Every industry today is relying on an understanding of the data generated through processes and products. To expand wide into the market space, businesses first need to work on existing product’s strengths and then penetrate the untapped market areas.
The whole of industries come with a set of processes streamlined into the operational flow and other supporting departments. Working on all the data that is generated from everywhere has led to an increased demand for professionals.
The experts here need to be equipped to fulfill specific business needs. Data Scientists are those professionals skilled in technical know-how. They have the aptitude for analyzing vast chunks of data that they can easily tap the problem areas and also wander into the untapped latent problem areas.
The overall objective is to bring significant business results and more eminent profits in the domain.
Opportunities in the Market
Numerous professionals and freshers alike are converging to enter the field of Data Science. It is an amalgamated job that combines data analytics, science, and management tools. It demands a working profile amidst enormous quantities of data that require solutions to business outcomes. Read: Compulsory skills you need to become a data scientist.
The rising demand of the industry has led to the growth of the profile of Data Scientists. According to a report, by 2026, there is an intended 19% rise in the number of data scientist jobs; and about 5400 new posts are about to be produced.
There are expectations about the data scientists salary structure’s growth. Reflective promotion opportunities are on the rise with the heightened amount of work output generated.
Multiple challenges crop up with the tremendous amount of data and differences in industries that require the administration to deliver a specific desired result. The tests extend from getting the industries knowledge and soft skills to function until the technical expertise on data analysis and business management tools.
If you are planning to get into the world of data sciences, there are specific prerequisites in the technical as well as non-technical domain that you need to work on before its first step.
Prerequisites for Data Science
It is not always necessary for professionals to have a data science background wheeling beforehand.
You might be a student or a fresher who is developing an interest in the data science field and planning to get individual experience in the sector. Or you might be a professional who is already established in one industry but wants to enter the data science courses because of the love for data or the rising interest and demand the profile offers.
The prerequisites that the field demands are categorised as follows:
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Data Scientist profiles vary with the expert level and your educational and experience profile. A minimum of a Bachelor’s degree is essential to pursue a data science’s course. A Bachelor’s/Master’s pursued in any of the STEM subjects proves beneficial as it lays the foundation to the basic mathematical or statistical knowledge that will prove to be of utmost importance in the future.
When beginning with the data scientist research, you must have been exposed to the requisites that are required in the industry for the job profile. The resources mentioned on t
he web for working as a data scientist must have displayed an array of skill and expertise requirements to fit the criteria. That is not always the case.
With the increasing qualifications, the knowledge and job profile will simultaneously increase. But there is always a difference in what is taught in the theoretical realm and the one you’ll gain on working professionally.
A PhD without experience would not be equal to another candidate with a Master’s qualification but having three years of experience.
Following are some of the technical and non-technical demands of the trade:
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Professionals and students from different backgrounds in Computer Sciences, Engineering, Economics, Mathematics or Operations, and Research enter into the industry of business development.
Not all of it is mandatory for a professional career in data sciences. The ultimate necessity is to have a clear and solid foundation on the mathematical and statistical concepts.
The demand in the domain of data science is mostly about clear statistical concepts of data that call for analysis to produce workable solutions to problem areas. Hence any background study will finish, but the polished and firm statistical and mathematical foundation is the entry-level call.
You don’t need to be a dedicated advanced programmer. Still, it would be best if you had a clear fundamental understanding of the concepts related to programming. Programming concepts like C, C ++, or Java will expedite the means of learning data science programming.
It is not required to be a hardcore programmer to help analyze widespread parts of data, to write quotes efficiently to explain the problem area and work with big data. Data science works on programming tools like Python and R. These concepts will help the candidate to journey a long way into the expertise of data science.
SQL or structured query language is one of the primary tools that is required to experience programming in data science.
For a firm footing in the work to be done, data scientists spend meaningful time writing SQL and script associated with it. You need to know how to write basic SQL, solve SQL query, and be comfortable with the groups, joins, or creating indexes.
It is not binding for you to gain excellence in database administration to work as a data scientist; because the basics of SQL are unmindful of the layers on top. Data analysis requires a strong foundation which can be retrieved from a database for the Hadoop cluster (example of language used).
You don’t need to get a degree in data science before entering the professional world. Data science requires the basics of statistics and mathematics, which should be clear to be able to analyze the problems that are at hand. To solve business problems, you need to have soft skills like team management and control over the projects to meet the deadlines.
To have a better comprehension, and a clear picture of the demands of the job profile with you are opting, you can get a business analyst certification online as well as pursue different courses on data sciences.
Machine learning is one of the fundamental concepts of data science and an indispensable part as well. Machine learning will be a part of your curriculum anyhow when you obtain a course online to earn a degree from University. Hence, it is not vital to know the basics of machine learning before your professional start.
Machine learning will be one of the determinations in the entire data science curriculum. An additional machine learning course online will help you with the analysis and fundamental foundation building element.
Working with Unstructured Data
Data Scientists work to analyze the business problem’s root cause and provide a solution framework with the help of data analysis tools. It will be beneficial to get your hands on popular data analysis tools like SAS, Hadoop, Spark, or R to get an understanding of what the data scientists work with.
Familiarity with Descriptive Statistical tools like Normal Distribution, Central Tendency, Kurtosis, Variability will guide your way to the long road.
Online certifications are prepared to help you further establish the expertise that is essential in the field.
With the idea that data science is there to help businesses solve problems and find problem areas, it would be of no use to have a technical strength in the data analysis part and be nil on the business acumen area.
Business acumen refers to the general knowledge of how businesses work. What are the necessary departments and how strong coordination is required to formulate teamwork to complete projects?
You cannot get an idea of business operations with your bachelor’s/master’s degree in technical science. And hence, an online course to help you with the basics of Business Administration will be beneficial.
It will provide a broader picture of how things need operations in an organization from a business point of view.
While working as a data scientist, it will be expected from you to work in a team, manage deadlines, handle project work, and coordinate with various departments.
Not everything is possible with the technical experience you have. For that, you need to be aware of specific business tools and management principles like team management, relationship-building, command, and division of work.
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A stronghold on soft skills like communication, leadership, listening, intuitiveness, and networking is essential when you will be working in a business.
Be it a small scale enterprise, a large multinational corporation; soft skills provide you with the knowledge and training of how to behave and deal with the diverse group of people in your team. The ultimate objective being profitable results.
The love for data and working with enormous amounts of it is one of the typical traits found in data scientists. The profession requires statistical analysis and mathematical functioning on the range of data present in front of you. Your craze and passion for data analysis will help you solve complex problems in businesses that are something not everyone, even the topmost management can solve.
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Data Scientists are specialists with different skill sets. It isn’t simple to master all the trades for a single self.
With the price, comes the challenging part of data sciences. With the right direction to the path ahead, the knowledge, training (from various courses pursued), and experience in the field will altogether gradually add to the budding professional career of yours.
If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s Executive PG Programme in Data Science.
What are the business applications of data science?
The finance staff at your company may use data science to develop reports, predictions, and evaluate financial patterns. You may also utilize data science to improve your company's security and secure critical data. Identifying inefficiencies in manufacturing processes is another method to apply data science in business. Purchase data, celebrities and influencers, and search engine queries may all be used to find out what items consumers are looking for.
Why is it necessary for a data scientist to have good communication skills?
All of your amazing research and insights might be swamped if you don't have good communication abilities. You'll need strong communication skills as a data scientist to fill out your qualifications and make your work accessible to the rest of the company. Communicating well with colleagues in different departments can help you gain access to possibilities that will help you further your career inside the company.
How is data science a dynamic field?
Data Science is a synthesis of several disciplines, including statistics, computer science, and mathematics. It is impossible to master all fields and be equally knowledgeable in all of them. A person with a background in statistics may not be able to quickly learn Computer Science and become a competent Data Scientist. As a result, it is a dynamic, ever-changing discipline that needs continual study of the different aspects of Data Science.