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 datadriven 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 midlevel 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 
Radical Data Science for Teens 

Apprentice MidLevel Data Science Course 

Wizard Data Science for Professionals 

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 realworld problems in industries such as finance, healthcare, ecommerce, 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, decisionmaking, 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, precisionrecall 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 Mindset
 Programming
 Machine Learning
 Python
 Data Structures
 Data Mining
 Data Visualisation
 Statistics
 Linear Regression
 Clustering
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 fulltime degree. It also has Master’s degreelevel 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 threeyear 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
 ObjectOriented 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.
Best Data Science Books
Top 6 books on machine learning and artificial intelligence for data scientists for beginners. Learn all you need to know to become a data scientist in the amazon artificial intelligence and machine learning market. The following table presents the list of the best Data Science books:
Name of the Book  Author 
Data Science from Scratch  Joel Grus 
R for Data Science  Hadley Wickham & Garrett Grolemund 
Think Stats  Allen B Downey 
Introduction to Machine Learning with Python  Andreas C Muller & Sarah Guido 
Data Science Job: How to Become a Data Scientist  Przemek Chojecki 
Handson Machine Learning with ScikitLearn and TensorFlow  Aurelien Geron 
FAQs
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 m.tech 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 – 45 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.
Wrapping it Up!!!
The above blog can be concluded by saying that it is a broadbased profession that is very broadbased 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.