive into the ocean of knowledge. Continuous learning and staying up to date with data can make you a good data scientist. By becoming a data scientist, you can earn more money than expected. Friends, if you liked this article, then share it with others.

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  4. Fantastic overview of the ins and outs of being a data scientist! Thank you for your insights.

    Collaborating with a friend in the field, I recently published a step-by-step guide for getting into data analysis:

    The guide and linked materials are free to use and provide a solid foundation for people looking for a career change. Check it out and let us know what you think.

  5. Now, we live in a big data environment, and data scientists are the digital world's backbone. Data points now dictate customer activity, consumer behavior, corporate operations, and decision-making for organizations. Customers leave data footprints via neural networks across several platforms and devices, creating massive amounts of data.