Data Science Eligibility Criteria

Entering the world of data science comes with certain qualifications and requirements. To be eligible, aspiring data scientists usually need a degree in a STEM field—science, technology, engineering, or mathematics. This educational background is crucial as it provides the necessary foundation for the analytical and problem-solving skills vital in data science. Exploring the criteria and requirements for data science reveals the key qualifications that pave the way for a fulfilling journey into this dynamic and ever-growing field.

Future Scope Of Data Science

Data democratization is rapidly changing our world. Every industry as well as governments around the world will be 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 is a highly employable and appealing profession.  Therefore, it is essential to have a deep understanding of the shifting demands of the current job market. 

By 2026, the demand for data science will be raised to around 11.5 million job openings. However, the industry has witnessed 650% growth since 2012 and is believed to have another rapid growth in the coming years.

Data Science Course Eligibility

Meeting the increasing demand for data scientists requires higher education to be adaptable and responsive. Certifications, degree programs, and executive-level courses must align closely with the workforce’s needs.

So, who can become a data scientist? The answer is simple—anyone willing to learn data science, whether they are newcomers or experienced professionals. Engineers, software and IT professionals, and those in marketing can all enroll in part-time or external data science programs. Now, let’s look at the eligibility criteria for data science courses.

For regular data science courses, a basic understanding of high school-level subjects is the minimum requirement. Since data science incorporates concepts from computer science, mathematics, and statistics, aspiring learners should aim to earn a degree in one of the STEM (science, technology, engineering, and mathematics) backgrounds.

The ones who are learning computer programming in high school can take advantage of this in the future while pursuing data science. Due to the subject knowledge in programming, statistics, or machine learning, learners have become experts in the practical implementations of data science methodologies. 

Individuals from other streams, like business studies, can study relevant courses in data science. Hence, anyone with a degree in business administration, such as a BBA or MBA, is eligible for higher studies in the data science domain. These professionals will 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 systems (MIS).

Data Scientist Qualifications and Skills

For those considering a career in data science, certain prerequisites and skills are crucial:

1. Degree: Graduation in the STEM stream has a solid foundation.
2. Mathematics: Foundational to machine learning, data science, and data analysis, mathematics encompasses algebra, statistics, calculus, differentiation, probability, arithmetics, and geometry.
3. Statistics: This subject will allow for understanding, analyzing, and drawing conclusions from data.
4. Data Visualization: After accessing and retrieving data, visualization is essential. Tools like R and Tableau are used for effective presentation.
5. Exploratory Data Analysis: Involves exploring Excel and databases to derive valuable information, learning from data attributes and properties.
6. Hypothesis Testing: Applied during case studies to formulate and test hypotheses, solving real business problems.
7. Programming Languages: While not an absolute requirement, knowledge of programming languages like Python, Java, and Scala is beneficial for learners.
8. Database: A strong understanding of databases is highly desirable for effective data science practices.

Module Of Data Science

Majority of the data science courses are PG- or certificate-level courses that are mainly structured for graduates. Many Indian institutions have started 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

Data Science Syllabus

Data science courses is structured by industry experts with years of experience in the domain. The syllabus is prepared since it can allow learners to be industry-ready and will apply the knowledge to optimize processes and performances in the industry. The syllabus will be also changed according to the industry standards and requirements.

Data science syllabus consists of 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. 

The major tools and programming :

• Python or R
• Mathematics 
• Statistics
• Algorithms
• Data visualizations
• SQL
• NoSQL
• Spark
• Hadoop

Requirements For Data Science

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

FAQ- Data Science Eligibility Criteria And Requirement