- Data scientist
Who is a Data scientist?
Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway ~ Geoffrey Moore
Data science is about creating as much impact as possible for the client or company one is working for and who do you think does that? Yes, it is a data scientist. A data scientist is needed to help in solving real company problems using data. For example, while one serves a page on the internet and an ad pops up, have you noticed that you would only see the kind of ads in which you have shown interest in the past. Now, how did that happen? A data scientist goes through several logs, repositions and other forms of data in order to understand consumer behaviour and take out insights. This is just one example of data science, there are several other tasks that a data scientist does and the field is constantly growing. Are you one of many who dream of becoming a data scientist? Keep reading the article in order to be completely informed about what all the career can offer you.
The main word which comes up to our mind when we first hear the word data scientist is intelligence. Yes, and why not when the career is all about data, information and scientific knowledge, the power of analysis. Okay, so if you have the knowledge of analysing any kind of information scientifically then maybe you are a potential candidate of this field. But this profession requires a thorough study and a good understanding of science, computer and programming skills. A Data Scientist's work activity centres around the information. Depending on the data analysis, they help the organization make the right decisions and strong marketing strategy in the global marketplace. These days in the technological and software world, data science is the new and mostly used lingo. It is a major step towards learning how to use computers. Day-to-day technology advancement and the generation of huge volumes of data has increased the opportunities for data scientists nationally and internationally.
Data scientist in a Nutshell
Data scientists are generally called as big data wranglers, collecting and analyzing vast collections of data from multiple sources. The job of a data scientist includes computer science, mathematics, and statistics. Data scientists are analytical and statistical professionals, who use their technological and social science expertise to recognize trends and manage information.
Quick Facts for Data scientist
Average Salary in India
Male, Female, Others
We often see that some professions are gender-specific, as a certain job can only be done by a man or a woman. But this profession is something that no gender obligation is there. Male, female both can easily take up this career. Whilst data scientist is nowadays the most exciting place of employment. Though 18 per cent of contemporary data science positions are filled by women, and 11 per cent of data teams have really no female employees.
Table of Contents for Data scientist
What is the role of Data scientist?
Data science is an interdisciplinary subject matter that utilizes several statistical methods, scientific algorithms, information science, data assessment, machine learning theories, as well as other similar subjects to extract relevant information from a wide data group to generate a strategy for businesses to understand their information and analyse them. Data scientists acquire industry-specific understanding in analytical and programming skills. They write extremely complex algorithms to assemble big amounts of information. An organization plans its business strategy and plans, based on the performance provided by a data scientist.
The data scientist performs a significant managerial role in assisting to develop the base of state-of-the-art science and technological abilities to assist many organized and ongoing data analytics activities. The data scientist determines the data to be collected by the company, establishes strategies for modifying the data system to generate this information and works with many other data and analytics departments to establish methods that turn raw data into usable advanced analytics. The data scientist will also be a supervisor to help employees in the task, and maintain successful implementation of assignments.
The data scientist performs an analytical role in developing, applying, and analyzing complex computational equations and application methods in some of the most complicated problems of the business. A data scientist constructs quantitative and statistical patterns for various issues, which include economic forecasts, classification, clustering, pattern recognition, testing, simulation techniques. Data scientists explore new models to control and simulate end-user activity and behaviour and also develop techniques for data analysis and visualization to convey main objectives in advanced analysis.
The data scientist plays a strategic role in introducing new and innovative strategies for recognizing market trends and behavioural patterns in the business, and also ways of solving critical business challenges, such as product quality and total revenue. Data scientist produces useful information through the implementation of advanced statistical methods such as computational statistical models, segmentation analysis, consumer analysis, observation and data processing. The data scientist is liable for the cleaning of huge large amounts of data and allowing analytical ability to survey the data and identify business requirements.
After the data has been processed, it can be analysed. Scientists can begin decoding the texts in the data using a range of methodologies named observational data assessment. The process of exploring will result in significant cleaning up of data. Descriptive statistical analysis, like the average, maybe generated to better clarify outcomes. In order to gain more information on the data signals, data modelling could also be used to process data in graphical format.
A data scientist spends a considerable amount of time researching and planning solutions within the company for existing problems. Business intelligence developer is indeed capable of creating OLAP or analytical processing. Often, he or she deals with relational and multilayered databases. A business intelligence developer is an engineer responsible for the development, deployment and maintenance of business intelligence interfaces. This would include database tools, interactive dashboards and data analysis, ad hoc reporting, and data modelling tools.
Types of a Data scientist
Data Developers: Data developer is a data engineer who is skilled with user experience and is also knowledgeable about business value. Data developers recognize business objectives and are therefore not confined to store, retrieve, information flow issues. IT Engineers, Data Warehouse Developers or Software Developers may also be known as Data Developers. Database Developers are able to gather data before creating the database. They develop, build, evaluate, introduce and manage new databases as well as current ones. Developers of the database build management techniques and processes to access data stored in databases safely.
Data researcher: A data researcher is liable for collecting and analyzing the data, securing and regularly enhancing the quality of data from an institution and working with the research group to analyze information in a logical manner. Their job description includes supporting the research department in monitoring and evaluating all facets of the institution's business and using data analysis for services, company ventures, and activities in business growth. They are often accountable for devising or changing new and current statistical and numerical data.
Market research analysts: Market research analysts evaluate consumer behaviour to help businesses in deciding how their services and products should be formed, marketed, and commercialized. Several market research analysts are working for contractually hired consultancy firms. Else work directly at product companies as part of a marketing team for business owners. Market research analysts collect and interpret information about customers and competition. Market research analysts analyze market dynamics to look at future product or service sales. They help companies in understanding and producing products that consumers want.
What is the workplace/work environment of Data scientist like?
Data Scientist operates mostly at the offices. Data Scientists typically employ computer and software programmes to calculate their statistics. A data scientist's primary task is to gather, analyze as well as to conduct statistical analysis of the data. He or she can convert research and facts into simple English, to support businesses and organizations, so that they could know how to make smarter business decisions. Almost every company collects data whether it is a business plan, profits statistics, transportation or the cost of travel. A data scientist could even collect the numbers and find out a number of products, like pricing manufactured innovations, reducing shipping charges, or addressing the issue which impacts the business.
Does Data scientist require travelling?
Many data scientists travel both internationally and domestically frequently for the purpose of providing data access control, guidance, and supervision for major shareholders or other related companies. They do need to attend meetings, seminars and conferences, and meet clients and other professionals.
Full Time, Part Time
People do their work both on a full-time and part-time basis. But this profession is still opted for by people on a full-time basis. Most data scientists work full-time, though schedules can differ by responsibilities and deadlines
Data scientists work on contractual as well as permanent basis. Although, many data scientists operate individually, similarly employees could work as part of a team in organizations. Many data scientists consult on assignments with their colleagues or interact with clients around the globe. They can establish their business entirely as they would like in their sitting room, but they will have to be ready to travel to meet customers and workplace conditions may vary based on the location of the client or company they deal with.
Office, artificial intelligence offices
Data scientists often work in offices where they do their research work and
Presence in Geographical Area
In rural areas, Data Scientist has fewer or nil opportunities. Many Data Scientist workers are mainly employed in urban areas since there are many IT firms based in metropolitan centres. Urban areas such as Gurgaon (Haryana), Delhi (New Delhi) and Bangalore (Karnataka) are locations where a data scientist may find employment because they are the IT hubs of India. Though it is less yet still rising in rural areas.
Data science is a profession that requires a lot of hard work, persistence, and supervision. There are a lot of tasks for data scientists to perform within a day. Hence, the time pressure is immense. Meeting deadlines, completing projects, holding meetings, performing data refining and other significant research requires a lot of time management. The time pressure for data scientists is considered intense.
Data scientists are required to work overtime almost every day. In fact, they also work on weekends and sometimes holidays. They are required to report to their office before everyone and leave the office after everyone. Working overtime for long hours is quite common for data scientists.
Weekly Hours of Work
Min 45 Hours
Data scientists actually work 45 hours a week, with increasingly stressful projects that usually employ additional shifts. But the scientists who are only engaged in research work can spend more than 45 hours a week.
How to become a Data scientist?
What are the skills and qualities required to become a/an Data scientist?
Communication skills: The data scientist's communication skills are a must, in both written and verbal manner. To clarify complex mathematical material to senior data scientists and other stakeholders, a data scientist would need to communicate well. Therefore, with clear advice and observations specific to the audience at hand, they must have the ability to translate and tailor this technical content into relevant business material. Data Scientists need to explain clearly their findings to a client or a small team of corporate executives. The key to success lies in inefficient communication.
Analytical skills: The applicant must have a good understanding of the methods of data-mining and be able to utilize such strategies to real-world business problems. The data scientist would display a skill for the company to analyze data, recognize trends, problems or data analysis requirements. The main elements of a good data analyst are enthusiasm and creativity. Strong fundamentals in statistical analysis are essential but thinking regarding challenges through a kind of creative and analytical perspective is even more important. It will assist the scientists in producing active research questions that will help employees know the subject.
Technology skills: An applicant for this role requires to be technically advanced, demonstrate exceptionally good computer skills, and demonstrate an interest in science, statistics, and data processing and also a basic competency and desire to develop and implement effective data analysis strategies within a company. The data scientist must be trained in at least one language and therefore should possess some specific skills. Data Scientists use computer languages such as R and SAS for data collection, data processing, statistical analysis, and data display.
Interpersonal management: The applicant always needs to acknowledge positive characteristics that would suit him for the task, such as possessing the capability to work in a group or collaborative setting. They need to be result-oriented, to be detail-oriented, be a strategic and analytical thinker and also they need to possess superior leadership skills. Data scientists pay very close attention to specifics, even have the capacity to operate on complex projects and resolve any issues, have excellent problem-solving ability and maintain calm and relaxed moments of stress and confusion.
Computer skills : The important aspect to remember is that every Microsoft Excel-related analytical tool. The development of information analytics typically focuses on data loading and internal collaboration on an excel sheet. The important advantages of entering an advanced Excel programme significantly increase productivity by saving the cost, becoming an expert in managing spreadsheets, efficiently developing charts and figures, and enabling a comfortable environment to operate. Data scientists will also have a good sense of competence and can recognize advanced modelling and analysis approaches.
Which certifications and internships can be helpful in becoming Data scientist?
An internship is an experience which someone gets from a company. Organisations offer aspiring candidates to work with them for a given period of time and they are termed as interns. Interns are usually university or graduate students and also most internship programmes span is for a month to three or six months. The internship helps you to gain access to real-world jobs beforehand. This also allows students to improve the skills, expertise, including theories they developed mostly during the course. Interns in data science spend much of their time conducting an exploratory analysis of data and reporting their conclusions to the manager.
For many other circumstances, computer science and numerical activities are assigned to them, like analysis into new methods or machine learning techniques. An intern will be liable for employing statistical methods to give information into user data to direct decision-making and improve customer service. In certain situations, interns in data science are termed as data analysts. They spend most of their time reviewing data from the research and submitting their conclusions to the group. For other instances, computer science and mathematical activities are assigned to them, like research into new methods or artificial intelligence strategies.
Career Path Progression for Data scientist
Data Engineer: A data engineer is regarded as the foundation of any major company. This role requires considerable understanding of technical knowledge as a data engineer works with the data structure infra-structure of the organisation. Data engineers are usually software engineers. Data engineers are able to compile and implement database systems, composing complex queries, balancing to different systems, and establishing disaster recovery systems, rather than data analysis.
Data analyst: A data analyst collects, analyzes and performs statistical analyzes of the wide collection of information. They start figuring out how data can be supplied to answer questions and provide information. With the growth of computers and a growing drive towards technology development, data analysis. The database development has brought a fresh breath to the data analysts. It is the responsibility of a data analyst to take that information and use it to help the company make smarter business decisions.
Business analyst: A business analyst evaluates a company or business domain and records, analyzes the business strategy or its compatibility with emerging technologies, its business or methodologies or structures. Business Analyst serves to empower companies with the help of data analysis to enhance processes, products, services and applications. The main purpose of the business analyst is to help the company to introduce innovative technologies in a cost-effective manner by defining project or system specifications and communicating them directly to investors, facilitators, and associates.
Data scientist Jobs and Salaries
- Average Salary 40000
- Junior Level Salary 30000
- Senior Level Salary 100000
Data engineers are able to identify trends in data sets and for designing algorithms to help make the company 's raw data more reliable. This IT task requires a considerable range of professional skills, such as in-depth knowledge of SQL database design and multiple languages.
The more experience you would obtain in these fields the better salaries you can receive in this sector. At the initial stage of their career, they get a little less, though better than other careers. which are almost Rs. five lakhs per year, but gradually they earn more than twelve lakhs at senior level.
- Average Salary 40000
- Junior Level Salary 30000
- Senior Level Salary 200000
Roles in data analytics include data collection and clean-up to disclose business trends and insights. Data analyst's day-to-day role varies depending on the industry to industry or the type of expertise in data analytics they want. Data analysts may use business intelligence tools, Tableau, and programming to create dashboards, plan, and maintain inter-department relationship databases and frameworks throughout their organization.
In data science, salaries are centred on the various departments and the place that one holds. The salary increases steadily for experienced workers. The average income for a data analyst is up to five lakhs per year. At the beginning of employment, they earn four to five lakhs a year, and that rises more than twenty lakhs a year at the senior level.
What is the job outlook for Data scientist?
In recent days data science is used in almost each and every sector and has resulted in a sharp 45 per cent rise in total data science-related employment. The increasing importance of data scientists will give an indication of Data Science's future scope in India. Electronic health records, payment, health services, wearable devices data and different parts provide massive amounts of data each day. This offers healthcare professionals a precious step to reinforce quality patient care, driven by meaningful intelligence from previous patient information. Of course, it really does take place in data science. Nationally and internationally, data scientists are progressively reshaping the health-care sector. From strengthening patient care to gaining experience and expertise, by activating the potential information, they work to maximize each component of health care operation.
Though banks were among the earliest who used information technology for methodologies and protection. Banks use the technology to recognize and maintain their customers and also to get the new ones. Data analysis allows financial firms to become more adequately engaged with clients by recognizing their transactional trends. E-commerce and retail are one of the most important industries which require a huge level of data assessment. Effective data analysis applications would also help e-commerce companies to predict transactions, revenues, failures, and sometimes even manipulate people to purchase stuff by monitoring their behaviours and attitudes. Major retailers evaluate consumer profiles and market the related goods based on the findings in order to manipulate the people to purchase.
The transportation sector is going to create large amounts of data every day. Passenger counting systems, GPS tracking systems, checking tickets and boarding pass collection systems, and scheduling and wealth management systems obtain much of the industry data and information. Data Sciences includes the immense potential in providing information into the development and preparation of transportation infrastructure. Analytics from this collected data are essential in building sustainable competitive advantage, improves system accuracy and managing risk.
Frequently Asked Questions for Data scientist
Que. What does a data scientist do?Ans.
Data scientists are working closely with the company stakeholders to recognize their objectives and to assess how data could be used to accomplish the desired objectives. They design processes for data modelling, generate algorithms and predictive analytics to extract meaningful information, the business needs, then assist evaluate the data and share information and insight with coworkers.
Que. Is being a data scientist a good career?Ans.
Not only Data Science will offer a great career but it would also support you in your self-improvement. You could have a problem-solving outlook. You will be able to fully enjoy the best experience although most Data Science positions bridge IT and Management.
Que. Do data scientists code?Ans.
Yes, data scientists are able to code and If individuals have a data engineer or a machine learning engineer, that could help them place their code into production and finalize some of the things they do.
Que. Who is eligible for a data science course?Ans.
The basic qualifications for being successful to follow this programme include a Bachelor's degree in Science, Business Management, Engineering, Computer Applications, or Mathematics or a Master's degree in Statistics, Finance, or Mathematics with 50 per cent or equivalent scores.
Que. Is a data scientist similar to a software engineer?Ans.
Data scientist duty involves the dashboards for data processing, machine learning, algorithms and business intelligence. A software engineer develops programmes and applications. Developers will be actively engaged from design to coding, running tests and analysis throughout all stages in the project. So, yes we can that these two professions are not similar to each other.
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Questions related to Data scientist
What is the difference between Bsc data science and BSc IT (data science) in terms of curriculum? Which is more useful?
So you can have a rough idea about the two courses but which one is better is something only you can decide as you will know better which subject interests you more.
which is better to choose between BSc in IT (data science) from MAKAUT and BSc in Data science from TIU? Which is more useful curriculum for future industry?
Hello Apu sarkar, Both the Universities are best for bsc and infastructure is also good but facilities are more in TIU like sports campus, wifi campus, and many more and they all are not in MAKAUT and placement is also good at TIU. So I personally preferred you to go for TIU.
Hope it will be helpful for you :)
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