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A typical data science course syllabus focuses on machine learning, business statistics and mathematics, and Python programming, among others. Depending on the programme (degree, diploma, & certificate), subjects vary by institute and course.
✅ Data cleaning, data visualisation, SQL, big data, and programming languages (Python/R) are the common topics covered across core data science subjects.
✅ Further, the syllabus for data science courses generally includes theory and practical subjects, along with electives and capstone projects.
✅ Data science courses from top institutions also include internship opportunities and research work for industry experience.
✅ Bachelor's degrees in data science cover 20-40+ subjects.
✅ Popular data science degrees in India include a BTech CSE with Data Science, BSc in Data Science, BCA in Data Science, followed by master's.
This blog is a detailed guide to data science subjects, covering core courses, practical subjects, and training. Learn about the typical syllabus of undergraduate degrees in data science. Explore the crucial tools and skills covered in a data science programme, top regular and online data science programmes, and more.
A typical data science course includes core subjects, specialised elective courses, capstone projects, and opportunities for hands-on experience such as internships. The programme usually focuses on mathematics, machine learning, statistics, and programming languages. Apart from the basics, you also explore subjects such as deep learning, natural language processing (NLP), and data ethics and privacy. Each subject targets specific areas of data science and industry-relevant skills. However, the subjects in data science vary by type as follows:
*Note that the subjects covered vary by course and your chosen specialisations for each type.
Core subjects in data science include mathematics, Python/R programming, data visualisation, and machine learning. These subjects build your foundation to understand advanced topics of data science that may include deep learning, data mining, and big data analytics, among others. Here is the list of essential subjects that are generally covered in a data science course, regardless of its type. Explore what these subjects cover and teach you about:
Along with theory, you would also study subjects in a data science course that focus on skills development. You would learn about tools like SQL, Python, and data science labs to gain practical exposure. Moreover, you may also get to work on a capstone project by the end of the programme so that you can apply your learning in real-world scenarios. Here are some of the practical subjects that are covered in many data science courses:
Both Python and R are used in data science; however, they serve distinct purposes. Python helps with machine learning, production deployment, and making workflows scalable. R, conversely, focuses more on statistical computing, scientific visualisation, and data reporting. In this subject, you would learn to write code and use it to perform multiple tasks.
SQL is a special programming language that primarily works with databases. It teaches you to retrieve, filter, and manage data stored in databases. It is used for cleaning data before it can be finally applied for machine learning.
This subject contains various hands-on exercises using datasets, allowing you to practise analytical methods and solve problems related to data. It enhances your problem-solving and critical thinking skills.
Big data is about managing large and complex sets of data. In this subject, you learn to work with large datasets with the help of distributed data processing tools and frameworks.
Many undergraduate and postgraduate programmes in data science include research projects. In this, you may investigate a real-world problem and analyse your findings to prepare a technical report. Research-based projects strengthen your knowledge and practical understanding of different application areas that can be beneficial for securing jobs.
These are typically included in most of the top data science courses, while opportunities for internships may vary. The end-to-end project work allows you to recall and apply most of the skills you have learnt in the programme. Internships, on the other hand, can be useful to gain real industry experience.
Data science course subjects depend on the institute and the type of course you select. Typically, UG and PG degrees cover foundational subjects, programming languages, electives, and dissertation/project. The first year of the programme generally focuses on core subjects. The second and third years often emphasise specialised learning, practical work, and training. Here is the general syllabus of a three-year data science course:
Popular courses in data science include a BSc, BTech, and a BCA programme. These are undergraduate degrees that you can pursue after 12th in the data science field. Each course has its own curriculum and focus area. However, Python programming, mathematics and statistics, machine learning, and big data are some of the common subjects covered in these data science courses. You may explore the general data science subjects and syllabus covered in these courses:
A BSc course with a specialisation in data science lasts three years. It focuses on building your mathematical and analytical foundations. Furthermore, it covers subjects such as probability, Python programming, machine learning, and statistics, among others. Here is an overview of the general BSc in data science subjects, semester-wise:
A BTech in Computer Science and Engineering with Data Science as specialisation is a four-year programme. It consists of eight semesters, focused on the advanced areas of data science and computer engineering. Learn about MLOps, cloud engineering, data visualisation, big data, and more. Check out the semester-wise subjects covered in a BTech CSE data science course:
A BCA in data science combines the areas of computer applications with practical data analysis skills. The programme typically follows a duration of three years, comprising six semesters. It typically covers programming in C and Python, business statistics, machine learning, etc. You may look at the generally covered BCA in data science subjects:
*Note: Actual subjects and syllabus depend on the institution and the particular programme. We have mentioned the general curriculum for each course.
Python, Tableau, R, SQL (Structured Query Language), and problem-solving are some of the critical skills and tools that you usually learn in data science subjects. You gain hands-on experience across these multiple tools so that you can apply them to projects. Here is the list of crucial data science skills and tools that are covered across many courses:
Python helps you with cleaning, manipulating, and analysing large datasets efficiently. You learn to use libraries such as Pandas and NumPy. It also supports automation and the development of machine learning models for real-world data problems.
This is another coding language that helps you perform advanced statistical analysis. It is also used in data exploration and high-quality visualisation. Moreover, you can identify trends, test hypotheses, and present evidence-based insights with this tool.
It is a tool and a language that allows you to access, filter, join, and organise data stored in relational databases. It helps you extract the exact information required to answer business and analytical questions. Common queries that you may learn include JOIN, GROUP BY, SELECT, ORDER BY, etc.
It is used for data cleaning, calculations, pivot-table analysis, and report preparation. This is a practical tool to manage smaller datasets and supports you in communicating your findings in an accessible format. IF, SUMIFS, VLOOKUP, and XLOOKUP are some of the crucial formulas of Excel used in data analysis.
These are two different tools that help you convert complex data into interactive dashboards and visual reports. With these, you can make patterns, performance metrics, and insights easy to understand for those responsible for decision-making.
It educates you on how to build models that can predict outcomes, classify data, or identify hidden patterns. You can apply it to areas such as sales forecasting, customer segmentation, and risk detection.
Tools used to handle big data help you process, store, and analyse datasets that are too large to be managed by traditional software. You can use them for data cleaning of large datasets, making the analysis process faster, and scalability.
Data communication refers to presenting findings in an explicit and simple way so that both technical and non-technical audiences can understand the insight. Storytelling, on the other hand, connects the data to a business problem and suggests what action should be taken. Tableau, Looker, and Power BI are common tools used for these purposes.
Learn to understand patterns, measure uncertainty, test assumptions, and make reliable predictions. These skills build a foundation for A/B testing, forecasting, risk analysis, and machine learning in data science.
You would need these skills for various purposes, such as checking the accuracy of data, spotting bias, and questioning assumptions. It helps you with analysing data, testing hypotheses, and evaluating models.
UPES Dehradun, Amrita University, Vellore Institute of Technology (VIT) and IITs are some of the top data science course providers in India. Depending on the institution, you can pursue a BTech, BCA, BSc, BS, MTech, MCA, MSc, MS, or diplomas and certificates in the field of data science. However, the offered courses and their curriculum vary for each institute. Below is a list of several top data science courses in India with fees:
Apart from regular mode, you can choose to enrol in a data science course online as well. An online BS is a 4-year degree offered by IIT Madras in the field of data science with multiple exit options. Similarly, BITS Pilani, Amity Online, and IIT Roorkee are other top providers of online data science courses in India. Check out the list of several popular online data science courses with fees:
Data science subjects vary from course to course and by institution. Typically covered subjects include programming languages, particularly in Python and R, statistics, mathematics, data visualisation, and machine learning. Furthermore, you may be provided with elective courses to choose from in the areas of NLP, deep learning, large language models, etc. Bachelor's degrees like BSc, BCA or BTech cover 20-40 subjects, including core courses, electives, and project work or dissertation. For specialised courses, you can opt for certificate programmes. Check the subjects and syllabus thoroughly to ensure practical training and industry relevance.
Data science courses refer to the degrees, diplomas, and certification programmes that focus on data analysis, machine learning, data visualisation, and subjects and projects related to data.
Data science helps in the process of extracting insights from raw, large, and complex datasets and using them in the development of predictive models to guide and solve business problems across industries.
Yes, data science has a growing and strong future in terms of job prospects across industries. However, the demand for the role is shifting towards data cleaning, AI collaboration, and decision-making, rather than basic coding only.
Yes, the field of data science requires maths. However, proficiency in advanced topics depends on the type of role you want to pursue. Probability and statistics are the basic subjects covered, while linear algebra and calculus can be useful for machine learning models.
Data science is a vast field that covers machine learning, AI, and advanced algorithms, whereas data analytics is a specific area that includes the process of data cleaning, organising, and finding insights to solve real-world business issues.
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