Data Science Training in Kolkata

But what is data science the world's largest question and confusion?

I would see data science as one, and I would find solutions to questions they are exploring from one to many attempts to work with data. In summary, we can say that it concerns much more data than science. You are exploring it according to your own needs and the exercise of analysing your data, trying to obtain some answers or to fulfil the needs of society through your data explored, manipulated and exercised – it is data science. You are curious about working with and manipulating the information according to your needs.

Today, data science is relevant, as millions of data are available on single or single data. We have not used the absence of data to worry. We now have tonnes of information. We had no algorithms defined in the past, now we have algorithms. The software in the past wasn't accessible by all, because it was too costly, so only big-bucks industries can use it but it is now free and open source. Because the storages are also very costly and available for a split cost now, it allows us to get billions of data sets for a very low cost. We don't think we could store a large amount of data in the past. The Internet connectivity was also not common and was not too expensive. Therefore it is all cheap, all available, everything ubiquitous and here, the tools for working with data, data variability, data storage, data analysis and last and most important connectivity! No better time than now to be a data scientist.

Career Path in Data science :

Data science is currently considered one of industry's most lucrative jobs. Data science jobs show only signs of growth with numerous openings across all sectors. As companies are increasingly engaged in data science, companies are employing hordes of data scientists. However the demand gap for data science jobs compared to applicants is only expanding although India is a pioneer in technological education and research.70 percent of jobs in this sector currently cover data scientists with less than five years of experience in analytics in the analytical ecosystem.

A data scientist's career path is difficult for various reasons to trace. Most management at the medium and high-level levels with over 10-15 years of experience began with software or coding designations, because the industry was not developed enough to include a data scientist's designation. But now things are changing, and the next generations of data scientists will have a clearer understanding of their careers.

Job Position :

1. Data Scientist :

The creme de la créme is in any company a "Data Scientist." That's why professionals are most popular nowadays for this designation. This designation is used by many companies as it is easy to search and apply for aspirants. For the same purpose, other companies use designations such as "business intelligence expert" or "market analyst."

• The role of a data scientist : "a unique mix of capabilities which can both open up data insights and tell a wonderful storey via the data" is defined by the American mathematician and computer specialist DJ Patil. Data scientists also have to develop machine learning models for prediction in modern workplaces, find patterns and trends in data, view data, and even pitch marketing approaches.

• Skillset : statistics, math, data modelling, programming of Python or R, Additional abilities: Business skills, Visualization/BI, presentation capability. Business acumen.

• Company ladder : The ladder would look like the following for a datologist / IT expert / market analyst. It is to be observed, however, that organisations, according to their convenience or their corporate structure, can rename certain appointments. A "lead data scientist" in certain organisations, for example, can be called a "main data scientist."


2. Data Analyst :

Organizations normally use this designation to report that more technical knowledge is involved in this role. Some of its synonyms are 'professional analysts' or 'business analysts.

• Role : The role of a data analyst is based on company data, which can then be used by the C-suite for action. The fact that your projects usually change from time to time is another interesting fact about data analysts. Thus, the marketing department can be operated on by a data analyst for 3 months, and production may take the next one.
• Skillset : Python or R, Tableau Data Modeling. Additional competencies: acumen of business, competency in database, visualization/BI, skills in presentation.
• Corporate Leadder : the ladder of a professional/business analyst for a data analyst would look like the following. It must be noted, however, that this name also offers the flexibility to move laterally to more specific roles and niches.

3. Data Engineer :

Any large organisation has a data engineer as its backbone. In general, companies hire data engineers to channel their talents for the development of software. The Data Architect and the Quantitative Analyst are synonymous with some of its roles. "

• Role : This role requires a deep knowledge of programming skills as a data engineer works with the core data infrastructure of the organisation. A data engineer is responsible for the construction of data pipes and the correction of the data flow in most organisations, so that information is received from the relevant Departments.
• Knowledge set : management of database, cleaning of data, programming Python or R, Hadoop. Other competencies: business acumen, skills in database cleaning, visualization/IB, skill presentation.
• Corporate lead : The corporate ladder would look like something like the following for a data engineer / data architect / quantitative analyst. Since it is more central and niche to the organisation, side movement is unusual. This job is, however, the most difficult to lay off for the same reason.

4. Business Intelligence Developer :

A developer of business information is a kind of jack of all businesses in any organisation which must essentially have a strong understanding of the fundamental elements of analysis and of the IT department. The "systems analyst" and "machine learning engineer" are among his synonymous roles.

• Role : The role of a computer scientist has a great deal of overlaps with key features such as data science, programming and data architecture. This is not just analytical but technical, and calls for advanced knowledge of every popular technology learning.
• Skillset : programming of Pythons or R, Hadoop, modelling, Notebook, Github, modelling of data.Additional skills: Accurate business, Visualization/BI
• Career Ladder : A corporate ladder would look something like this for a computer engineer/system analyst/machine learning engineer. Because this role is relevant for nearly every other sector, notably digital and emerging technology, the lateral movement in the organisation also has a great opportunity.

Industry Path in Data Science :

Data science allows dealers to influence our buying practises, but the importance of data collection goes far beyond that. Data science can enhance public health by wearable trackers, which motivate people to adopt healthier habits and alert people to potential health challenges. Data may also enhance diagnostic accuracy, speed up the finding of treatments for certain diseases, and even stop a virus spreading. when the outbreak of Ebola virus reaches West Africa, scientists could track the disease's spread and predict the areas most vulnerable to the disease. These data have been used to prevent health officials from getting into the world before the outbreak. These data helped health officials to cope with the outbreak and prevent a global epidemic. In most industries, data science has critical applications. For example, farmers use data for effective food growth, food suppliers to cut food waste and non-profit organisations to boost fund-raising efforts and forecast funding needs. For example, farmers use data to reduce food waste. Economist and Freakonomics author, said in a lecture that CEOs are aware of the importance of Big Data but have no appropriate teams at their disposal to perform these skills.

"I really still believe that combining collaboration with corporate big data and randomising [...] will be at the heart of the economics and other social science." Pursuing a career in data science is an intelligent move, not only because it is trendy and well worthwhile, but because data can well be the centre of interest for the whole economy. In virtually every job – not only in technology – data science experts are needed. Actually, only one-half of a million employees are employed by the five largest tech companies — Google, Amazon, Apple, Microsoft, and Facebook. However, advanced education is generally necessary to break through to these highly-paid, on-demand roles. "Data scientists receive high educational qualifications–88 percent have at least a master's and 46 percent have a PhD–with remarkable exceptions, the profound knowledge required to be a data scientist is often very strongly educated," reports KDnuggets, a leading site on the Big Data.

Data Scientists are in Constant Demand :

Schedlbauer concludes "There is a clear need for professional professionals to understand a business requirement, to develop a data-dynamic solution and then to implement that solution," although work on information sciences will probably be automated over the next 10 years. Almost all fields, from state security to dating applications, require data science experts. In order to successfully serve their customers million enterprises and government departments rely on big data. Careers in data science are highly requested and this trend will not soon slow, if ever.There are a number of ways you can prepare to take on these challenging but exciting roles if you want to break into the field of data science. Maybe most importantly, by demonstrating your expertise and previous working experience, you will need to convince future workers. One way to build these skills and experience is to conduct an advanced degree in your field of interest.

For example, the University of Northeastern offers Masters in data science and data analytics to develop the skills employers seek. Both programmes also offer students a chance to participate in cooperatives and experiential learning experiences, so that they can build practical experience before graduation. Once factors such as your personal background, interest and career aspirations are taken into consideration, you can determine which degree programme is right for you and take the next step in achieving your goals.

Advantages of Data Science :

Data science's different advantages are :

There is a high demand for data science. There are many opportunities for prospective job seekers. It is Linkedin's most rapidly growing job and will generate 11.5 million jobs by 2026. Data Science is therefore a highly employable sector.

Very few people have the necessary skills to become a full data scientist. In comparison with other sectors of IT, data science is thus less saturated. Data science is therefore an extremely rich field and has many opportunities. Data science is highly requested, but low in data scientists' supplies.

One of the highest paid jobs is data science. Glassdoor reports that the average annual rate for data scientists is $116,100. Data Science is therefore a very lucrative career opportunity.

Data science is used in numerous applications. It is widely used in the fields of healthcare, banking, consultancy and e-commerce. Data science is a multi-faceted field. You will therefore have the chance to work in different areas.

Companies are demanding that qualified data researchers process and analyse their data. They analyse and improve not only data but also quality. Data Science is therefore involved in enriching data and improving it for its company.

Data scientists make smarter business decisions for companies. Firms rely on data scientists and use their expertise to give their customers better results. Data scientists are therefore given an important role in the company.

Data Science contributed to the automation of redundant activities by different industries. Companies use historical data to train machines for repeated tasks. The arduous work of people before has been simplified.

Data science involves the use of machine learning, which has allowed industry to create better, more customised products.

For example, e-commerce website recommendations provide users with personalised insights based on historical shopping. Computers have now been able to understand human behaviour and take decision-making based on data.

Data science has improved significantly in the healthcare sector. Early-stage tumours are easier to detect with the development of machine learning. Many other health industries also use data science to assist their customers.

Data Science not only offers you a great career, but also helps you to grow yourself. You can have an attitude that solves problems. Since many data science roles are a bridge between IT and management, you can enjoy the best of both worlds.

Roles and Responsibilities :

Data scientists work in close collaboration with business players to understand their objectives and to identify the use of data to achieve these objectives. They develop processes for data modelling, create algorithms and predictive models to collect data from business requirements, analyse and share data with others. The process for data collection and analysis, while each project is different, generally follows the following path:

• Ask the right questions to start the process of discovery
• Data collection
• Clean and process data
• Data integration and storage
• Initial data research and data analysis exploratory
• Choose one or more possible algorithms and models
• Apply techniques of data science, such as machine education, statistical modelling and artificial intelligence
• Measuring and enhancing results
• Present the stakeholders' final results
• Make feedback-based adjustments
• Repeat a new problem solving process.

Payscale :

The average salary for data scientists is 698K Dollar. An enterprise scientist with less than a year's experience can earn around 500K per year. Early data scientists with 1 to 4 years of experience have a yearly experience of 610K. A data scientist with a mid-level experience of 5 to 9 years earns 100K dollars per year in India. As your experience and skills increase, as senior data scientists in India, your earnings increase dramatically by more than 170K a year! Data Engineers are about Rs. 7 LPA in their early careers (1-4 years of experience). The wages of a data engineer are Rs. 121K as they move to the mid level (with five to nine years of experience). More than Rs.157K LPA can be made by data engineers with 15 years of work experience. And you can earn an average total compensation of Rs 900K when you are a mature and experienced data analyst, who has been in the sector or 10 to 19 years.