Data Science Course Syllabus Beginners – Course Curriculum

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Data Science Course Syllabus: Data Science is the practice of using data to understand and analyze problems. Data Science is a rapidly growing field and there are many opportunities in the industry. Data Science is a multidisciplinary field of study and includes concepts from computer science, statistics, and machine learning. This course will teach you how to apply these concepts to solve problems and understand patterns in large data sets. By the end of the course, you will be able to develop mathematical models to predict outcomes, recognize patterns and trends, and make data-driven decisions.

Within this blog, we will outline the components of the data science syllabus and some of the important subjects that are covered within the course. The following sections would discuss the various courses, subjects, and books that you might pursue to become a Data Scientist.

What is a Data Science Course?

The field of data science has been on the rise for many years now and its growing popularity has led to an increase in the number of institutes offering data science courses in India and abroad. This course is a combination of several different subjects such as computer science, mathematics, and statistics. These subjects are used to analyze and make sense of large volumes of data in order to achieve higher efficiency and improve business processes. 

A thorough knowledge of all the related subjects is essential for anyone interested in pursuing a career in this field. The Data Science Course includes an amalgamation of: 

  • Computer science  
  • Data analysis
  • Statistics  
  • Visualization skills
  • Computer programming skills and 
  • Database management techniques. 

The objective of this course is to provide students with the necessary skills to analyze big data and look for solutions to critical business problems using advanced analytical methods.

Data Science Syllabus- Overview 

The Data Science course syllabus covers a wide range of topics that are mainly focused on three main components. Based on the Data Science Courses provided by Pickl.AI, includes a Data Science course syllabus for beginners, a mid-level data science course, and a Data Science course syllabus for professionals. The overview of the data science course syllabus can be stated as follows: 

Data Science Courses by Pickl.AI Syllabus  Data Science Course Fees
Radical– (Beginners Data Science for Teenagers) 
  • Introduction to Data Mindset 
  • Introduction to Programming 
  • Introduction to Machine Learning 
  • Supervised learning 
  • Project Mentoring
Apprentice– (Advanced Data Science)
  • Introduction to Data Mindset 
  • Introduction and basics of Python 
  • Data Structures and Strings 
  • Lists, functions, and OOPs
  • Working with Files and Exception Handling 
  • Numpy 
  • Panda 
  • Visualization and VS Code 
  • Statistics for DS 
  • Introduction to ML 
  • Linear Regression Classification and Ensemble methods 
  • Unsupervised Learning
Wizard– (Advanced Data Science with Capstone Project)
  • Introduction to Data Mindset 
  • Introduction and basics of Python 
  • Data Structures and Strings 
  • Lists, functions, and OOPs
  • Working with Files and Exception Handling 
  • Numpy 
  • Panda 
  • Visualization and VS Code 
  • Statistics for DS 
  • Introduction to ML 
  • Linear Regression Classification and Ensemble methods 
  • Unsupervised Learning
  • Project
Dabbler– (Foundation course in Data Science)
  • Data Mindset 
  • Python 
  • Introduction to Statistics 
  • Supervised Learning 1
  • Supervised Learning 2 
  • Unsupervised Learning 
  • Business Intelligence- Tableau 


Components of Data Science Syllabus

The components of the Data Science syllabus mainly include the following topics –

Big Data

Big Data is a collection of data that has become so large and complex that traditional data processing applications are inadequate for processing the data in a manageable fashion. Examples of big data include website logs, social media data, streaming data from sensors, mobile application data, and so on. In this course, you will learn about different tools and techniques that can be used to process and analyze these datasets and uncover valuable insights from them. You will also learn about different challenges related to processing and analyzing these huge datasets and learn ways to overcome them.

Machine Learning

Machine learning is a field of study that focuses on the development of computer algorithms that allow computer systems to learn for themselves without explicitly being programmed. Some of the areas covered in this course include: 

  • supervised learning  
  • unsupervised learning, 
  • Clustering,
  • Classification  
  • recommendation systems, and so on. 

You will learn about how these techniques can be used to tackle real-world problems in industries such as finance, healthcare, e-commerce, and other fields. The course will also cover related topics such as deep learning and reinforcement learning.

Artificial Intelligence

Artificial intelligence (AI) refers to the ability of machines to perform tasks that normally require human intelligence, such as speech recognition, image recognition, decision-making, and language translation. In this course, you will learn about the major concepts underlying AI as well as the various techniques that can be used to build intelligent systems and solve problems in a variety of applications. The course will also cover topics like natural language processing, machine learning, and computer vision.

Modelling Process in Data Science

The process of modeling involves specifying the intended characteristics of a data set, devising a plausible model to explain the characteristics of the data, and evaluating the performance of the model using various metrics such as accuracy, precision, recall, and so on. In this course, you will learn various modeling techniques such as linear regression, logistic regression, decision trees, clustering, etc. You will also learn various techniques for comparing the performance of different models and evaluating their performance using metrics such as AUC, F1 score, precision-recall curve, RMSE, and others.

Data Science Subjects

Following are the various Data Science subjects that you would find in the different Data Science courses that you might undertake. These subjects are important for every student who wants to become a Data Scientist including all the theoretical and practical evaluations:

Data Science Syllabus IIT

IITs offer Data Science courses at the undergraduate level and also have several certificate programs for those who are interested in pursuing a career in this field but do not have the time to pursue a full-time degree. It also has Master’s degree-level courses offered by various departments, including the Department of Computer Science & Engineering and the Centre for Artificial Intelligence & Robotics. Students who have completed their Bachelor’s degree in Science or Engineering can also enroll in the Advanced Professional Program in Data Science offered by the Department of Computer Science & Engineering. The Data Science Syllabus IIT includes the following:

BTech Data Science Syllabus IIT

  • Data handling and Visualization
  • Data structures and Algorithms
  • Principles of Electrical and Electronics Engineering
  • CAD Design
  • Engineering Physics
  • Engineering Chemistry
  • Python and applications of programming
  • Data Structures Using C
  • Applied Statistical Analysis
  • Computer Networks
  • Software Engineering and Testing Methodologies
  • Data Mining
  • Introduction to Statistical Learning for Data Science

MTech Data Science Syllabus IIT

  • Statistical Foundations for Data Science
  • Data Structures & Algorithms
  • Python Programming 
  • Machine Learning 
  • Stochastic Models
  • Machine Learning
  • Scientific Computing
  • Optimization Techniques
  • Matrix Computations

BSc Data Science Syllabus

The BSc Data Science syllabus includes all the core concepts and principles of data science that you would study during your three-year bachelor’s degree program. Different areas of specialization are available that you might pursue as part of your course. The BSC Data Science Syllabus includes the following:

  • Introduction to Artificial Intelligence
  • Cloud Computing
  • Machine Learning
  • Operating Systems
  • Probability and Inferential Statistics
  • Discrete Mathematics
  • Data Warehousing and Multidimensional Modelling
  • Object-Oriented Programming in Java Machine Learning
  • Operations Research and Optimization Techniques
  • Data Structures and Program Design in C
  • Basic Statistics

Is Coding important in Data Science?

Coding skills are required in almost every career in the tech industry, which is one reason why every software engineer should learn to code. This is true in the data science field as well. To make the most of data and make accurate predictions, data scientists need to understand the theory behind programming. They also need to be able to translate that theory into real computer code. For these reasons, learning to code is an important part of data science education. 

Many schools offer courses in computer programming, which is a great way to learn the fundamentals. There are also a number of coding boot camps that offer intensive courses for people looking to learn on the job. Once you have some basic coding skills under your belt, you can move on to more complex programming languages like Python and R to build models that analyze data and make predictions. Although you don’t need to know how to code to be a successful data scientist, learning to code will give you an advantage over other applicants and help you land the job of your dreams.

Skills Required to Become a Successful Data Scientist 

With the right skills you will be able to become a proficient Data Scientist and utilizing the skills to gain experience will embark you on a successful journey as a Data Scientist. There are two types of skills required to become a successful Data Scientist. It includes Technical Skills and Non-Technical Skills. The skills can be explained as follows: 

Technical Skills 

  • Statistical Analysis 
  • Machine Learning 
  • Deep Learning 
  • Programming 
  • Mathematics 
  • Data Visualisation 
  • Statistics 
  • Big Data
  • Processing Datasets 
  • Data Wrangling 


  • Strong Business Knowledge 
  • Communication Skills 
  • Teamworking Skills 
  • Problem-Solving Skills 
  • Data Intuition 


1. What are the eligibility criteria to pursue Data Science?

For admission in BSc: you must have at least 50% marks in BSc or equivalent from any recognized board, 60% of aggregate in the graduation from a university or institute recognized by UGC/AICTE.For admission to the program, you should have an M.SC degree in Physics/Applied Physics with at least 60% marks from a recognized University(or) M Tech degree in Electronics with not less than 55% marks from any recognized University.

2. What is the duration of Data Science courses?

  • Bachelor’s Degree – 3 years
  • Masters’ Degree – 2 Years
  • Ph.D. degree – 4-5 years

3. Is Maths required for Data Science?

Yes, Math is required for Data Science. In order to work with data effectively, you must be able to analyze it mathematically and use it to make reliable predictions.

4. Does Data Science require coding?

Yes, every course in Data Science requires coding knowledge. Knowledge of different programming languages such as Java, Python, and SQL is required for students to become successful data scientists.

5. What is the salary of a Data Scientist in India?

In India, an average Data Scientist earns around ₹10 Lakhs annually which ranges between ₹3.6 Lakhs to ₹25.6 Lakhs. The basis of the range of salary depends on the experience level, skillset and the proficiency of the candidate in using different tools.

6. Who can do a Data Science course? 

Whether you’re a Teenager, Graduate, or Working Professional, you can pursue Data Science despite any academic or professional background. You need to have a keen interest in the field and opt for your choice of course. Pickl.AI offers you a myriad of courses for different age groups of candidates from teenagers to working professionals. 

Wrapping it Up!!!

The above blog can be concluded by saying that it is a broad-based profession that is very broad-based and is not constrained by a particular set of technologies or even disciplines. While a computer science background is definitely an advantage, most data professionals start out with no particular training in the field. While many data sources are unstructured, the most fruitful areas of research tend to be driven by computational algorithms applied to structured datasets. Data science does require a deep mathematical and statistical understanding of how to solve problems efficiently, but the varied nature of the problem domains means there is no single skill set that one must have to succeed as a data scientist.



  • Asmita Kar

    Written by:

    I am a Senior Content Writer working with Pickl.AI. I am a passionate writer, an ardent learner and a dedicated individual. With around 3years of experience in writing, I have developed the knack of using words with a creative flow. Writing motivates me to conduct research and inspires me to intertwine words that are able to lure my audience in reading my work. My biggest motivation in life is my mother who constantly pushes me to do better in life. Apart from writing, Indian Mythology is my area of passion about which I am constantly on the path of learning more.