Data Science Syllabus: Data science is the process of extracting insights from raw data. It includes processes such as collecting, organizing, filtering, and processing data. The prominent topics of data science syllabus include machine learning, artificial intelligence, Big Data, modeling, and data visualization. Some of the key components of the syllabus have been discussed in the below sections.
Data Science Syllabus, An Overview
Data structures, statistics, mathematics, algorithms, machine learning, coding in Python, R, etc. are some of the key subjects of the data science syllabus. The syllabus varies as per the course you choose. An overview of the data science syllabus has been given below:
| Data Science Syllabus, An Overview | |
| Particulars | Details |
| Course Duration | 3 months to 3 years |
| Course Mode | Both Offline or Online |
| Requirements for the course | 12th Science |
| Prerequisites | Fundamentals of computers, math, and statistical sciences. |
| Main Subjects |
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| Opportunities Available In The Market |
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| Data Science Courses |
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Also read: Data Science Course Syllabus and Subject
Data Science Syllabus for Introductory Courses
Some data science courses are meant for beginners. These courses can be classified as introductory courses, and they discuss the fundamental concepts of data science. Some of the key components of these courses are explained below:
| Data Science Syllabus For Introductory Courses | ||
| Sr. No. | Syllabus Topics | Description |
| 1 | Introduction to Data Science | The processes related to gathering, organizing, interpreting, and making decisions are explained in this topic. It covers the fundamental principles and terms of data science. |
| 2 | Business Intelligence | The techniques used to draw meaningful insights from raw data and using those insights to make business decisions are explained in this topic. The concepts related to business analysis and evaluation methods are discussed in it. |
| 3 | Cloud Computing | The topics such as cloud architecture, cloud security, database management via cloud services, etc. are discussed in this topic. |
| 4 | Communication and Presentation | Presenting data and communicating the data lapses are covered in this topic. |
| 5 | Data Warehousing | The ways of building a data warehouse, components of data warehousing, leveraging data warehousing techniques, etc. are explained in this topic. |
| 6 | Data Mining | Mining data from various sources, pre-processing data, data modeling, data presentation and classification, clustering, and various other concepts are discussed in this topic. |
| 7 | Data Visualization | The fundamentals and importance of data visualization are explained in this topic. It also covers how to use different data visualization tools such as Tableau. |
| 8 | Machine Learning | The basics of machine learning along with how to build and train data with machine learning models are discussed in this topic. |
| 9 | Model selection and evaluation | Observing statistics of machine learning models and determining the actions to be taken are some of the key topics covered in this unit. |
| 10 | Storytelling with Data | Storytelling using latest and traditional research techniques and data visualization applications are explained in this topic. |
| 11 | Understanding Exploratory Data Analysis | Understanding data components in-depth and observing various characteristics of data can be learnt from this topic. |
Also read: BSc Data Science Syllabus, Subjects, Semester, Teaching Process
Key Components Of Data Science Syllabus
Data science courses are designed to impart theoretical and practical knowledge and skills to the students. Most of the courses are designed around the key components of data science. Some of the common and prominent components of data science syllabus are discussed below:
| Key Components Of Data Science Syllabus | ||
| Sr. No. | Component Name | Description |
| 1 | Big Data | Big Data refers to the raw or unstructured data comprising text, videos, images, and other forms of data. Various strategies are employed to organize and structure this data. This unit of the data science syllabus focuses on acquainting the students with the techniques and strategies used to transform unstructured data into organized data sets. |
| 2 | Machine Learning | The process of making human language understandable to machines is referred to as machine learning. It consists of algorithms and models to code and encrypt machines so that they can get accustomed to human language and emotions. Time series forecasting, predictive analysis, and some other processes require statistical machine learning models. You have to feed the data to these models so that they can be trained in forecasting data accurately. |
| 3 | Business Intelligence | Business Intelligence or BI is also referred to as business acumen. It helps you to interpret the data and draw meaningful insights from it. By using visual elements like charts, graphs, etc. you can present the data in an informative and appealing way. All these concepts along with uses of AI (Artificial Intelligence) are discussed in this unit of data science syllabus. |
| 4 | Modelling Process in Data Science | The process of using data to build data models is explained in this unit. It includes important stages such as data preparation, data understanding, data collection, evaluation, deployment, and various other modelling methods and strategies. |
Also check: Data Science Course Duration: Planning Your Learning Journey
Data Science Subjects
The key data science subjects across which the syllabus is distributed have been specified in the below table:
| Data Science Subjects | ||
| Sr. No. | Subject Name | Details |
| 1 | Introduction and Importance of Data Science | The fundamentals and processes involved in data science are explained in this subject. It also highlights the relevance of data science in the modern world. |
| 2 | Big Data Fundamentals and Hadoop | Big Data fundamentals and the Hadoop framework used to process large data sets are covered in this subject. The techniques used for processing Big Data are also revealed in it. |
| 3 | Applied Mathematics and Informatics | The mathematical fundamentals and role of informatics in data science are explained in this subject. Program theory, coding theory, cryptology, program verification, and some other topics are covered in it. |
| 4 | Information Visualization | The process of transforming complex raw data into meaningful visuals is called information visualization. The techniques and tools used in this process are discussed in this subject. |
| 5 | Data Mining, Data Structures, and Data Manipulation | The data mining tools and techniques are discussed in this subject. It also talks about data manipulation processes that make data understandable and interpretable. Data structure types like linear, tree, hash, and graph data structures are also explained in it. |
| 6 | Statistics | The basics of statistics and their relevance in the field of data science are explained in this subject. |
| 7 | Integration with R | The integration functions in R and how to write them are explained in this subject. |
| 8 | Algorithms used in Machine Learning | Logistic regression, linear regression, KNN Algorithm, Naive Bayes Algorithm, and various other algorithms that are used in machine learning and its models are discussed in this subject. |
| 9 | Predictive Analytics and Segmentation using Clustering | The process of segmenting data using certain characteristics or similarities along with clustering techniques is explained in this subject. It also talks about predictive analytics i.e. how historical data and trends are used to predict or forecast future trends and patterns. |
| 10 | Data Scientist Roles and Responsibilities | The various roles and responsibilities of a professional data scientist are explained in this subject. It talks about data collection, interpretation, manipulation, and various other tasks that a data scientist performs on a daily basis. |
| 11 | Data Acquisition and Data Science Life Cycle | The methods and tools used to gather data from various sources are discussed in this subject. The other aspects of data science life cycle like ideation, data exploration, validation, research and development, monitoring, and delivery are also discussed in it. |
| 12 | Deploying Recommender Systems on Real-World Data Sets | The approaches, techniques, flaws, and challenges of recommender systems are discussed in this subject. |
| 13 | Experimentation, Evaluation and Project Deployment Tools | This subject talks about deployment tools, experimentation techniques, and evaluation strategies used in machine learning models. |
IIT Data Science Syllabus
The key components of data science syllabus covered in IITs are listed in the below table:
| IIT Data Science Syllabus | |
| Sr. No. | Core Subjects |
| 1 | Introduction to Statistical Learning |
| 2 | Computing for Data Science |
| 3 | Data handling and Visualization |
| 4 | Introduction to Data structures and Algorithms |
| 5 | Mathematical Foundations of Data Science |
| 6 | Matrix Computations for Data Science |
| 7 | Information Security and Privacy |
| 8 | Optimization for Data Science |
| 9 | Statistical Foundations of Data Science |
BSc Data Science Syllabus
The key components of data science syllabus covered in BSc Data Science course are listed in the below table:
| BSc Data Science Syllabus | |
| Sr. No. | Core Subjects |
| 1 | Basic Statistics |
| 2 | Data Structures and Program Design in C |
| 3 | Discrete Mathematics |
| 4 | Probability and Inferential Statistics |
| 5 | Introduction to Artificial Intelligence |
| 6 | Machine Learning |
| 7 | Cloud Computing |
| 8 | Operating Systems |
| 9 | Object-Oriented Programming in Java Machine Learning |
| 10 | Data Warehousing and Multidimensional Modelling |
| 11 | Operations Research and Optimization Techniques |
B. Tech Data Science Syllabus
The key subjects of data science syllabus covered in B. Tech Data Science course are listed below:
| B. Tech Data Science Syllabus | |
| Sr. No. | Core Subjects |
| 1 | Introduction to Artificial Intelligence and Machine Learning
Artificial Intelligence |
| 2 | CAD Design |
| 3 | Data Mining |
| 4 | Computer Networks |
| 5 | Principles of Electrical and Electronics Engineering |
| 6 | Engineering Physics |
| 7 | Engineering Chemistry |
| 8 | Application Based Programming in Python |
| 9 | Data Structures Using C |
| 10 | Applied Statistical Analysis |
| 11 | Software Engineering and Testing Methodologies |
We hope you have understood the key components and topics in the data science syllabus. Before you apply for a data science certification, you must go through the syllabus. It will help you determine whether you are eligible and ready to take up the course.
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Data Science Syllabus (FAQs)
What is the eligibility criteria for pursuing a data science course?
The students who have a background in data science or computer science can pursue the degree course. It is also essential to have a basic understanding of computers, mathematics, and statistics to apply for a data science course.
Is data science in demand?
Data science jobs have a great demand in the market. As technology creeps in different business verticals, the demand for data scientists and analysts will continue to grow. So, acquiring data science skills can be lucrative for your career.
What is the syllabus for data science?
The data science syllabus includes subjects like Linear Algebra, Programming in Python, Machine Learning, Cloud Computing, Artificial Learning, Big Data Analytics, DBMS (Database Management Systems), etc.
Is it possible to study data science online?
Yes, you can study data science online by enrolling in a data science course. Diploma, degree, and postgraduate-level courses are offered by several educational platforms. You should choose the right platform to study data science online.
Which are the main subjects of BSc. Data Science?
Cloud Computing, AI, Machine Learning, and Applied Statistics are some of the main subjects of BSc. Data Science.
Which is better BSc. Data Science or B. Tech Data Science?
Both the courses are good. However, B. Tech Data Science, which is a 4-year course, is more professionally acclaimed as compared to the BSc. Data Science.
Whether to opt for BSc. Computer Science or BSc. Data Science?
If you want to pursue a career in data science, you must opt for the BSc. Data Science course. Though data science is a part of computer science, the BSc. Computer Science focuses more on programming languages. So, if you want to work as a software developer, you can opt for BSc. Computer Science.
Which are the various roles available in industries after completing the data science certification?
Various job roles like Data Engineer, Data Analyst, Database Manager, Business Analyst, Data Scientist, Data Visualizer, etc. are available after completing the data science certifications.
