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Data science as a field is interdisciplinary and is about applying the scientific approach to processing large data requirements. It involves several scientific techniques and theories that have been derived from various fields such as statistics, mathematics, information science, computer science, and domain knowledge.

**Data Science Demand**

Data democratization is transforming our world in a fundamental way. Every industry as well as governments around the world are acting on a wide variety of data sources. Businesses have undergone major transformations with insights and predictions powered by data.

The aforementioned changes led to the requirement for data science professionals across various industry verticals that had rapidly shifted. Businesses are having to rethink hiring, training, and partnerships. The latest demand is for a group of professionals who are skilled in data analytics, machine learning, and artificial intelligence.

These days, data science may seem like a highly employable and appealing profession.** **However, instead of looking at what’s trendy, it is essential to have a deep understanding of the shifting demands of the current job market.

As per a report by the U.S. Bureau of Labor Statistics, by 2026, the increasing demand for data science will create approximately 11.5 million job openings. Therefore, the industry has seen 650% growth since 2012 and is expected to have another rapid growth in the coming years.

After the United States, India is the second most prominent hub for data science developments. This demand has introduced a rapid surge in the data science course providers.

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**Data Science Course Eligibility**

To be able to meet the explosive demand for data scientists, higher education needs to be nimble and responsive while certifications, graduations, and executive-level programs have to be responsive to the requirements of the workforce.

So, who can become a data scientist? The answer is simple, anyone who is willing to learn data science, be it a newcomer or an experienced professional. Engineers, software and IT professionals, and marketing professionals alike can sign up for part-time or external data science programs.** **Now, let’s take a look at the eligibility for data science courses.

To take regular courses in data science, knowledge of basic high-school-level subjects are the minimum requirement. As data science is, loosely, an amalgamation of the concepts derived from computer science, mathematics, and statistics, learners should aspire to, first, earn a degree in one of the STEM (science, technology, engineering, and mathematics) backgrounds.

*Have a look at the key *** differences between Computer Science and Data Science**.

Studying computer programming in high school can benefit you in the future while pursuing data science. Based on the subject knowledge in programming, statistics, or machine learning, learners become experts in the practical implementations of data science methodologies.

Individuals from other streams, such as business studies, can study relevant courses in data science.** **Likewise, anyone with a degree in business administration, such as a BBA or MBA, is also eligible for higher studies in the data science domain. These professionals can go on to work in the capacity of executives and be responsible for generating customer relationship management (CRM) reports, business-related data quality assessment (DQA), and management information system (MIS).

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**Data Scientist Qualifications and Skills**

Let’s take a look at some of the data science course eligibility criteria. For admission to a data science course, generally, the following qualifications are required:

**Degree:**A graduation from the STEM stream**Mathematics:**It is the heart of machine learning, data science, and data analysis because the models are created by processing the data of mathematical algorithms. Mathematics includes algebra, statistics, calculus, differentiation, probability, arithmetics, geometry, etc.**Statistics:**This subject helps one understand, analyze, and derive a conclusion from the data.**Data Visualization:**After data access and retrieval, data visualization is performed and presentation is done with R and Tableau.**Exploratory Data Analysis:**It involves exploring Excel and databases to derive useful information and conclusions and learn from data attributes and properties.**Hypothesis Testing:**Hypothesis formulation and testing are applied during case studies for solving real business problems.**Programming Languages:**While coding is not an absolute criterion for eligibility to sign up for data science courses, knowing programming languages, such as Python, Java, and Scala, will surely benefit the learners.**Database:**A good understanding of databases is also highly desirable.

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**Data Science Curriculum**

Most data science courses are PG- or certificate-level courses that are tailored for graduates. Recently, Indian institutions have introduced several degree-level programs in data science and analytics. A typical data science course will include the following modules:

- Git
- Python with Data Science
- Advanced Statistics
- Data Analysis with Excel
- Machine Learning and Prediction Algorithms
- Data Science at Scale with PySpark
- AI and Deep Learning Using TensorFlow
- Deploying Machine Learning Models to Cloud (MLOps)
- Data Visualization with Tableau
- Data Wrangling with SQL
- Natural Language Processing

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**The Science Behind Data**

The field of data science highlights the processes involved in methods, algorithms, and systems, with the help of which knowledge and intelligence are derived from a pool of structured and unstructured data. Several data-driven initiatives like big data, data mining, machine learning, and AI are catered to by Data Science.

Data science could be considered as a concept that unifies statistics, data analysis, and methodologies to perform analysis and make sense of real events with the help of data.

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**Data Science Syllabus**

Usually, data science courses are designed by industry experts with years of experience in the domain. The syllabus is prepared so that it can help learners be industry-ready and apply the knowledge to optimize processes and performances in the industry. The syllabus is also tuned to align with industry standards and requirements.

Data science syllabus mainly comprises subjects in data science and focuses on specific areas such as open-source tools, databases, libraries, Python, R, SQL, data analysis, data visualization, and machine learning. It follows data handling methodologies and implements models based on systematically-designed algorithms.

Some of the major tools and programming languages that are used in data science are:

A data science professional is expected to possess the following skills and competencies:

- A good understanding of mathematics, computer fundamentals, statistics, and machine learning
- Expertise in one or more programming languages such as R or Python
- A thorough understanding of databases
- Skills in big data tools such as Hadoop, Spark, and MapReduce
- Experience in data wrangling, mining, cleaning, visualization, and reporting tools

*Go through the Data Science Course Syllabus to learn about the curriculum in detail.*

**Conclusion**

Data science is a very rewarding field with better opportunities when compared to any other field. It requires following a certain career path to becoming a successful data scientist. First and foremost, a bachelor’s degree in computer science, IT, mathematics, or a related field is essential. After the completion of the degree program, one can start with an entry-level job as a data analyst or a junior data scientist to gain experience before moving on to bigger and better opportunities.

The career of a data scientist can really grow significantly if professionals invest their time and effort to earn a master’s degree or Ph.D. One can pursue a master’s degree while simultaneously doing an entry-level job as well. After the completion of higher degree programs, one can start climbing up the career ladder with more opportunities.

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**Frequently Asked Questions (FAQs)**

Absolutely! Anyone with interest and dedication can learn data science. It blends computer science, math, stats, and domain knowledge to extract insights from data. While a background in these areas helps, it’s not a must.

Data science suits those with analytical minds, math and coding skills, curiosity, adaptability, and a desire to extract insights from data to drive decisions.

Yes, any graduate can pursue data science with the right interest and dedication. While a background in quantitative fields can be quite helpful, resources and courses are available to acquire the necessary skills.

Absolutely! A BSc student can excel in data science. STEM backgrounds, like mathematics or physics, provide a solid foundation. These disciplines equip students with essential quantitative and analytical skills for success in data science.

A bachelor’s degree in mathematics, computer science, statistics, or physics is the basic requirement for data science. Nevertheless, employers often favor candidates who hold a master’s degree in data science or a related field.

The qualifications for a data scientist vary depending on the specific employer and the job position. However, the minimum qualifications typically include a bachelor’s degree in a related field, such as computer science, mathematics, statistics, or physics.

Some streams that are well-suited for data science include Computer science, Mathematics, Statistics, Data science, Physics Engineering, Economics, and Business analytics.

Yes, a 12th pass can become a data scientist. However, it will require more effort and dedication than if you had a bachelor’s degree in a related field.

Data science is challenging but achievable. It demands a solid base in math, statistics, and computer science. Despite lacking a formal background, ample resources exist to acquire these skills.

Yes, data science involves a significant amount of mathematics. It requires understanding concepts like statistics, probability, linear algebra, calculus, and optimization. These mathematical principles form the basis for data manipulation, analysis, and modeling.